From 713b9fcbbebe734ab7d436c41a014fae90e0e56d Mon Sep 17 00:00:00 2001 From: Dominik Klein Date: Wed, 3 Apr 2024 15:02:15 +0200 Subject: [PATCH] restructuring neural models + addition of OT-FM and GENOT (#468) * draft of BaseSolver and UnbalancedMixin * draft of BaseSolver and UnbalancedMixin * [ci skip] continue flow matching implementation * [ci skip] continue flow matching implementation * [ci skip] add neural networks * [ci skip] add test * [ci skip] resolve import errors * [ci skip] MRO not working * [ci skip] basic test for flow matching passes * [ci skip] add tests for FM with conditions and conditional OT with FM * [ci skip] add genot outline * [ci skip] restructure genot * [ci skip] restructure genot * [ci skip] fix transport * [ci skip] flow matching tests passing * [ci skip] add more tests genot * [ci skip] add more tests genot * [ci skip] add TimeSampler * [ci skip] add docs for TimeSampler and Flow * [ci skip] add docs for OTFlowMatching and replace jnp.ndarray by jax.Array * [ci skip] change init arguments of GENOT and add docstrings to GENOT * [ci skip] split nets into base_models and models * [ci skip] add references * add tests for learning the rescaling factors * [ci skip] partially fix rescaling factor learning * [ci skip] fix rescaling factor learning * [ci skip] all tests passing but k_samples_per_x in genot * k_samples_per_x working in GENOT * [ci skip] changed dataloaders to numpy and dict return * [ci skip] changed dataloaders to numpy and dict return * revert jax.Array to jnp.ndarray * move dataloader from tests to module * add docstrings to neurcal networks * [ci skip] adapt type of scale_cost and cost_fn * [ci skip] clean code * [ci skip] fix genot tests * [ci skip] fix otfm tests * [ci skip] fix otfm tests * add scale cost to otfm * incorporate feedback partially * resolve circular import errors * resolve a few pre-commit errors * resolve pre-commit errors * resolve pre-commit errors * fix rng bug * Update pre-commit * fix import error * Run linter * replace rng jnp.ndarray type by jax.array * replace rng jnp.ndarray type by jax.array * fix import error * [ci skip] start to incorporate feedback * restructure neural module * fix import errors * incorporate feedback partially * make time encoder a layer * make conditions Optional and minor feedback * revert faulty jax.array / jnp.ndarray conversions * make formatting in neural nets nicer * add description to Velocity Field * replace time sampler class by function * add citations * add more references * rename keys_model to rng * fix tests regarding time sampling * fix typo in tests * rename neural_vector_field to velocity_field everywhere * fix OTFlowMatching.transport * fix rescaling networks * Update src/ott/neural/flows/flows.py Co-authored-by: nvesseron <96598529+nvesseron@users.noreply.github.com> * Update src/ott/neural/flows/flows.py Co-authored-by: nvesseron <96598529+nvesseron@users.noreply.github.com> * test for scale_cost * update test for scale_cost * fix bug for scale_cost * fix bug for scale_cost * jit solve_ode in genot * incorporate changes partially * [ci skip] intermediate save * [ci skip] neural base solver update * make resamlpemixin a class * incorporate more changes * move noise sampling to flows * fix bug in passing rngs in otfm * introduce otmatcher in otfm * [ci skip] split GENOT into GENOTLin and GENOTQuad * remove dictionaries in OTFM and GENOT classes * change logic in match_latent_to_data in genot * change data loaders / data sets * finish data loader refactoring * Update linter * fix bug in _resample_data` * incorporate more changes * add docs * incorporate more changes * problem with custom type * fix scale cost bug * fix bugs * fux bug in unbalancedness/rescalingMlp * unify unbalancedness step in GENOT * change OTDataSet and OTFlowMatching to 4 data loaderes * Fix bug in the `ConditionalOTDataset` * Polish docs in the `flows.py` * Update `OTFM` * Fix small bugs in `OTFM` * Polish layers * Fix typo in citation * More polish for the docs * remove print statements and unbalancednesshandler * remove tests * make genot training loops more similar to otfm training loop * adapt tests to the extent possible * Add weights to sampling * Start cleaning matchers * Add conditional sampling + resampling * Add initial quad matcher * Improve typing * Remove `base_solver.py` * Add TODO * Update datasets, fix OTFM tests * Start cleaning GENOT * Update GENOT * Remove old GENOTLin/GENOTQuad * Remove axis swapping * Remove old todo * Fix OTFM tests * Remove `MLPBlock` and `RescalingMLP` * Add forgotten license * Remove `__post_init__` from `VF` * Move cyclical time encoder * Move more stuff to `utils` * Remove `samplers.py` * Rename `cond_dim` -> `condition_dim` * Nicer formatting * Fix bug when sampling from the target * Fix another bug when sampling from the data * Add initial test for GW * Remove old GENOT tests * Remove old dataloaders * Add more todos * add docs to dataloader * expose args in GENOT * add docs and adapt data_match_fn * fix linting * fix data loading and add genot fused tests * genot tests passing * adapt docs * adapt docs * add error message * clean docs * comprise genot tests * change reference for GENOT * add missing docstring * Modify behaviour of `ConditionalLoader` * Update docstring * Clean GENOT docs * Improve VF * Simplify GENOT test * Better metadata wrapper in tests * Fix condition in GENOT test * Add quad cond dl * Add conf fused DL * Polish docs * Remove conditional loader * Fix link in the docs * Improve VF * Fix GENOT test * Polish docs * Remove `uniform_marginals` argument * Fix undefined variable * Update `GENOT.transport` docs * Add `diffrax` to `conf.py` * Restructure files * Fix neural init tests import * Update `docs/` * Update Monge Gap * Update MetaOT and NeuralDual * Update ICNN inits * Fix links to neural in the docs * Check for condition dim in VF * Don't use activation fn in the last layer of VF * Update assertions * Try skipping OTFM/GENOT tests temporarily * Be extra verbose when intalling packages * Remove `torch` dependency * Remove `torch` from tests in `pyproject.toml` * [ci skip] Update docstrings --------- Co-authored-by: lucaeyring Co-authored-by: Michal Klein <46717574+michalk8@users.noreply.github.com> Co-authored-by: nvesseron <96598529+nvesseron@users.noreply.github.com> Co-authored-by: Dominik Klein --- .pre-commit-config.yaml | 46 +-- docs/conf.py | 9 +- docs/neural/datasets.rst | 15 + docs/neural/index.rst | 30 +- docs/neural/methods.rst | 37 ++ docs/neural/networks.rst | 33 ++ docs/neural/solvers.rst | 28 -- docs/references.bib | 50 +++ docs/solvers/index.rst | 11 + docs/spelling/misc.txt | 1 + docs/spelling/technical.txt | 3 + docs/tutorials/MetaOT.ipynb | 31 +- docs/tutorials/Monge_Gap.ipynb | 21 +- docs/tutorials/icnn_inits.ipynb | 45 +-- docs/tutorials/neural_dual.ipynb | 57 ++-- docs/tutorials/point_clouds.ipynb | 6 +- docs/tutorials/soft_sort.ipynb | 7 +- .../sparse_monge_displacements.ipynb | 2 + pyproject.toml | 172 +++++----- src/ott/__init__.py | 1 - src/ott/datasets.py | 24 +- src/ott/initializers/__init__.py | 7 + .../neural}/__init__.py | 2 +- .../neural/meta_initializer.py} | 195 +---------- src/ott/math/__init__.py | 7 +- src/ott/neural/__init__.py | 2 +- src/ott/neural/datasets.py | 120 +++++++ src/ott/neural/losses.py | 148 -------- src/ott/neural/methods/__init__.py | 14 + src/ott/neural/methods/flows/__init__.py | 14 + src/ott/neural/methods/flows/dynamics.py | 164 +++++++++ src/ott/neural/methods/flows/genot.py | 317 ++++++++++++++++++ src/ott/neural/methods/flows/otfm.py | 199 +++++++++++ .../map_estimator.py => methods/monge_gap.py} | 146 +++++++- .../neural/{solvers => methods}/neuraldual.py | 169 ++-------- src/ott/neural/networks/__init__.py | 14 + src/ott/neural/networks/icnn.py | 160 +++++++++ src/ott/neural/networks/layers/__init__.py | 14 + .../{solvers => networks/layers}/conjugate.py | 0 .../{layers.py => networks/layers/posdef.py} | 7 +- .../neural/networks/layers/time_encoder.py | 34 ++ src/ott/neural/networks/potentials.py | 185 ++++++++++ src/ott/neural/networks/velocity_field.py | 124 +++++++ src/ott/problems/linear/potentials.py | 10 +- src/ott/solvers/__init__.py | 2 +- src/ott/solvers/linear/lineax_implicit.py | 5 +- src/ott/solvers/utils.py | 182 ++++++++++ src/ott/tools/soft_sort.py | 2 +- tests/__init__.py | 0 tests/conftest.py | 5 +- tests/geometry/costs_test.py | 4 +- tests/geometry/geodesic_test.py | 11 +- tests/geometry/graph_test.py | 17 +- tests/geometry/lr_cost_test.py | 4 +- tests/geometry/lr_kernel_test.py | 4 +- tests/geometry/pointcloud_test.py | 4 +- tests/geometry/scaling_cost_test.py | 4 +- tests/geometry/subsetting_test.py | 4 +- .../initializers/linear/sinkhorn_init_test.py | 4 +- .../linear/sinkhorn_lr_init_test.py | 4 +- tests/initializers/neural/__init__.py | 16 + .../neural/meta_initializer_test.py | 9 +- tests/initializers/quadratic/gw_init_test.py | 4 +- tests/math/lse_test.py | 4 +- tests/math/math_utils_test.py | 4 +- tests/math/matrix_square_root_test.py | 4 +- tests/neural/__init__.py | 15 +- tests/neural/conftest.py | 197 +++++++++++ tests/neural/map_estimator_test.py | 86 ----- tests/neural/methods/genot_test.py | 92 +++++ .../monge_gap_test.py} | 91 ++++- tests/neural/{ => methods}/neuraldual_test.py | 26 +- tests/neural/methods/otfm_test.py | 63 ++++ tests/neural/{ => networks}/icnn_test.py | 10 +- tests/problems/linear/potentials_test.py | 7 +- .../linear/continuous_barycenter_test.py | 4 +- .../linear/discrete_barycenter_test.py | 4 +- tests/solvers/linear/sinkhorn_diff_test.py | 4 +- tests/solvers/linear/sinkhorn_grid_test.py | 4 +- tests/solvers/linear/sinkhorn_lr_test.py | 4 +- tests/solvers/linear/sinkhorn_misc_test.py | 8 +- tests/solvers/linear/sinkhorn_test.py | 4 +- tests/solvers/linear/univariate_test.py | 4 +- tests/solvers/quadratic/fgw_test.py | 4 +- tests/solvers/quadratic/gw_barycenter_test.py | 4 +- tests/solvers/quadratic/gw_test.py | 6 +- tests/solvers/quadratic/lower_bound_test.py | 4 +- .../gaussian_mixture/fit_gmm_pair_test.py | 4 +- tests/tools/gaussian_mixture/fit_gmm_test.py | 4 +- .../gaussian_mixture_pair_test.py | 4 +- .../gaussian_mixture/gaussian_mixture_test.py | 4 +- tests/tools/gaussian_mixture/gaussian_test.py | 4 +- tests/tools/gaussian_mixture/linalg_test.py | 4 +- .../gaussian_mixture/probabilities_test.py | 4 +- .../tools/gaussian_mixture/scale_tril_test.py | 4 +- tests/tools/k_means_test.py | 8 +- tests/tools/plot_test.py | 7 +- tests/tools/segment_sinkhorn_test.py | 4 +- tests/tools/sinkhorn_divergence_test.py | 4 +- tests/tools/soft_sort_test.py | 4 +- tests/utils_test.py | 1 + 101 files changed, 2730 insertions(+), 949 deletions(-) create mode 100644 docs/neural/datasets.rst create mode 100644 docs/neural/methods.rst create mode 100644 docs/neural/networks.rst delete mode 100644 docs/neural/solvers.rst rename src/ott/{neural/solvers => initializers/neural}/__init__.py (91%) rename src/ott/{neural/models.py => initializers/neural/meta_initializer.py} (51%) create mode 100644 src/ott/neural/datasets.py delete mode 100644 src/ott/neural/losses.py create mode 100644 src/ott/neural/methods/__init__.py create mode 100644 src/ott/neural/methods/flows/__init__.py create mode 100644 src/ott/neural/methods/flows/dynamics.py create mode 100644 src/ott/neural/methods/flows/genot.py create mode 100644 src/ott/neural/methods/flows/otfm.py rename src/ott/neural/{solvers/map_estimator.py => methods/monge_gap.py} (62%) rename src/ott/neural/{solvers => methods}/neuraldual.py (78%) create mode 100644 src/ott/neural/networks/__init__.py create mode 100644 src/ott/neural/networks/icnn.py create mode 100644 src/ott/neural/networks/layers/__init__.py rename src/ott/neural/{solvers => networks/layers}/conjugate.py (100%) rename src/ott/neural/{layers.py => networks/layers/posdef.py} (98%) create mode 100644 src/ott/neural/networks/layers/time_encoder.py create mode 100644 src/ott/neural/networks/potentials.py create mode 100644 src/ott/neural/networks/velocity_field.py create mode 100644 src/ott/solvers/utils.py create mode 100644 tests/__init__.py create mode 100644 tests/initializers/neural/__init__.py rename tests/{ => initializers}/neural/meta_initializer_test.py (96%) create mode 100644 tests/neural/conftest.py delete mode 100644 tests/neural/map_estimator_test.py create mode 100644 tests/neural/methods/genot_test.py rename tests/neural/{losses_test.py => methods/monge_gap_test.py} (58%) rename tests/neural/{ => methods}/neuraldual_test.py (86%) create mode 100644 tests/neural/methods/otfm_test.py rename tests/neural/{ => networks}/icnn_test.py (93%) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 396cca399..ec54873a3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,25 +6,8 @@ default_stages: - push minimum_pre_commit_version: 3.0.0 repos: -- repo: https://github.com/google/yapf - rev: v0.40.0 - hooks: - - id: yapf - additional_dependencies: [toml] -- repo: https://github.com/nbQA-dev/nbQA - rev: 1.7.0 - hooks: - - id: nbqa-pyupgrade - args: [--py38-plus] - - id: nbqa-black - - id: nbqa-isort -- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - rev: v2.10.0 - hooks: - - id: pretty-format-yaml - args: [--autofix, --indent, '2'] - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: detect-private-key - id: check-ast @@ -37,13 +20,34 @@ repos: - id: trailing-whitespace - id: check-case-conflict - repo: https://github.com/charliermarsh/ruff-pre-commit - # Ruff version. - rev: v0.0.285 + rev: v0.2.1 hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix] +- repo: https://github.com/pycqa/isort + rev: 5.13.2 + hooks: + - id: isort + name: isort +- repo: https://github.com/google/yapf + rev: v0.40.2 + hooks: + - id: yapf + additional_dependencies: [toml] +- repo: https://github.com/nbQA-dev/nbQA + rev: 1.7.1 + hooks: + - id: nbqa-pyupgrade + args: [--py38-plus] + - id: nbqa-black + - id: nbqa-isort +- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks + rev: v2.12.0 + hooks: + - id: pretty-format-yaml + args: [--autofix, --indent, '2'] - repo: https://github.com/rstcheck/rstcheck - rev: v6.1.2 + rev: v6.2.0 hooks: - id: rstcheck additional_dependencies: [tomli] diff --git a/docs/conf.py b/docs/conf.py index 69ef540ee..571fc0cfd 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -26,9 +26,10 @@ import logging from datetime import datetime -import ott from sphinx.util import logging as sphinx_logging +import ott + # -- Project information ----------------------------------------------------- needs_sphinx = "4.0" @@ -62,13 +63,13 @@ "python": ("https://docs.python.org/3", None), "numpy": ("https://numpy.org/doc/stable/", None), "jax": ("https://jax.readthedocs.io/en/latest/", None), + "jaxopt": ("https://jaxopt.github.io/stable", None), "lineax": ("https://docs.kidger.site/lineax/", None), "flax": ("https://flax.readthedocs.io/en/latest/", None), - "scikit-sparse": ("https://scikit-sparse.readthedocs.io/en/latest/", None), + "optax": ("https://optax.readthedocs.io/en/latest/", None), + "diffrax": ("https://docs.kidger.site/diffrax/", None), "scipy": ("https://docs.scipy.org/doc/scipy/", None), "pot": ("https://pythonot.github.io/", None), - "jaxopt": ("https://jaxopt.github.io/stable", None), - "optax": ("https://optax.readthedocs.io/en/latest/", None), "matplotlib": ("https://matplotlib.org/stable/", None), } diff --git a/docs/neural/datasets.rst b/docs/neural/datasets.rst new file mode 100644 index 000000000..67d5e3b6b --- /dev/null +++ b/docs/neural/datasets.rst @@ -0,0 +1,15 @@ +ott.neural.datasets +=================== +.. module:: ott.neural.datasets +.. currentmodule:: ott.neural + +The :mod:`ott.neural.datasets` contains datasets and needed for solving +(conditional) neural optimal transport problems. + +Datasets +-------- +.. autosummary:: + :toctree: _autosummary + + datasets.OTData + datasets.OTDataset diff --git a/docs/neural/index.rst b/docs/neural/index.rst index d0315edae..5cf025cdc 100644 --- a/docs/neural/index.rst +++ b/docs/neural/index.rst @@ -1,7 +1,6 @@ ott.neural ========== .. module:: ott.neural -.. currentmodule:: ott.neural In contrast to most methods presented in :mod:`ott.solvers`, which output vectors or matrices, the goal of the :mod:`ott.neural` module is to parameterize @@ -13,29 +12,6 @@ and solvers to estimate such neural networks. .. toctree:: :maxdepth: 2 - solvers - -Models ------- -.. autosummary:: - :toctree: _autosummary - - models.ICNN - models.MLP - models.MetaInitializer - -Losses ------- -.. autosummary:: - :toctree: _autosummary - - losses.monge_gap - losses.monge_gap_from_samples - -Layers ------- -.. autosummary:: - :toctree: _autosummary - - layers.PositiveDense - layers.PosDefPotentials + datasets + methods + networks diff --git a/docs/neural/methods.rst b/docs/neural/methods.rst new file mode 100644 index 000000000..028651a34 --- /dev/null +++ b/docs/neural/methods.rst @@ -0,0 +1,37 @@ +ott.neural.methods +================== +.. module:: ott.neural.methods +.. currentmodule:: ott.neural.methods + +Monge Gap +--------- +.. autosummary:: + :toctree: _autosummary + + monge_gap.monge_gap + monge_gap.monge_gap_from_samples + monge_gap.MongeGapEstimator + +Neural Dual +----------- +.. autosummary:: + :toctree: _autosummary + + neuraldual.W2NeuralDual + +ott.neural.methods.flows +======================== +.. module:: ott.neural.methods.flows +.. currentmodule:: ott.neural.methods.flows + +Flows +----- +.. autosummary:: + :toctree: _autosummary + + otfm.OTFlowMatching + genot.GENOT + dynamics.BaseFlow + dynamics.StraightFlow + dynamics.ConstantNoiseFlow + dynamics.BrownianBridge diff --git a/docs/neural/networks.rst b/docs/neural/networks.rst new file mode 100644 index 000000000..647243192 --- /dev/null +++ b/docs/neural/networks.rst @@ -0,0 +1,33 @@ +ott.neural.networks +=================== +.. module:: ott.neural.networks +.. currentmodule:: ott.neural.networks + +Networks +-------- +.. autosummary:: + :toctree: _autosummary + + icnn.ICNN + velocity_field.VelocityField + potentials.BasePotential + potentials.PotentialMLP + potentials.PotentialTrainState + + +ott.neural.networks.layers +========================== +.. module:: ott.neural.networks.layers +.. currentmodule:: ott.neural.networks.layers + +Layers +------ +.. autosummary:: + :toctree: _autosummary + + conjugate.FenchelConjugateSolver + conjugate.FenchelConjugateLBFGS + conjugate.ConjugateResults + posdef.PositiveDense + posdef.PosDefPotentials + time_encoder.cyclical_time_encoder diff --git a/docs/neural/solvers.rst b/docs/neural/solvers.rst deleted file mode 100644 index c405d89ba..000000000 --- a/docs/neural/solvers.rst +++ /dev/null @@ -1,28 +0,0 @@ -ott.neural.solvers -================== -.. module:: ott.neural.solvers -.. currentmodule:: ott.neural.solvers - -This module implements various solvers to estimate optimal transport between -two probability measures, through samples, parameterized as neural networks. -These neural networks are described in :mod:`ott.neural.models`, borrowing -lower-level components from :mod:`ott.neural.layers` using -`flax `__. - -Solvers -------- -.. autosummary:: - :toctree: _autosummary - - map_estimator.MapEstimator - neuraldual.W2NeuralDual - neuraldual.BaseW2NeuralDual - -Conjugate Solvers ------------------ -.. autosummary:: - :toctree: _autosummary - - conjugate.FenchelConjugateLBFGS - conjugate.FenchelConjugateSolver - conjugate.ConjugateResults diff --git a/docs/references.bib b/docs/references.bib index 0c9899bdb..d07643e8c 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -826,6 +826,56 @@ @misc{huguet:2023 year = {2023}, } +@misc{eyring:23, + author = {Eyring, Luca and Klein, Dominik and Uscidda, Théo and Palla, Giovanni and Kilbertus, Niki and Akata, Zeynep and Theis, Fabian}, + doi = {10.48550/arXiv.2311.15100}, + eprint = {2311.15100}, + eprintclass = {stat.ML}, + eprinttype = {arXiv}, + title = {Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation}, + year = {2023}, +} + +@misc{klein_uscidda:23, + author = {Klein, Dominik and Uscidda, Théo and Theis, Fabian and Cuturi, Marco}, + doi = {10.48550/arXiv.2310.09254}, + eprint = {2310.09254}, + eprintclass = {stat.ML}, + eprinttype = {arXiv}, + title = {Entropic (Gromov) Wasserstein Flow Matching with GENOT}, + year = {2023}, +} + +@misc{lipman:22, + author = {Lipman, Yaron and Chen, Ricky TQ and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt}, + doi = {10.48550/arXiv.2210.02747}, + eprint = {2210.02747}, + eprintclass = {stat.ML}, + eprinttype = {arXiv}, + title = {Flow matching for generative modeling}, + year = {2022}, +} + +@misc{tong:23, + author = {Tong, Alexander and Malkin, Nikolay and Huguet, Guillaume and Zhang, Yanlei and {Rector-Brooks}, Jarrid and Fatras, Kilian and Wolf, Guy and Bengio, Yoshua}, + doi = {10.48550/arXiv.2302.00482}, + eprint = {2302.00482}, + eprintclass = {stat.ML}, + eprinttype = {arXiv}, + title = {Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport}, + year = {2023}, +} + +@misc{pooladian:23, + author = {Pooladian, Aram-Alexandre and Ben-Hamu, Heli and Domingo-Enrich, Carles and Amos, Brandon and Lipman, Yaron and Chen, Ricky}, + doi = {10.48550/arXiv.2304.14772}, + eprint = {2304.14772}, + eprintclass = {stat.ML}, + eprinttype = {arXiv}, + title = {Multisample flow matching: Straightening flows with minibatch couplings}, + year = {2023}, +} + @article{iacono:17, author = {Iacono, Roberto and Boyd, John P.}, url = {https://doi.org/10.1007/s10444-017-9530-3}, diff --git a/docs/solvers/index.rst b/docs/solvers/index.rst index ddfbc9230..d23b4cdac 100644 --- a/docs/solvers/index.rst +++ b/docs/solvers/index.rst @@ -23,3 +23,14 @@ Wasserstein Solver :toctree: _autosummary was_solver.WassersteinSolver + +Utilities +--------- +.. autosummary:: + :toctree: _autosummary + + utils.match_linear + utils.match_quadratic + utils.sample_joint + utils.sample_conditional + utils.uniform_sampler diff --git a/docs/spelling/misc.txt b/docs/spelling/misc.txt index 26bc961ce..4be10fe05 100644 --- a/docs/spelling/misc.txt +++ b/docs/spelling/misc.txt @@ -1,4 +1,5 @@ Eulerian +Utils alg arg args diff --git a/docs/spelling/technical.txt b/docs/spelling/technical.txt index 7c7ba4ae9..f7997b48c 100644 --- a/docs/spelling/technical.txt +++ b/docs/spelling/technical.txt @@ -25,6 +25,7 @@ McCann Monge Moreau SGD +Schrödinger Schur Seidel Sinkhorn @@ -46,6 +47,7 @@ chromatin collinear covariance covariances +dataclass dataloaders dataset datasets @@ -110,6 +112,7 @@ preprocess preprocessing proteome prox +pytree quantile quantiles quantizes diff --git a/docs/tutorials/MetaOT.ipynb b/docs/tutorials/MetaOT.ipynb index 1ef687b28..ad7426a0f 100644 --- a/docs/tutorials/MetaOT.ipynb +++ b/docs/tutorials/MetaOT.ipynb @@ -23,8 +23,7 @@ "\n", "We will cover:\n", "\n", - "- {class}`~ott.neural.models.MetaInitializer`: The main class for the Meta OT initializer\n", - "- {class}`~ott.neural.models.MLP`: A Meta MLP to predict the dual potentials from the weights of the measures\n", + "- {class}`~ott.initializers.neural.meta_initializer.MetaInitializer`: The main class for the Meta OT initializer\n", "- {class}`~ott.initializers.linear.initializers.GaussianInitializer`: The main initialization class for the Gaussian initializer" ] }, @@ -46,8 +45,8 @@ "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", - " !pip install -q git+https://github.com/ott-jax/ott@main\n", - " !pip install -q torch torchvision" + " %pip install -q git+https://github.com/ott-jax/ott@main\n", + " %pip install -q torch torchvision" ] }, { @@ -63,6 +62,7 @@ "import jax.numpy as jnp\n", "import numpy as np\n", "import torchvision\n", + "\n", "from flax import linen as nn\n", "\n", "import matplotlib.pyplot as plt\n", @@ -70,7 +70,7 @@ "\n", "from ott.geometry import pointcloud\n", "from ott.initializers.linear import initializers\n", - "from ott.neural import models\n", + "from ott.initializers.neural import meta_initializer\n", "from ott.problems.linear import linear_problem\n", "from ott.solvers.linear import sinkhorn" ] @@ -215,7 +215,7 @@ "This tutorial shows how to train a meta OT model to predict\n", "the optimal Sinkhorn potentials from the image pairs.\n", "We will reproduce their results using \n", - "{class}`~ott.neural.models.MetaInitializer`,\n", + "{class}`~ott.neural.initializers.meta_initializer.MetaInitializer`,\n", "which provides an easy-to-use interface\n", "for training and using Meta OT models.\n", "\n", @@ -238,7 +238,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" ] @@ -383,10 +383,10 @@ "in the meta distribution $\\mathcal{D}$ during training\n", "\n", "The following instantiates\n", - "{class}`~ott.neural.models.MetaInitializer`,\n", + "{class}`~ott.neural.initializers.meta_initializer.MetaInitializer`,\n", "which provides an implementation for training and deploying Meta OT models.\n", "The default meta potential model for $f_\\theta$ is a standard multi-layer MLP\n", - "defined in {class}`~ott.neural.models.MLP`\n", + "defined by the ``MetaMLP`` below\n", "and it is optimized with {func}`~optax.adam` by default.\n", "\n", "**Custom model and optimizers**.\n", @@ -437,7 +437,9 @@ "outputs": [], "source": [ "meta_mlp = MetaMLP(potential_size=geom.shape[0])\n", - "meta_initializer = models.MetaInitializer(geom=geom, meta_model=meta_mlp)" + "meta_initializer = meta_initializer.MetaInitializer(\n", + " geom=geom, meta_model=meta_mlp\n", + ")" ] }, { @@ -450,7 +452,8 @@ "Meta OT models have a preliminary training phase where they are\n", "given samples of OT problems from the meta distribution.\n", "The Meta OTT initializer internally stores the training state\n", - "of the model, and {meth}`~ott.neural.models.MetaInitializer.update` will update the initialization\n", + "of the model, and {meth}`~ott.neural.initializers.meta_initializer.MetaInitializer.update`\n", + "will update the initialization\n", "on a batch of problems to improve the next prediction.\n", "While we show here a separate training phase, the update\n", "can also be done in-tandem with deployment where the\n", @@ -500,7 +503,7 @@ "Now that we have trained the model, we can next deploy it anytime we\n", "want to make a rough prediction for new instances of the problems.\n", "While in practice, the model can be continued to be updated in deployment\n", - "by calling {meth}`~ott.neural.models.MetaInitializer.update`,\n", + "by calling {meth}`~ott.neural.initializers.meta_initializer.MetaInitializer.update`,\n", "here we will keep the model fixed so we can evaluate it on test instances." ] }, @@ -515,7 +518,7 @@ "prediction of the solution to the transport problems from above,\n", "which are sampled from testing pairs of MNIST digits that\n", "the model was not trained on.\n", - "The initializer uses the Meta OT model in {meth}`~ott.neural.models.MetaInitializer.init_dual_a`.\n", + "The initializer uses the Meta OT model in {meth}`~ott.neural.initializers.meta_initializer.MetaInitializer.init_dual_a`.\n", "This shows that the initialization is extremely close to the ground-truth coupling." ] }, diff --git a/docs/tutorials/Monge_Gap.ipynb b/docs/tutorials/Monge_Gap.ipynb index 8d25b550b..be9098a09 100644 --- a/docs/tutorials/Monge_Gap.ipynb +++ b/docs/tutorials/Monge_Gap.ipynb @@ -31,16 +31,17 @@ "\n", "import jax\n", "import jax.numpy as jnp\n", - "import optax\n", "import sklearn.datasets\n", + "\n", + "import optax\n", "from flax import linen as nn\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "from ott import datasets\n", "from ott.geometry import costs, pointcloud\n", - "from ott.neural import losses, models\n", - "from ott.neural.solvers import map_estimator\n", + "from ott.neural.methods import monge_gap\n", + "from ott.neural.networks import potentials\n", "from ott.solvers.linear import acceleration\n", "from ott.tools import sinkhorn_divergence" ] @@ -57,7 +58,7 @@ "T^\\star \\in \\arg\\min_{\\substack{T:\\mathbb{R}^d \\rightarrow \\mathbb{R}^d \\\\ T \\sharp \\mu = \\nu}} \\int c(x,T(x)) \\mathrm{d}\\mu(x)\n", "$$\n", "\n", - "We show how to use the {func}`~ott.neural.losses.monge_gap`, a regularizer proposed by {cite}`uscidda:23` to do so. Computing an OT map can be split into two goals: move mass efficiently from $\\mu$ to $T\\sharp\\mu$ (this is the objective), while, at the same time, making sure $T\\sharp\\mu$ \"lands\" on $\\nu$ (the constraint).\n", + "We show how to use the {func}`~ott.neural.methods.monge_gap.monge_gap`, a regularizer proposed by {cite}`uscidda:23` to do so. Computing an OT map can be split into two goals: move mass efficiently from $\\mu$ to $T\\sharp\\mu$ (this is the objective), while, at the same time, making sure $T\\sharp\\mu$ \"lands\" on $\\nu$ (the constraint).\n", "\n", "The first requirement (efficiency) can be quantified with the **Monge gap** $\\mathcal{M}_\\mu^c$, a non-negative regularizer defined through $\\mu$ and $c$, and which takes as an argument any map $T : \\mathbb{R}^d \\rightarrow \\mathbb{R}^d$. The value $\\mathcal{M}_\\mu^c(T)$ quantifies how $T$ moves mass efficiently between $\\mu$ and $T \\sharp \\mu$, and only cancels $\\mathcal{M}_\\mu^c(T) = 0$ i.f.f. $T$ is optimal between $\\mu$ and $T \\sharp \\mu$ for the cost $c$.\n", "\n", @@ -67,7 +68,7 @@ "\\min_{T:\\mathbb{R}^d \\rightarrow \\mathbb{R}^d} \\Delta(T\\sharp \\mu, \\nu) + \\lambda_\\mathrm{MG} \\mathcal{M}_\\mu^c(T)\n", "$$\n", "\n", - "We parameterize maps $T$ as neural networks $\\{T_\\theta\\}_{\\theta \\in \\mathbb{R}^d}$, using the {class}`~ott.neural.solvers.map_estimator.MapEstimator` solver. For the squared-Euclidean cost, this method provides a simple alternative to the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` solver, but one that does not require parameterizing networks as gradients of convex functions." + "We parameterize maps $T$ as neural networks $\\{T_\\theta\\}_{\\theta \\in \\mathbb{R}^d}$, using the {class}`~ott.neural.methods.monge_gap.MongeGapEstimator` solver. For the squared Euclidean cost, this method provides a simple alternative to the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` solver, but one that does not require parameterizing networks as gradients of convex functions." ] }, { @@ -292,7 +293,7 @@ "outputs": [ { "data": { - "image/png": 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S+VmnpaVxyCGH8PXXX4fP2ZE48MAD4x6bNm1a+O9jjz22Uv3+8ccfrF69GoDbbruN2267rULnbWvF8qKiIl544QVee+01pk2bFreIHKhSeiIkSgdau3bt8N+2tomFzeiVlpbGQQcdFLeP1NRUDjnkkF1STHHChAk89dRTfPHFF6xbty5uu/LmKNFzA/7zfvDBByeMR+rcuTNpaWkxv69p06aFK1VfcMEFXHDBBQmvabGrqt5fcskl3HXXXbz++uvceeedLF68OEyMEhGoIIIZ0oLrR3mIdc9r1qxhyJAhvPvuu8yZMwfP8+KeH/199+7dm5o1a7JhwwbOPPNMTjjhBM444wyOO+44Dj74YJKTkys8tqpAouetKuZsd7tfB4ftgbNYODjsYcjJyeHUU0/lzjvv5IMPPmDlypW8/PLL1KpVC4Dly5dzxx13RJxjhbj69euX23/Dhg0jztmRsGOOhaCwUZkNG2DVqlXbNJ6KpHKNxrp16zjyyCMZMGAA33//fUJSAYTTB8dDZmZm3GNBIbmkpKTMOEDkozxBpEGDBgmP7wjcfffdHHPMMYwYMaLcZ6u8OUr03ADhtJ7lBSsnJydHkLUgduYzVBW4+OKLAZgzZw6TJk3i9ddfx/M8WrZsyTHHHFOhPqrqnn/88Uf2339/HnzwQWbPnp2QVEDZ77tOnTp88MEHNGnSBM/z+Oqrr7jpppvo0qULtWvX5qyzzuLDDz/cprFuCxI9b1UxZ7vb/To4bA+cxcLBYQ9Heno6l19+OY0bN6Znz56Asua88MILZbS1iVyCdgV2lCYuKHTfeeednHvuuRU6L1g9uqK44YYbwm4Nffv25YorruDAAw+kfv36ZGRkhOe8efPmLF68uFwha3uxu33HAF9++WU4G1br1q255ZZbOOaYY2jevDlZWVlh974777yTe++9t9z+doYGN/gMPf/88xx11FEVOq880rOj0Lp1a44++mgmTJjAa6+9FrZIXXzxxRV+JoL3PHXq1Ah3pUQIZrYqLCzkvPPOY+3ataSmpnLdddfRp08f2rdvT61atcIZ3+bPn0+bNm0AYv4mjj32WObOncu7777Lxx9/zDfffMOSJUvIzc1l5MiRjBw5klNOOYX33nsvIRmvCiR63qpizmD3ul8Hh+2BIxYODnsJTjnlFJo1a8bixYtZv349a9euDWtsrVZ25cqV5fZjTfTRmtwgSbEuIvGQl5dXqbHHgvVZB1lhKlMx2MYzgFx/OnXqtN3jiYXc3FzeeustQH7YQf/2aJRXHGt7YQXatWvXUlJSklAYqshzUJV48cUXAY1x0qRJcS0JVWUlq1WrFitWrAi7w8VDSUlJ3O8l+AxlZmbusGeoKnHppZcyYcIEXn75ZbZu3QpUrihe8J7r1au3Talwx44dG47/eeaZZ7jqqqtitqvId52RkcFFF10UjnFYsGABH330EU8//TSzZ8/m008/5fbbb+fxxx8Pn1PRdaoq1iiomjmz2Jb7dXDY3eBcoRwc9iI0btw4/HdQS2mFogULFiQUtoqKisKxDdGCVHZ2dvjvRELyunXrWLt2beUGHgOHHnpo+O9vvvmmUue2bt06HIQ+YcKE7R5LPMyZM4eioiIAzj///Ljtfvvtt3KrSm8vbAB6YWFhwuDn4uLiiHoPOwMzZ84EVHk6kXtSVVRBB+jYsSOgBACJhMvp06fHrWR88MEHh39D2/MM7UwL0nnnnUd6enqYVBx++OG0b9++wucfcsgh4b+39Z7tdw2JfxPb8l23atWKAQMGMHny5LAAH13DpqLrVKxq6duCqpizeKjI/To47G5wxMLBYS/Bli1bmDVrFqA4jKAmrUePHoBcDl555ZW4fbzzzjvhQHB7jkWtWrXC2YQSCQVvvvlmlbj7HHTQQTRr1gyAl156qVKCeXJyMqeddhoAn332Gb/++ut2jycWiouLw38n0oA+99xzO+T6QQS/r1dffTVuu5EjR+5w60k07DwlmqNp06bx/fffV8n1TjrpJEBxOp988kncdv/5z3/iHqtXrx5HHHEEAG+88Ua51o94CGZjikdiqgo1a9akb9++pKenk56ezmWXXVap83v06BF2s3nqqae26Xdckd9EaWlp2Iq1LcjJyQkXGowO/A5mC0u0Tv3vf//b5usHURVzVh4S3a+Dw+4GRywcHHZjbN68mcMPP5wPP/wwoea1tLSU6667LpwpqHfv3hGa0r59+4atGffff384g1AQixcv5pZbbgHk+hErZalNl/j+++/HTGv7+++/lwkc31YkJSVx6623ArBkyRIuvfTSuIHRpaWlLFu2LOKz2267jeTkZEpLSznnnHPCqWtjoaSkhP/+978J28RC27Ztw/P86quvxhQqRo8ezdChQyvV77agW7duYSvPs88+y/jx48u0Wb58efg73plo164dAOPHj2fu3Llljq9evbpSLjvl4bLLLgv78t94440xhbHvvvuOYcOGJexn8ODBgFzezjnnHDZs2BC3bUFBAcOGDQtbCyyCiQdi/WaqGm+++SZbt25l69at9O/fv1Ln1qxZkwEDBgAwceJEBg4cmHDdWblyJS+99FLEZ/a7Bhg+fHjM82677TamTp0at99PP/2U5cuXxz2+ceNGfvjhB0Ba/SBq1aoVzuL0yiuvxHS5Gj9+PE8++WTc/iuDqpiz7blfB4fdDrumfIaDg0NFsGnTpnCRpiZNmnjXXnut9/rrr3vffvut99NPP3lff/219/jjj3udO3cOt6tRo0bM4mEffvihFwqFPMDLzs72/vWvf3kTJkzwJk2a5A0ZMsSrX79+uI9nnnkm5njGjBkTbtO0aVPvpZde8n788Udv3Lhx3p133unVqFHDa9u2rVevXr0KFchLVOTM81Qk7+STTw63b9++vffEE09448eP96ZOnep9/PHH3p133um1a9cuZpGrxx9/PGJebr31Vu+TTz7xpk6d6k2cONF74403vOuuuy5cETdR1dt4OP3008PX6NGjh/fuu+96U6ZM8T7++GPvyiuv9JKTk7127dpVyZyUV6Rw0qRJXkpKigd4GRkZ3m233eZ9++233g8//OA9/fTTXqNGjbzU1FTvoIMO2qkF8t5+++3wscaNG3tPPfWUN2HCBG/ChAneI4884jVq1MgLhULekUcembDInj0W67uOxgMPPBBu37x5c++ZZ57xfvjhB+/bb7/1Bg8e7FWrVs1r2bJl+Hvp169fzH5uuOGGcD8NGzb07r77bu+LL77wpk2b5o0fP94bPny4d+WVV3q1atXygJiFHG2hx1atWnnvv/++99tvv3lz5szx5syZ4+Xm5pZ7L0HEKpBXGZRXeXvr1q3e4YcfHm5z0EEHeUOHDvXGjx/vTZs2zRs7dqz39NNPe3369PHS0tK8ww47LOL8zZs3h9eS5ORk729/+5s3ZswYb8qUKd6bb77pnXTSSR7gHX300XGf5csuu8xLTU31TjvtNO+JJ57wvvjiC2/q1KneuHHjvGHDhnkHHHBA+NzHH3+8zD08//zz4eMdOnTw/ve//3lTp071vvjiC2/gwIFeenq6d9RRR1W4QF552N452977dXDYneCIhYPDboz8/HyvYcOG4U2lvFe7du28KVOmxO1v+PDh4SrTsV7JycneAw88kHBM119/fdzzmzdv7s2aNavClbfLIxae53l5eXneOeecU+69xxM2X3jhBS8zM7Pc89PS0rw5c+aUO55oLFq0yGvevHnCOZk5c2aVzElFqp+/8cYbXlpaWsyxpKSkeC+88IJ32WWX7VRi4Xmed/nllyd87p544olyhbnKEIvS0lLvb3/7W9xr1q1b15s8ebLXrFkzD/CuvvrquP3cc889YcKW6JWVleVt2bKlTB+24nKsV3lV7KOxo4mF53lebm6ud9ZZZ1VozTnxxBPLnD9mzBgvIyMj7jknnHCCN2PGjITEoiLXvvrqq72SkpIy1y8pKQlXcY/16ty5s7d8+fKEz1NliMX2ztn23q+Dw+4ERywcHHZzlJSUeBMmTPDuuece79RTT/Vat27tZWVlecnJyV5OTo63//77e+eff773xhtveAUFBeX2t2DBAu+GG27wDjjgAC8rK8urVq2a16ZNG+8vf/mL98svv1RoTG+88YZ33HHHeTk5OV61atW8/fbbzxs0aJC3Zs0az/O8KiUWFmPHjvUuueQSr1WrVl61atW8tLQ0r1mzZt4ZZ5zhPf/88wk1vytWrPDuuece7+ijj/bq1q3rpaSkeFlZWV779u29s88+23vuuee81atXV3gs0VizZo136623eu3bt/fS09O9GjVqeAcddJB31113eevWrfM8r2rmpCLEwvM8b+bMmd4ll1ziNW7c2EtLS/OaNGninXfeed6kSZM8z/N2CbHwPM977bXXvGOPPdbLzs720tPTvRYtWniXXHKJ9/3333ueV74wVxliYfH+++97f/rTn7zatWt7GRkZXtu2bb3rr7/eW7x4sed5nlejRg0P8P7xj38k7Gf+/Pne3//+d69Lly5e7dq1veTkZC87O9vr0KGDd9FFF3mvvvpqwmfw3Xff9f70pz959evXjyApuyOxsPj222+9q666yttvv/287OxsLyUlxatdu7bXtWtX79prr/U+/vhjr7i4OOa5M2bM8C6++GKvcePGXmpqqlevXj3v+OOP91544QWvpKQk4bO8bt067/XXX/euuOIKr0uXLl6TJk28tLQ0r1q1al779u29yy67zPv2228Tjr24uNgbNmyY17VrVy8rK8vLysryDjzwQO/+++8Pk7+qJBbbM2dVcb8ODrsLQp63g5OqOzg4ODg47IZYsmRJRIKAK6+8chePyMHBwWHPhgvednBwcHDYJxHMDGQzQDk4ODg4bDucxcLBwcHBYa9DXl4eubm5EVmZgpg2bRrHH388mzZt4rDDDquyGhoODg4O+zJc5W0HBwcHh70Oq1ev5oADDqBv37707NmT/fbbj/T0dJYtW8aYMWP497//TX5+PqFQiCFDhuzq4To4ODjsFXAWCwcHBweHvQ4LFy4sN+d/WloaL774IpdeeulOGpWDg4PD3g1HLBwcHBwc9joUFRUxcuRIxowZw+TJk1m9ejXr1q0jMzOTli1b0qNHD6677jpatGixq4fq4ODgsNfAEQsHBwcHBwcHBwcHh+2Gywrl4ODg4ODg4ODg4LDdcMTCwcHBwcHBwcHBwWG74YiFg4ODg4ODg4ODg8N2wxELBwcHBwcHBwcHB4fthiMWDg4ODg4ODg4ODg7bDVcgrwIoLS1l2bJlZGdnEwqFdvVwHBwcHBwcHBwcHHYKPM9j06ZNNG7cmKSkxDYJRywqgGXLltGsWbNdPQwHBwcHBwcHBweHXYLFixfTtGnThG0csagAsrOzAU1oTk7OLh6Ng4ODg4ODg4ODw85Bbm4uzZo1C8vDieCIRQVg3Z9ycnIcsXBwcHBwcHBwcNjnUJFwABe87eDg4ODg4ODg4OCw3XDEwsHBwcHBwcHBwcFhu+GIhYODg4ODg4ODg4PDdsMRCwcHBwcHBwcHBweH7YYjFg4ODg4ODg4ODg4O2w1HLBwcHBwcHBwcHBwcthuOWDg4ODg4ODg4ODg4bDdcHQsHBwcHhypDt27dmDx5cvj/rl278sMPP+zCETk47NkoLi6muLh4Vw/DYS9AUlISqampFapHsa1wxMLBwcHBYbvgb1JZQCpQHxnES5k8eQ6hUHUgDwDP8yrU53777cfs2bPD/7dv357ff/+9Ckft4LB7Y8uWLaxZs4a8vLxdPRSHvQipqalkZ2dTt25dkpOTq7x/RywcHBwcHLYJSUlJeF4KUAdoAfwJ6Ai0BzKBLcBsYCbwGfAHoVAaoVAxpaWlZfpLRFBmz14VQVCys7PZtGlT+Nzs7Gxyc3N3xG06OOx0FBYWsnjxYlJTU2nUqBHp6ek7VMvssPfD8zxKSkrYvHkzGzZsID8/n2bNmlU5uXDEwsHBwWEno2fPnnz66afh/0855RTGjBmzC0dUeUjIyQE6ATcCvYH0GC27mfe7gQ+Ax/G8mYRCobD1Qn2lUj5BmQ58Cixm06Z80z4VKGXTpsII4hFEy5YtWbBgwXbfs4PDzsKqVatITk6mRYsWO0Sr7LDvonr16tSoUYNFixaxZs0aGjRoUKX9h7yK2qX3YeTm5lKjRg02btxITk7Orh6Og4NDAnTs2JFZs2aF/+/QoQMzZ87chSMSymrj07DaeCgEiqisu9Cugu6lPnApcCeQXYmzNwH/Av4DrDKflUdQCoBliGB4wE/Ay8Aa4ALgJOA9YASwFs3rnju/Dvs2PM9jzpw51KpVi3r16u3q4TjspVi+fDl5eXm0adOmXGtYZeRgZ7FwcHDY45HIhWbWrGXb5ONfVcjIyKCgoJTKuQulACXhPnanAGifVAwCBkYd3QT8gQT5NHS/0aQjG3gEaIisGBlAP+ITlLXASrRdNcEnIWcCjyKC8jQiEE2BPwMdgNZANURK5hLtjgVFjmA47JYoKiqipKSEatWq7eqhOOzFyM7OZsOGDRQVFZGWllZl/Tpi4eDgsMei4i40u0aorJy70Gwz5k+AeUgzL8K0rQHQQbRp04b58+fHPHbEEUfw3XffldtHUlISup9L8UnFb8C7wBRgUYyzmgNdgLOB/QOfN0UE5WLg74gURmMlskrUAhoAQZeQLOBKRMD+DVyGLCF2fj1kpSgBDkVE526sOxZEumM5OOwusPFHzgXKYUfCPl+x4t22B45YODg47JHYHh//nSFURroL3QysR0J4GhK2rSC9GHgQ+B6oDZyBBPBsIB9ZXtZT0QDosmPAXCvJvKeYv1MQcSlh0qRZEcSlU6dOzJgxI9xPp06dmD59ugnU7oSsC38ADwE/mHF3R5aH1sgKsRWYD8wCxiJXpW7I0hECnkJk4DzTV4eo0a9FpKI+EMsdZAGQC/zF3Mvnpn0Te/dorouAYvOeDpwL9MS6Yzly4bC7YmcHa2/ZsoUlS5ZQWFhIWloaTZs2JTMzc6eOwWHnYUc9Xy7GogJwMRYODrsXqtLHf0csgRpfHeAIZFFZjDTo4RZAMyTo/4608deiWIGgSXolsML01RS59fjkCHJjjt+35OQAjZBLUK75+0REXBqbMa1DQvpc4BtgKSI0mdjAaLk2FZh+nkaWg6eQJSHWuKNRCHwJDAU2oO8rBXjLHFuECIn9HguQ1aamGWc0liDi0RDN3WbkAtXKjC+IoOUijUirx2PAw+yo58DBYVuwdetWFixYQKtWrcjIyNih15o/fz7vvvsuX3/9PXPn/kFBgYfnQSgE6ekh2rZtwQknHM7ZZ59N69atd+hYHHYuKvOcuRgLBweHvRaJffwrgqCP/8NlNNaHHHIIP/30U9yzDz30UH788ceYxw477DCmTp2KBPJkRGJ6EluT/x9gHIoVuMYcjxbO6yOh2LoDZRFL4167dm3WrVsXOC8LOAg4CvjOnHsPkQQgH5GKusiCcgKyIEwBXgUWAgeaz4qQu1EectkaBZyFLDGxXJiikQacChyHBPkXkaUiC5GVVESgLLFYhranWNlK8hCpqBM4Xh0RnDvQ3AYFIEuySs19BInFQHPdF5zlwmGfwrJly7jvvof49NOJ5OXVJiWlOxkZl5KV1ZpQKAPP20pBwXx+/nkWP/74Oc899xannHIUgwcPonHjWGTfwUFwFosKwFksHBx2D/juTxcjl6EtSLN+GLHdZRKhFPgH8ALS5oPvKpSCCEAychtKxcY7SJteTNm0pvbcdHNOyLTLQm4+tyBrAcBI4H7gb0jAXoU06g0QCQiiBJhj3jsGPn8NCfZboq4btDDkAJcDg/EJQAEiDXmmvX0FzeIFiPi8bT4/HlkJmiB3rr8iwpFKYv1UrO1lJSIp7yEi0Nf0vR7ojAjPfHOtmjHOn4m+l3ZEkoQC9Ez0AG41nwXvybpDRVstchFRm4LnFSa4FweHnYMdbbEYNWoU99zzGMuX1yQ7+1pyck4iFEqN297zisjN/ZJNm4bSqNFG7rrrZvr27Vvl43LYuXAWCwcHhz0K3bp1Y/LkyWU+39YMRyIV9vU20pqH8F1dYgnwFnlIeLXZiuog4nAy8F+0FDZDBGUuEvTrm37aInecIiTEr0CC+fvmvToKHD/ZXL8dfnzEHGAG8v+/ALkiPQgMAfqg+ABMHyuB5cBGJOwWILLQChGORebz8YgslCJLQzBgvZq57hvAaGQpOcOMoyVyGVqFBOtMRDyqUZaU5CMrSm1kXfjJ3Pda4HxEiIrMy0MEw8IjNqGw2BwY/2NAVzOGNaa/dej7iLV55SKC0NjcQ/A6aej7Cj5bwePJ5txiIolFDrJc9HdWC4e9Hi+//DL/+tczFBX1pXHjm0hKKj+GIhRKpUaNnmRnH8fKlUO48cb7WLduHVdcccVOGLHDngZHLBwcHCqEI488kkmTJoX/j5VJyA8Gs+4t9ZDA6vu5T548nVAoEwmv5Wc4isz81Bw4BWm22+FnfoolwN+N4he+R0K5FXhL8IXhVabPnubzUebcR5Hm2wrMlsDkmnMeQRr2LsB1ZkxFiBxYwbWDOe4BtwMfAU8CFyKB/abAXS4A3kGkwWZWCmrbG6O4iMlIY98BP2A9LXBvIUROfkSk4nYU07ASxSwkmTlriMiPJWpBVANqIDJzPRL0XzPz08h8Br4Vp9i8Rwv60fcAPnGpg4T571EA+EOm7RZEAnPMWKOx0ly3RoxrYeblPXxLVjQseYrGGYjwrY1xzMFh78CoUaP417+eAfrTqNGVlT4/KSmTRo0Gs2ZNQ+699xnq1KlDnz59qn6gDns0HLFwcHCIi0T1ISZN+i2q0nE6EpibIteZ9kgYtprnP1CGoC9QgPAWoDBhhqPIzE/XIT/9HMoKrF3N+13IkjAECdaNgEtQkHdzcw8FKJB4NCID85BWHnzN+UuoyvPZQBt8ob0mEug/Q65AFwEfIw18olSrfYFeKI7hYSSot0MEZgIwCT+z0uX49R+CmZWeNGO4EgWsBzX6VsgOISG9JiIuSabfXHyBviG+0B49j/Z/e781UCzHWERY+iMiU4KIRAo+aQxalGL1jWmXj+YqCVltnkXPBvjuSqWIFFoLU1P0DOab+4k37tZmPEuA/aLmBnPNEtN/kLhkIHI4O8aYHRz2fCxbtox77nmMoqK+20QqgqhT50pWrFjB3Xc/SteuXV3MhUMEYqmEHBwc9nEkJyebeg91UA2A61C2ndHIKjDa/H8dEuZqoEDfe4DXkbXgGhQs3MW8zkI+9TY70GFIE98Kz8suk/rOD9L+KzAGaZWrEVtgtdhixvECEoJTkOb6VERyMpCA/joiQtcBTyDh9kUUtHwXSov6Jco0dB0iQh6KyfgMuQLlAVcgobububfhwJvm/Q7z+deI2NyKiMKDqG5DCSIJHwADkEXjNjNPnZDr0kEouHu1uZdBwH2IIBWbe7YkIIRI0ndm7m1MxWrkXtXAzHcswT/achH8OxuRxCbAMWjbsC5Q1poUtFzEsoLMQ4TqAjNnVyAC9RwiLBeg7+UuRIh6I9J2uXk/wczLK8hqEW/cNt1wYZw29j2axG5Ez3AoHAzv4LA34b77HmL58po0aHBT+Y3LQSgUon79gSxfXoP77nuoCka3bZgzZw5XXHEFrVq1Ij09nerVq9OiRQtOP/10XnnllTLtP/30U3r16kX9+vVJS0ujcePGnH/++UyZMiVm/y1btiQUCrFw4cKYx/v160coFGL48OFxP58xYwbnn38+jRo1Ijk5mbvvvjvcrri4mJdffpkePXpQt25d0tPTadq0KT169ODpp6Oz2wlffvklZ511Fo0aNSItLY369etz5plnVqgO0c7CbmGxePvttxk2bBg///wzhYWFtG3blosuuoiBAweSmho/oCgaw4cP5/LLL0/Y5pNPPqFnz57bO2QHh70W5deHWIlcWFKQcJ2N/O7PQYJbOpEByEGhzkOa7p7IqjEEua4cAfwU9nEvm/mpFAnl1q1qC5ExE03NZ6uRtr4B8ADS/D9jPgshMlQHBU6fYu6hwBxbh9yG2pt7vhmfBP0ZWR1eNWP9zNzjfYg8xVqnOiGNfLCfC5DgfKOZx5fN2J8xY7nEjCUFX3M/GcVM9DPnQWR8Q3AZf8+MqzsiLkVIcK9N7AxLFtGpcKNRhL4vzJhC+IHQBMbrF/UTliILyndmDCciVzBbwHArsmD9F5HHhsgFrRuyOASzaM1EpPYblO1qEH7NCosC8x4r9W00CfkFkcCpaI5snE4m69cX7NJq7Q4OVYn58+fz6acTyc6+r0IxFRVBcnIW2dkD+PTTweEA4J2JGTNmcPTRR5Obm8t+++1Hr169SE5OZsmSJXzzzTcsXbo0Qh684447uO+++wiFQhx11FE0b96cX3/9lREjRvDuu+/ywgsvVHnMyMSJE7n66qtp1KgRxx13HPn5+WRnK/Pdxo0b6dWrF+PHjyc1NZWjjjqKxo0bs2LFCn755Re+/PJLrrvuuoj+brnlFh577DGSkpLo0qULxx57LIsWLeL9999n9OjRvPjii+XKwDsDu5xY3HjjjTz55JOkpKTQvXt3qlevztixY/nHP/7B6NGj+eyzzypd1r5NmzYcc8wxMY81aRK9ETk4OFjErw/xGdI4z0buKKlI4EsFrkYuQ+loSdmEMgfZKsuxBFUPWR8GI4H3eWRV+DhAbGx1Z5uFaSHwIRK0/yBSGC414z7cnGeDc/+Cgo6tlv9c4AYkfIfwSUU6cptKxbcO1MJPkToE1Y4oNdc+CxGEimzSqVH9PIB8+W9HblCbTX+PIjesG4kMNL4CCeJ3RPUZtBIUo5iPr8x1bCzDUtM2SCoswYsmARbRQvQWlP71ZPxMU5hr2D7seEvwDeEjzf3WBO7FJ2D2uUkxbd5GsTj3IEKx3vQdzJDVCTgdpZSdgIjYn83/h+ATzF/NeJrGuC97b4uQ1WgqSrV7EorNaIq++2z0He+aau0ODlWNd999l7y82jRpclKV9pud3Z1ly2rzzjvvcOutt5Z/QhViyJAh5Obmct9993H77bdHHMvPz49IHDJmzBjuu+8+MjIy+OCDDzj55JPDx/79739z1VVXcfXVV3P44YfTsWNHqgovvvgigwYN4v777ycpKdJB6IorrmD8+PEccsghvPfee7Rs2TJ8rLi4mI8++qhMX4899hht27bl3Xff5cADDwwf++abb+jVqxdXX301xxxzDO3atauye9gW7FJiMWrUKJ588kmqV6/OuHHjOPTQQwFYs2YN3bt3Z/z48dxxxx08+uijler3mGOOKWOacnBwSIzY9SG+R4LtWiR4XYyEvJrIbeVUFMybj/z4tyLBbityf2kbfRXzskIpwFVIez8G392lI3ILykPE4FEiKzxfglyw0vGDt39HdSE+Ao4099HYjH8MIjKD8IXyUvMKWhvq4gdh27iSTERGpgPfIvecSE1SxZBJJJGqi8jJtcjVpyESmOuieU5CGvq1SOiuHtVfCn5xua1m3CvQ95OGn92pIWUtKpZcVARLTNu2iDQ2xHfDKg70beMXUpAl5llksbmJyFoXdu7/bdr0NnNQAz+OZTWyIhQTmUY4FcWmHAmMQM9ebfy5saRkGH58TBD/RVaruohcnBwYfz4Kom+FXL4gVrV2i9NOO63M5u/gsDvi66+/JyWle8KUstuCpKQ0UlK6M27cD+xkXsHKlXKJPO2008ocq1atGscdd1z4fytDXnPNNRGkAuDKK69k1KhRfPjhhzz55JO88MILVTbG9u3bc99995UhFT///DPvvfceGRkZjB49uozCOyUlJSIovrS0NOxC9eabb0aQCoDjjjuOO+64g7///e88//zzlZaZqxq7NMbigQceAGDQoEFhUgFQt25dnnnmGQCGDh3Kxo0bd8n4HBz2FSQnJ+NbCW5EguSNKK6hFnLjmYBciC4APkEC35lI8NyMApWb4btNbUSuRbFgCYbVeA9EAuViM47rkBvMh2ZMi1EQ8UcoPuEUJDQ2Rdr805HL0bvI0jIX1Yd4B5GTAWY8f+BbM0rwte1BNDCfrQh89hMSOi9B6V2L4txXPASF+KuQW9VjwMHmHh5G1pWzUEXrhWh5HmKOnxGjzw1oXmz61XQz7rbm3I3ET9saHFcoxisIG6/QAN13HpEE0cK6qY1ChOFqZGWxpMLDD5oOthmMSFfIHEtG5KUuysBlC/+tQKmEL0AE8s9oDv8PEbWXzfELkXvV+fjxMaBYmsdQEP07wGlEZv2yblRBa1EqcvEbg2J9bPKC+nz88URCoeqEQqEygoODw+6CLVu2MHfuH2RkdNgh/WdkdGDu3IVs2bJlh/QfD926dQOgf//+fPrpp2zdujVmu+LiYiZMmAAo9iEWrrxSwexfffVVlY6xb9++Zm+NxJgxYwA4/fTTK+RFM23aNJYtW0abNm047LDDYrY54YQTALlf7WrsstVw6dKlYVPVhRdeWOb4McccQ7NmzSgoKODjjz/e2cNzcNgnEAopWLW0NBlpuq27zYVII9wPkYizkRb8N6QlfgsJ2lchi4ANsL0S+fnnIQFtXrwrE0kuspDWehIiKKeiAm0Po3iMtxCZKEECoI0tSDOvdPOeadr/F/nq32vGcxbSbI/EF5yt2060IC3BUWSp0Bx/DhGpOyhLOiqLIJEagojKr+a6N6MYkHtMuznmeFrU+WuQRj8HEawcfLKTYdpsRpr8shtbYkRbMuy1LYlYFWgXHZuxDL9Gx1VRfdq2K1Ggdh/0vAQzWtn3ECIWtdBcj0CEYhEimB8ii9bF+HUw6iE3sMHm+L3oGT0HEeBnzPX+SVkXtlJ8Fz8bm1KELEH2cxt0X9e8nkTE5VA8rzahUFql3XZBe539HYZCobhuvA4O24IlS5ZQUOCRnt66/MbbgPT01mzd6rFkyZId0n883HrrrfTo0YPvv/+enj17kpOTQ9euXbn55psj3KDWrl0bJh3x4kDatJFlc+nSpTGPbyuC7k1B/PHHHwDsv//+MY9HY/78+QDMmzcvYq0IvizRWr169fYPfDuxy1yhpk2bBkDt2rXjftldunRh8eLFTJs2jQsuuKDCfc+dO5fBgwezatUqqlevTqdOnejduzd169atkrE7OOzpSElJoaQkCQmx6UhYvxH5lw9EGuG/IxcgkKb/HlQfIQtp7o9FQm0wwHYWypL0LtLGn28+i9aWBQVN+/eJSIA7EfgUZWm6GmnyC/GFfFs5OagxD2raPSSQ3oHvdtTC9Dsl6rrxdCvZaHlcj4T46SgIuToiHUvx/fq3BVnIijIYzeVm5P5TDwmrt6GA53xE+IJYH2hr17Rgkbqt+AQsVmpei6BLWnDuotHUHJ+Pvsdi5J5VO3COJWiP4ae6tQhaNUKILNbCd7eLJhbBtg1Qhq0RiNwOxCcFReY+ayDrzAr0nS1Az0czNL+voTiOTigAfaYZbzX0XVY3Y9iEvvd0IuNQSs11CtHzuBw9m0XIOjYYpRx+nK1bZ1aoyJ7vUmUtZjXR76iUCRNmusBxhypDYWEhngehUNVX8AYIhdLxPF1nZyIzM5PPP/+cyZMnM2bMGCZOnMjEiROZMmUKQ4YM4ZprrmHYsGE7dAyxUqQHsS2KhkTXadiwIaecckrCtruDnLvLiMWCBQsAaN68edw2zZo1i2hbUUyYMCFs+rLIyMjg7rvv5h//+EclR+rgsGehTZs2YQ0HQOvWrZk3z7cclM36dDVKbdobxTFEZyB6HWlnrV96ayQINTTHreDTEbns3ISyIA1Dwv155nhtc160MG5JQTF+LYyn8TXaW5Fwl0Kk60p0H9ECWBJyX1mN3GAuRhrvLUiojHeeHU82Eu7+ix/kC5GkI16mpVhZq6K15N3Nvc4z9zURBbw3Mm0fx48psLDxHzURsQiOPUgA7DlpJE7PC7HJRJBoZCJiNgN9xw2QEB9CBMGePw/F5Nxnzon1fdh0uPcG2tgsU7HIzQfIbelviGCG8LNlWdepFPQsLkVZoqyloQQRuL8hV7F3kcWtB35M0EJzfl3Tvg6RNT7s85FijhejtMETEOm+xLTtgyxl/wL+E5NcRNaEqYm+m2C1801m3MehJAR+4HhycinFxcU4OFQWaWlphELgebFdhbYXnldAKKTr7Ap07dqVrl1Vx6i4uJhRo0Zx6aWX8swzz3DOOedw7LHHkp6eTkFBAfPnzy8TnwC+RSDaLcne06ZNm2Je21oeKgsr9/72228Vam9l4Tp16uwR8cO7zBXKflFZWVlx21SvroC83NzcCvXZsGFDbr/9dr7//ntWr15Nbm4ukydP5tJLL6WgoIBBgwaF4zoSoaCggNzc3IiXg8POQOfOnSPMm507d67Qef451Zk/fx3SxDYE6jN//rqwL3jZ2hDnIkHsT0ioj85A9CSqMm390nsiQSuD2K4w1qJwKtIy90IuTW8hF5p5KC7AFlQLuiItNP1ORILXQHwhMpWyAchBTXc8ra51L6qH/O7z8IOR7bkRMxn4uxrS+k9D1g67cSbhk44g5qO5Ohe5hUXXYTjXHLekL830O8Pc462m7V+RReBbJAC/GThnBdJwW0IT1HxbAjCLSEtOIo13LGE+FhHphubPFtyrjQjb6sC575nPg5ln7DEbf/EufgC+fX5ssb1oWLeqM9E8FiMB3Fpn0s27/T5SkeUnG79SeSPT/i8ormUoEt6bmOPNzbWXmWva79gz19tqXjYzGab/G9Czejd+JrQ/EHm9HKgbJhL63VW0JsxBKA3uEPRbexboQklJVpk6Lxb169cv4xZhBREHh6ZNm5KeHqKgYH75jbcBBQXzycgI0bRpvExsOw8pKSmcc845Ya3+Tz/9REpKSti9MJ5Q/vLLLwNw4oknRnxuicavv/5a5pwVK1YwderUbRqnLXnw8ccfs2zZsnJaizzVrVuXWbNmMXPmzG265s7EXhVx1rNnT+677z66detG3bp1yc7OpkuXLrz66qvhKPl//etf4WwC8fDggw9So0aN8Mst0g47EkFSMGPGEoKkYMaMJVGkINa5FRVaDjbtWqGq0dlICE1GmujPkEB7ozn2OgqIvRa5fGTi+/FbASw64Df4fxZyE7kGxTZ8Y9rkIXeVDVF3Yy0T81Fxu2r4WYaijasVEZbt31nm/hcgK8NiYgvU0UhDgv0y4ICotpZ0lJrj1yO3ry9Q4b/BKLvV/8z7YPO5DSq+HhGpdogspCPXKHvOcHPOpSgt6vnoe5iPng8riHtRr24o5axNQxtN/iqD4HlnmPHajdRmcFpnxrQBWSu64xPAWMTve3yXN0s2gq8goiuIl6L7tkHqwWtYcrEVCfwp5rNi9F3VQM9htunXjqsmCna32cAWIvJSgB+Dk0pkHE8aShbQHBHFDsjF6nwUBzLaXLN6wDrYFZGEiSjd8MXou+pk3i9G1sCJpt1hiAQNQG6BChyPJCtaM1avLiS4ZkBNlixZH0E0HPZdZGZm0rZtC7ZunbVD+t+6dRZt27YkM7Nq6mNUFM888wy///57mc9XrFgRLnjXokULAG6++WYAnn32Wb788suI9sOHD+eDDz4gNTWVG264IeJYjx49APi///s/NmzYEP589erVXHrppWzevHmbxn7wwQfTp08f8vPz6dOnD4sWLYo4XlxczAcffBD+PzU1lbvuugvP8zjzzDMZP358mT5LSkoYO3YskyZN2qYxVSV2mSuULRKSlxet9fNhv7ScnERZTSqGG264gQcffJA1a9bw2Wefcckll8Rte9ttt3HTTb6PcG5uriMXDlUObfipSNhvgawGHfGLh21BdSNi59Mvv5CdRTckLF2Ishv9F7k6XYaIRJq55m34GYgWImvFWUjba2GLoEUHPScSXv6ChO/XUGEzK/jZdKLWJzQDuafsh7Te1qph08OWJyAnGkN3ZLVYgoR3q1WP7jMYb5CE7/LTMqqtJVbvIuJWE79WQ1qMfjtRtujexUQWFTwVzX8G0qjXRG4xDZHV5GkU9zIIafFjuXCdjSxF3yICaS0+iQK4gwQknq5poenndeT2Y+NdMtH3MxdZoy7Cz7Zl59Km9d2MtPq2CCDo+7dpaq27WzIiK0GXKWstiCYtBP6vhgiOvV8Pv4BjGiIXf0OxQgvM/Nh7bmrarDD/NyH2nNlxV0cWhWXo9zsX38XJM38Xm7Gfg0hNW2L/PoNIR5Yt361KVpfN6Dm426QMjbVmVENEOHLNgNwwuXDxGvsmTjjhcH788XM87+YqTTlbWlpIcfFYjj/+5PIbVzFeeOEFrr32Wlq1akWnTp3Iyclh9erVfPvtt+Tn59O9e3d69+4NwKmnnsrgwYO57777OPnkkzn66KNp3rw5v/32G1OnTiU5OZnnnnuuTA2La6+9lhdffJGpU6ey3377ceSRR5KXl8fkyZNp3rw5ffv2ZdSoUds0/ldeeYXTTjuNSZMm0a5du4gCedOnT2f16tURv9cBAwawaNEiHnnkEY499lg6duxI27ZtqVatGitWrOCnn35iw4YNPPvssxxxxBHbPK9VgV1msbDR8osXL47bxh6LF1lfGSQnJ4eLhpSXvSA9PZ2cnJyIl4NDVUIbfXUkbPRDLhU3Ub4mswuQbc6vhQTJd5AwsgH5pL9p3oOWuRVIYL0QCcNnoADUd8zxVUggsRmI7kEC/y2BPqzwVxEhP+JuEfGphWoX1EOCaRZypdlg2rU01zgOXzCs6CZYnlY2zfSbiqw08ymbLtXC3pv1fQcbWOsjCbn+/B+aszeRMFien3EKym71lmk/HJG7uvjWmoX4grSNPTkVCfWnI433vwPHg6/WqMbDs/gBx/ECDBN9h8FjeShepidy+RmCLAMeIratzBg9pMW3Gn8bcG/JjTX529oSlqRaa4CNnyhA5KgWImpBImuDzYMB+JbApEf1W2r6qmaOp5j+aqL5j77/+khg34BfODHaIhdEJzTH9yPCUgNZLV5HbldZyDLV37Sbi75rTP+r0W90NX6qW4tsZA35u+mnIQpAD5nr3gq8gDJc2TWjM2XXjGeQ9UOKPGe92Ddx9tlnk5W1jtzcL8tvXAls2jSWrKx1nHPOOVXab0Vw//33079/f2rWrMmkSZN4++23mTVrFocffjivvvoqY8aMISXF153fe++9fPLJJ5x66qnhitvLli3j3HPPZeLEiTGrbtesWZMJEyZw6aWXAvDJJ58wb948/vrXvzJx4kRq1KixzeOvVasW48aN49lnn+Xwww/np59+4p133mH27NkcfPDBMQPPH374YSZMmMBFF13E5s2bGTNmDB999BHLli3jhBNO4KWXXuL888/f5jFVFXaZxeKQQw4BlAosXjl4a84K1rjYHqxdq0XdWkscHHY2kpOTTYaH6kig2oCE0v8hgSgTuVbcgrTsFkuQlrU+8lGvhn6+3+JnXLLClXUxKTLt2iFh4yxzLBNpzkFWiXR8X/JOKMD1RyScWPO27c8Kb4X4AdAVQTYSsP6FrBI1zXs2fjG6JPNqRewaExUhNImOt0JzXgsJrjcTX+iGyKxP1jXGEp33kaA3gMi0quWNwcK6idnUslnoe8pFJM/Wp0gzn5Wi+b4dxQ88h8hInxh9D0IB86+jmJlaxI9jsIjnNuWhIPKNKLvWDyg4O4Rf46QECcxJ+NmNCvADq4NB0Jjjtkq3dYkCPVul5tgP6PmPHrMlEcE+7d+2H/udBt327LWzkCuWTe4RLWhb68AflM1kFo12Zvzz0HfZEs1RA2A8cmMbhMjYamRN+AMlEIBIvZ5VdqWaMdQx/w9ERPh1ZMGsiebtDfMCPQ9dkNLgAPQ7Xo8IYSdkHXsFPa+5LuPUPojWrVtzyilH8d57w8jOPo6kpO13WyopyWPTpqGcddZRcTN77kicfvrpnH766ZU6p2fPnuH4hoqiSZMmvPrqqzGPDR8+PGbcRrzPo5GWlsbVV1/N1VdfXeHxHHXUURx11FEVbr8rsMuIRdOmTenatSuTJ0/mjTfeKFOSffz48SxevJj09PSYlRUri6lTpzJ79mzAL6zi4LCzkJmZSX5+MRLEmiOtdWcknFi3pzkokPczJCQ0RML9x8gtpDYSivohwe11pAXtiFK/NkNCRg0khM4P9HcbyoxkLQbrkfC5Gblb/Iifgeh/SGgNmretW1K6eeWb6yRCdEDwCcid522kcc3Dd1tZYfpMx8/QE8taEYtcVEQ48ky/mcAhwGS0/FnCEEuAzUffFfiCZgl+HYa+lCUV5Y3B3oPFecht5VskcLZAZGs5SnObhATFDfia9yvRfD2GfPcbR12nEdKUP4AIbH/8wnPRSKTB9tDz8j4SnJsgF6yv0fe4FpEMSyqS8SuvY/4PCs+WpG1G5CFW7EwIEbol6DmPJiW2vyAJstcrjfN/tGG+E7LUbUC/xyCSEDFYhJ9+1l4vGtVM+0IUa2K/l7vRb/smM5bq6LlejX531cx1M/FjR2y9jFxEKpeg+XsUpUjuhH4/xyCrlHV7simev0Tf04FmHE3M2OuieX8UBas/Zd6XEu1a6bB3Y/DgQXz33fmsXDmERo0Gb1dfnuexatXjNGq0kcGDB1XRCB32FuzS4O1//vOfADz00EMR0fVr167lmmuuAeRXFjQ3jRw5kv3335+TTjopoq8tW7YwbNiwmGnBvvnmG84++2xAxYgcsXDYmQiFQuTnpyKt4lAUxPwACvTsggTWLub/+5E2dRgSHi407e9FRb/+gQSeR5Fw9giqG/EgSht7IvIXr4ncZ+5H2tOhSMA5Dd/NqT1wF9Ig/4rvMz8ZuYwE/dltxeQCJKhsREJ2hWfBnNcdaaNtEbo8JGRtRkKX1UDnUTXLkxWYNiFBLQuRhUVIMLMBvrHO24RIWGNkxbGuOg+ZMd8Q57xE44gW5PORZrsx+l7An5v6+C5Hq4hMyzoQEbuHKAsPxXNchzTV96H5LYpqkwh56Nl5HhGTvubzzcDPSJj+ClktRiNNeRISdGPdq3WbKkJkOFaWLwubtSuoBQ32Gc9FybqtBRMLQFmrVFvzXjbTi1DDjG0Fsa049rr55lhtpBhIQvPkoargmfjF9kLoO26Mnrc8RCDmmvvNQL9bm6nqG0SsVqDv4SNEGE5F5KGdeT8TWbE+QWvKGuQ+9T0imDXNODJQnMdFyCXt3/iulTnORWofQOPGjbnrrptJTR3FmjX/Lv+EOPA8j7Vr/01a2ijuvvsWGjeOVmw47OvYZRYLULnz66+/nqeeeoojjjiCk046iaysLL788ks2bNjA0Ucfzb333htxzsaNG/n999/LlG8vLCxkwIAB3HzzzRxyyCE0b96c4uJiZs+ezYwZMwCl8hwxYsROuz8HBz+966Vow7fay0Sw8QAtkZvUpyj+oSfSlL+C3JpuwXdHsgJ5jrnGSqT1LkJuFWehWID7kKXDM/1UR3n9vzafrUWCRwfT31bzWR6+AO6Zv38z17dBq4kKMFnhrAOKTdiCXw+iyLxPMO0KzTWtNjla6AlqsCviHlWKBHOrIW9tzlmMyFWBGUNQ0N1k7rEWsgp8iVyn5iHf9bvwtdkVRfR92NoFjfCL4v2OgtdL8eMu1qD5Wos00SH8SuV34AciR1/ncvP37ShofyCyQmVEtYse01hEbDciS0Vfc6wUCbmzzLVXIOvTQhSzUISyRvVAc2djVLbiz2dTJEzH2nqsEB+Ma7HjtKQqun0o0MZavGx8jiUY0W571l0wXhpx+ztaH+eYxRx0z93wLV+foBiSg/CrzyejZ2s+im/6Bj17KUSOv4XpqwRZVHqjec4yY96InuM6+NbDZHzSfyKqf/EUUjisI5L82tTL35njL1Ne7Q2HvQt9+/Zl3bp13HvvMyxfvpwGDW6qlFtUSUkeq1Y9TlraKO644xr69Inliumwr2OXEguAJ598kqOPPpphw4YxceJEioqKaNOmDYMGDWLgwIEVLrqSmZnJHXfcwZQpU/jtt9+YOXMm+fn51KpVix49enDuuefSr1+/XVbExWHfg08qBiEBoRg/wxHEFuzAr6zcHFUpbouCMOcgl5lriczUFO0CkoSE1VTTTwoS/LOQhrsREixuQ5aOW5BGtBiwqeqaI/cfKxzlIOHMFvWyAmMJEtDWIZLSkNhZkez/QaF+fyScbzHXeAe/pkAbJETF0obFEn6CvvZB0lGKSJYdZwjfvSkfP+uU1Sqn4BMRSwIvAEYhcvELIhvHkzhmobzxFpvxlCCrSA/k2vQGSu3r4Rd+24TmaJ0ZT30z1u4o/uEd5FpmYZ8DD8VYgAjCLchdrjtyrWmDX3V9HtLgf2WucyR6bm3BqGLkSvcqSmv8GZqnJsi6lYOE5tGIiATjK9LNcTtvXyABN7gWBy0DlnhbtyprMUvkylVq5ikn8HkSfsrgoNueDcwOWuKCfYGe9TX4LlvBcdp2M9Az0hiRuwJkjeuJSNF69P2tQr+7oDtjO/RbsClyrUvTh0iJ0Am52WXjV1HPROTSWuWDgevB8f/ZjPkRpDC4Gykp7L1eg8jmDOQW+IgZ58OOXOwDuOKKK6hduzb33PMYy5ZNIjt7ADk5JyXMFlVaWsimTWPZtGkojRpt5O6773CkwiEudjmxADjvvPM477zzKtS2X79+9OvXr8znaWlp/Otf/6rikTk4bBuU0zsHWSoGIEHc+qEncjsopGxl5b8gYeM15O70l6hz4vVXFwmv35u+bAG1G5A14xWUCvMwJGTORBrzrYiQ5CDBsTpl3ZLSkEBui7VtQgLUfPN/zajxWY2z1UIX4KcIXYdS4M4z581FMSMbzHXqEiksR8MKiUFyYduuRQJeY1RLo2XgXpLxLRXWCuMhgbIEv7L4Afh1BdKQcJyCTxQriyLzsmTMzslJyGpjU7CGzBjrIZ940ByXoDlOQ0Lq5Kj+7RxYYfwKNN//h77bccjSEB0D0Rz58Z+NnoeQudZy5Hr3prnf2WhOf0G1Up5EZPcjlJ3sD0SG6yEyYbeZEH463C+RWw+UjZdoat7no1iFEnM8SCyin4OghSk4D9XRb6AB/nNhM4I1QL+3WJa2DNNmCz5ZCV67AJEr6wIGIuF/oHS6toDihyiLVk38dMSp+C5Saea+Opv56IXm9X8ow9R1yHKRjH6/uWZ+1pqxpeATYFsw0ENrTh6KnzofEYh+5p4bmf7ORUkA/oSsWSuAFxy52AfQt29funXrxn33PcSnnw5m6dIhpKR0JyOjA+nprQmF0vG8AgoK5rN16yyKi5X96ayzjmLw4EHO/ckhIXYLYuHgsLdBgdqHoHSQtlhZeaQCylZWBj/moD3SRsZCUHNpXS6+R4KO1cbb6zdDxfKaIK3oNPP+VxQobuMhWuNnmIqGLRxm+452wQrWp4iO0QBfK51qxjoKLUfLzLgvQULianONBsSPuQgSCXsdG2S9AWmJU5E2vge+YNmSyPoJqUhwX4PcTVIC934XcidbhzJsWWJhg8wrYr0owScv683/wQ26AxK6N5sx23vLwrcUFZl72owsFwfgu5YFXRpsULB1bfoIkbhNSHs/AH9OayJhtXZg7taZtl8gl5nZ6Hk4Gwm9bRBJOQo9my+g+T4c+e8/iF9gzt4H5rwjkQXuODPm6CB/GwfzK8p6ZYmWfRbts27Pi7YwBVEzcC+WIMxCBLsasZ9tO3+27+DYbPuPUGzEY4E2y/BjQzLQvI0y93CTuS97fjL+82OvZVNC/9mc8ziyJmxGbm222v1S9Mw1QwTGEkD7bKWheb0TKQtS0DMzGD27PfFTHV+KnoOXkFvdBBQsXhbNmjWLSNXetGnThOniHXZvNG7cmGeeeYr58+fz7rvvMm7cD8yd+y55eR6eB6EQZGSEOPjglhx//Mmcc845uyT7k8OeB0csHByqGMnJyUgovhEJA1aIKC8YeSsSImxxLiuEzEeb/SDzma0uDJFC9TLKulxcioQo6+KzAAlWY5FAuATVszgCCSvFSPC0wc6JgpHtcSvQ2FSZ0S5YVuOcgi/UNzPXfxQJpaciUtMNCfZTUGBwIySw5iFB2gpS0Qi6tuSa65eY87ORILgaWWjeQoJrNn560wKkBd5o7msD+i6ykbBay5yzFGni0/AFd1sszloAgi5ZpYEX+L79G/Hdxixam/cl+OlG7fznmPHYwO5NZix2rMNRxqAM9HzMQ64u3yAScwQKxA6hZ+R50+cRiFSswK+svtic/y36Lg5C1rJ2iASuQm5V6eYerjN9DMHPfvQaCja2VrfgdzYICc9DkEsOgTmz6IZIzfX4Lk3xYF3dGsY4lo4IhyUexYhgHm+Ox/tNRmeUCo7N1vJohMjRhqhzM5AlJ5iOONYza8kf6PnbjIhmCH0Xg809PYueh5PMfVpLYnDsNpbDWsM8RGQGIMLwKiKgw9Fvqanp5y5E8nqhtWIg0D9stfCDurPQ77p+eNxLlmx2qWv3ArRu3Zpbb72VW29VEpwlS5ZQWFhIWloaTZs23ekVtR32fDhi4eBQxVCdimZIOzsLaUbrU37Q9gb0k8wmUqB/FxGFM1Cg7Dp8AcTifaThrIE0lcfju0ZYS0EyyiTTB/m4f46CuW/Brxh9AdJOz0IuGOXB+tHbImgpyFJRhF+fwloRkky/zZDAOwwJtGnIgvI1EnL+DwWYd0NWnyxkBVkamJ9gvIcV8G26zmIkmDVFQl4eEqQ7mvZf4hdeA2n7bZEya6WwBG6rOf66GXMKvlDfCL9wXhGxs0tZWNJhU47WxbdKWAQLvFnYMdqq0lno+2+N705VgAJ2X4jqryGKh/gLfiYkUKrYeei5+g65xNk4CEsCWiELzTmBczeiZ6wOIgbB5/lM9H09hJ7R19F3MACRy1L8ea2HyMj/mTHGErytZeQLVBQwmgBY4rbGzIsltLGE24aI0K5EGa3WIUEaIl3ZggTIZuOKFqo89JuZi35zIdM2Df/ZWYS+j774MS7R4wbfOoS5h2T8hAB2HDaF7aPIYpmML9zHgo3hslZSG4fzESIYDdBvoQ8iMrOAMchN63WknGgOrDWpaOtQtsK3TY89m2CFb5e6ds9HZmYm7du339XDcNjD4YiFg0MAHTt2ZNasWeH/O3TowMyZMyt0bqR2bz0S6IIWhZbIVcT6sEcjj7Ia+RByDToRCS9Wcx3E/yH3k17I1aEavk8+SADMMH1bQSoNCWztUVDu20jYvQO5sdgA24rEEFih2WpLi5HQvBkJWfXw3YxGm/fBZi7OQGSipjmnCSJGvZAryX3mWHMkQK8387Q+xjjs/NQksj7AY0hwewBl2JqFBKvF5nixmbPmRFoitiLy9RQSog5GmbDSkHVjMxLUauAL2dZKYREy47bkpRgJutGkAsq6idn+gp9VN/3YwPaaSNB8AX0PBaZtazPOeeh5jEYbRCjvR+5y95l+Ms08VCdSSPfQ/M1BVd1jkeQm+KTlFhSTshZ918EMWiFERHKRj/8KfFchkLCdiQTZ55D7WpDwgeZ4Bfpe6xMZ0xONVPQ9zUMC+pHmHJsiNhoeIqnJRG6RpSie5HXkmnQQflKA6mYMIfR7qoWsLdGEKdrty/ZrrWM26DwYczIQJVV43PydGtVHdJ92rmxBy2AczlWIYL2Pnvl3kAvew/hJHVqg7+IgZHXtTezv26Ztvxs9E48DM12MhoPDPg5HLBz2eSQy98+ataxcc396ejqFhR7S7jVHLindkHWgGr5ryq9IYB9B2aw7NvNLnUDPNrZiEXJTABGEdfiC1V9RjMS1SDuaSJO/AQkvQRec2kjYqIPIxbeoaNsXSBNpK5tGu7NEw8ZleOZeSpCgtQxfSPsWCbHnIdLVGrmUtEfuP1agyTJz83ck7Nxm7ifd/G+vYV0+7LVtnENQ+HwRuYD8BQn1LyNScjciYyfhFyrcaNoECxUuQULpx4gYdkTC8jH4VZRT8bNmZVC26NlG8391c4/x3Hrmm/cW+OQkKGBi+m+AvnubwjSEUtTGIhDBtsEg+FJz/6OQkHwcvjtNrDoQVqC+ELlPJUIrZOUYg1xwfkLf5cmmb9uvfe6GIPJ8Dfrd2PiTAUg4H4LIiSVteYhYFSHSau8rkTBbA1lopiMrzir8OKbguZa85BIZ1J2Ln6r5FES+C9BvyhLpDPP+G5qvTCJ/M9HjC85vgTk31m8sE/2+b0ek4IA4fYYC76n4ljSb4jkPPSMDUW2bxei3+ARwNH5Sh5fRc/AavnteIqSjQPD4qWuPP/54vvnmm/D/xx13HOPGjatA3w4ODnsaHLFw2GchQpHK9pj71UcOSg95IwpmXYM0wrayroc2d+uC9CVyA/oz0tT2xXd/iRY6bbEwu8FbzeE7SBO/FBGK2yhr6QAJuzWQEGVjD2zmplr4AaHno+XgMRSDcBDSOB9D2aw4iRDUmNpg2iwzFyORcPmYaVeI3J9ORH7vJ+EL5Rcht5WnkGB+N5GBvknErsdgj+chDeooJKBejsjKPGQh+QNp11/EJy1We11ixrw/0uR2D/SfiUjHheg7zUXCniV70WOx339t5JqVCLMQ0QzOd5Ak2fuubca4GgnkzYhNKux1i/EF8QaI7DyOtNb9zP3Z56CYyJoeQYH6OCSEJkKp6fs/+K5kU5HQ2tpcq4P5Ox0RkCtQ8PBfEOk9GdUOaYmE1RHoGT4HuaHZ76cpPskM1oSIngMPkajPzbU+QYL1bab/6N/cRjMHzZDA/xEiN/PQc/RA4Fqr8QvQ2SxcNZErYiJSEfwsWLcjHnnvjn6vY1HmrvJgf4dFiOh5aC3ZD/0ur0K/iz8jIn0PIhSDUQD3NESQKkIsLLLR7+J7YGNChc033/zi4jMcHPZSOGLhsE+iLCGovLlfqI+sCXeijXWF+dwG8RJ4twHOPZGQNgS5oqxDsQ32vCCii4UlIe3jKDPeQyhLKuy1Iu7Y3G82kZmbqptjNRDJ+R5pY+9Fgtzjpn+rzU5ktYCy2WlqIjePV5Cm/9lAH2PNvTc07+cG7tFDQv3vSOj8EdVqOImyy1bwXm0WpKFIm3w7Im5PopS2f0bCIeg7BQmu1hc+GZGa/yN2AbwDEMm8Cz8uIQdfk24Jok0PmoLmO5fYNQeCwuWXiMhFE0Trx4+5prWOlSDXri1IWLZxMtHffX0zjsXI8vIWIhfWHW0TkXNabK73Kfr+5yCB8YkY8xFELnpu/gOsiiLfG8w9/oCe32hNewukNQ+h+IUvTZt1yILxICLE15m2NnZoK5rzWM+mh08w30cVxM9DbkFrkTD9NJFkJ8Xc70IzT9Zq1QgRjEOJjO/YasYTvOaxaH5rljNfdg5snEXwt2+vYdukIAI+rYJ9gp80wn63hYFj3ZB19VMiizM2Qb/7GaimSldkFUqE29Hvy5KZNPy6IR56/jqafg8gnsImJcWjqKgIh50Dz/NYt24dderUKb+xwdq1a6ldu7ar1O6QEI5YOOxziKyGbQlBeYg297+KNshByLXAwrrjlBKJ6IU4OutLDtp8g+dZ4Rx8wfJ9ZK04Hwlo11I2ZWciJBGZuckGHGfgZ5EZjISdG5HPdSM0V8XEznxkXVRsgK7N4mOtCi+aMf8daaFBAt8wJLi8i1zDgtpRO1/vI2HwM+Qq0wZfEGyDX+BtPn62q3XIXec2NK+DkFvH0YhwRKMDEgIPQ4L3f/ErVUcTzlsQ8fgQxcoE3bKSEFGL1oDXxE95WiPqmJ3DL5CgeiFlv8skJKimBtqDhMwSM+7BiKhGWwSCc/M5Etq3ItevGchVrTW+dWE2vhvYYvRsvYuE5XjYimJnRLohN0ID7WcX+gXFqdyGrIKlZq5iWVzWo2fmJWRB6II06b+hZ/Rkc39p+JXTg/NeQNkK4r0Rkd+KnqtCRConm3v0TNtcM7YcJAg/ip9FKkgqNhKZZniL+Xx/814rxlwFv7+ghStE2TUjiGL0vX5A2dTCFrHWAPvsgD8/1vWqB8q+dgua/974KXiro2egoznWwbQ7MdD3UET2s5Al5E+IZNVFa6qNB/oVfRcPIULzD2IpbIqLXXzGzoLnebz//vt88sYb9LrkEs4444xyzxk9ejQfvvYap154IX369HHkwiEuHLFw2KegxbAeEpqvInEay1jIRsJJdZS6MTq4OQdpOLfiC0tB/+1oQaIfsh48bvptjDZ3i6b4xcJqI81gX/TTrY0EyVh+1kF4Ue8h/MxNa8z/1iLS3fT7NhLIrZVhBRLosvA1rEGE8K0aVvO62dzXi6guxZ8D9/04EswOQNrsQXH6DCHN8c1ovucgQendGG2bm/GfhzSvH+Jr3M8jNqnA9FeC3LKKkWA5BWm4H0Q+9dZFrh4iB48jIcsKYnasJUR+z6C5DaY8ja55kYc054chAS0aSUT6/NvYm2FI4H/K3OM7SEh+J0YfLZAg+QL63t9AZGA2kTE5BWYO8vAtRzdEzYFNaWu1zp+iWJNckpJKKCkpKxj65GIK+i7bIAtLB/wg41gEsRuyMD2P4n3modowQwLnt0C/hxIzjlmUrSDeCLlDvY+ITX30PdyCH+j8AHpWT0LPSl3K/raC9VFqmXfbZomZw8amPxtgH09QttnS7PNQSGzCYC2A7fBdmiqauScJPw7Huo5tMn0egqwwc5AL55fIEtca/XZtgoIZiJRegObxJfP3SmTBuRGRkmXoOa1j2tl1IOgCOhQR8+uRcqT8+AyHqkWYVDzzDJ3y8vhw2DCAhORi9OjRfDh0KJ02b+aTZ54BcOTCIS4csXDY6+EvftWQ4H8ucBraCJeYY7YGQ0XMwivQxuohYfcE5FIFvnbbWhiiMwQV4xdls+gPTERC3z+Q8N4AkRibpWcW0mrXQoLeX5Bga7XYiRDLRSRkrrHBjMcKN2lIOP/B/H8DmpMnUWaa/khbnEGkQB2E1RYPRcJHOkpdWmzu+z9IwDscCYC34wexx8IW/KDoIqR1b4KsHa2Qn35zM445SIC0Gvc6KLbj6Dh9e0gLrgD3oECj52YtEqBT8OMwCtAz8ACKP7CB4zYrlvX5DyKY8jRYFK8UxZysRRmQol2kQMJaEZHabkvMLCFrh+8SZ7NxFZqx2SxPQdyKBD0bsyA0b96cP/74owJzEE1CyveTj6yLUIq+13gk6ARkEWqJCFAu+n38GQn/H6A4iRG2d/OejATjHoHz8xFpt+5QZ+FbmUKm70eQpeoMFGsQngF8i9xm/MrnjcxcbAhcvxDNp11LVuMHzRNoB5HVxEvQ9xTLfcquF6n4xLKQiiOEnu9maC0pNfeQhebJ3t+xqCL7n0y7DWh93B9Z0e7Cj+s6Aq1DfzGfZ6N1NBc95zYoPvg8pKFaNdYF9GFE/Pqb49noO2gIPOzIxQ5CkFScm5pKj06d+GTRIkYlIBeWVPRNSuLUTp34YskS3nbkwiEBHLFw2GtRNjg7GRGLu5GgFczck4sEUSuMWi1uDmVdLDYjwfYWJGzfj3LuW6SY/hoRKSgW4gvjKfguRRnIz/kOJGyloUJ2qWbc3ZBwlY82clDw8SWUTyoiZiQwFjuOmvjCkkUHZBHIQ0LGRcj3/x4zxieQ4NaR8t2RbjTzVM3c/xAk6DfHJypnxhlvASJTTyP/7yz0/XyEhJBPKOsiVGSu1RqRvlPj9B10YZoJFJcRZIL/H3TQQfzyyy9RfbyGngPrCpeKTy6iff7TkMC13LSrR2TWqqCbGIFzrYbcCvJLkYXgfeTeE4uQVUffYTyUoliImUBJQgEueOzII49k0qRJ4f+POOIIvvvuuwTXid1famoqxcXL8Qu1dUP33gY970mIyLxK0BqieX0VWT0GIjfGAkQ0C9HvMgsJvTZ17hgUrJ+L5quPGYklgu8j0jwTuVsNQc9UkDzlExk0bl0JN5i+7Pow2txTNXwysZrYleOLo/7PMtew7eyzbGMkbCrh4PUqgiL8xAigZ8mmPN6MX3G+jbnmYmQ1swqSWea+JqPvYSWyaF6DYiZWIre1deaeLamA2Jm6soisp1EHWRMtBiLS/gKpqaku5qIKUYZUNFUyiVObN4c45CKCVDRvDqDzdiNy0bJlS/744w8WLFhAy5Ytd9k4djT2pPt0xMJhr0TZ4OxOSBC/mchgROvaUwdt7GuRexBog4+2aGw1n9tUkgOQpvgHfL/hxmiD3ogE92L8glXBtKjBxbgHEnDGmzHabENzkLb9aSQsH49IhUfsWhgVgd3wS5CmcBUSnqzrVmtzfBHSWIIEvpeR9vM/qFjcCCIF6Gh3pDaIRHhI+Pun6bMEBYk3NPc4ktgxAV8iwawjEtY2muOn46fBXY6sPVtMm8OQ0GJds+y8R8eDWI1xIbJu5CWcsZ9//rnsLIZCyG98GX6sTprpM9rnHyTwFpt72ogyLY1EgeMXxbiqZ/reiuZ1MQoq/xwRkb4JxxwbwaxNuZXSCleWRMSDFRZ9a8hcKmoN8d2pYrmp1UfC/memz/Hmf2vNaW7OnUNkOuHaKDX0bPQ9XkakO2Iq+r3XRL8R+7zbwnhb0ZyONMctUa+HH7yfh185PpjcwKImEtA3mT5sG/DXi6BLUyzEskx+idazs/CLCdbFz2IFkRnRLHlZjuZ3FlobuyMr79OInN2KH5ezAd9NzFrJohHtHmjraTyJTywx47gDmEBx8ZQ49+lQWcQjFRaxyEUsUmGxu5ELh90Ljlg47HWIHZz9f0iAOCnQ0m6EQe19TST4bTB/ZxBp0SjFj08AafLrIjeKJuZVB2WVWYXvg281g0EtXjDmIQ25Nv0QGEsO2uCtVaMrvutEMC5iW2DdspLM2LYGjlltZSyXiwOQC9AWZFVZiOakFnJLqh24x/XmflYhQbgICWgHI23nROR+8SZlU3M2Mu0uQgLV5+Y6H+Bnj8K0OztqjNYSUUx82ADzjxDZqTx8154XUGDxQORrnoRPaKJjcLKRFv5pNHeX4buDRPSOBK+1pp+vkPD6C3oGfkdCaEUSD1jEztq0q7AtdQ4iCUYiF60ifBe69fjFKq0loDoizTYwO4SIwSOIeFyNfo9Z+L+5oAUBc/1fULzKRrTWvIa+G5vooKbpYwWyNiXhp1+2606B6T8J/d6b4WdXKsBfJ2Yhgh8rDiMW8pC15kC0lqzGX+NS8IlKM/waKmko49yj5px7kdIjDRHgJsj9rpqZ2xIUc1Rg5mM+firrREjCL/z3fyiOyyLHHOvvXKKqAOWRCosguZg9ezazP/ssJqmwcOTCIR6ic1s6OOz2OPLIIwmFQuHXkUceGT7mk4pBSEiwgtcP+NWrQUJz0LUgAwmBqcjiUB8JCyHz/35oA05CQkyyea+ONt7f0cb9I7Io2Gwsq0yfaZRNNWphN84O5twt+NmVmph7KDDXX4cvWGxl22GJkc3cFEyHauclOv2uFcys0NwG3XtXJODnIrIx37z/gdyV1qI5PAulnX0TEYshSGs8DrlFPGnex5nPhyBt8+NIiM5Dblibyrm3ZHNPtr5AWuAV/NymIq2c5j4InRcM9j4SuYy9g565aciN5A1ksTkGuYZtRs/jWFRl/EHkljMdZdH5Es3Ty0ig62+ukYcfb3AKCrK331c8bDXteprzdj2piMa4cePwPC/8Kq94mt92Mwcf3Ar9zlYAqzj44FZ4Xp65x63oWe2Hgtz/jdye5qPg/mPxLVl90Ty1Q7Ez56I15AP0nSxARGIk+o4vRUJxK/Rd9UVxQ+PwrVbgrynV8V0pVyJiuQAR22X4FraN+OtFCL/Q5Vik3a/Id2ctU2vNvW9CFpTa+AqOWciKk4lPLN5HrqI9EEnqbvr6Ba1tA/CttUHlRC1kdbTKkNUVGGN1098PyBIaxBlmbA7bi3Xr1vHJG2/QKS8vLqmwOLV5c/qGQsx+++2EpMKiR9OmdMrL45M33mDdunVVOexyMXz4cEKhUDgmrFWrVhFywddffw3Ae++9x1VXXUWnTp2oVasWGRkZtGrViiuuuILff/89Zt/9+vUjFAoxfPhwZsyYwfnnn0+jRo1ITk7m7rvvDrdbu3Yt119/Pc2bNyc9PZ0WLVpw4403smHDhog+YuHLL7/krLPOolGjRqSlpVG/fn3OPPPMMpbhit7n7gRnsXDYI5Co2NKkSb8Fii3loA1/YODsPCKrV1sNobUigDbILfiZWmriZ3bJQhu9fa+GH5OQhtysRiHLhXWnslrOXLQRR+eCj+V/bF2QbNYXe/wNM5bGpr9s/ExRnU2bygiKwYBQ0JwtQ1aa2kRqMqPHG7QEWHeRRubdWoCsi8WH5l6eQGlx46E2Esij4ccCZGWlkZ+fT2npDJQ95pFy7tEiidj6E7/vpKRYWa4qjvK16JaMZSPy+Bh++tJ5KJ7FpjzNQ4JnARKKS4kVHJ3YJSh+1qZgcce9BdOmTYt7TFalNJTxaxCRFh773Vj3uBB6lp9CvwH7vbxHZIrYIvQ9V0OZuQ5Cc74FkcS3ELk4yZxnFQylSFiuhW8BKTV9bUFEfSN+HE5dfCvMV8jyYq1zwe8wFPWZhzI3jUIZtZog8lOKv+XbBAt2jNPN8f+h+InL8clDCBHluihxQ/S1bJ/JaI2yqaxTiAxGj37uQvhZ6N5F7lWY8zLQMz0bh+1DnTp16HXJJXw4dCifLFpULlk4tXlzjmnYkOy08mN5Plm0iBnZ2fS65JJK1cOoCrRt25bLLruMd955h7y8PM4++2yqV/eTVDRs2BCA8847j/T0dDp06ED37t0pLi5mxowZvPLKK4wYMYLPPvuMo446KuY1Jk6cyNVXX02jRo047rjjyM/PJztba8jy5cs59thjmTdvHrVr16ZXr16Ulpbyn//8hzFjxnDAAQfEHfstt9zCY489RlJSEl26dOHYY49l0aJFvP/++4wePZoXX3yRyy+/vFL3uTvBEQuH3RrJycmUliZTserYQ82xQfgEAeTC5CHBvRBfqM5HWvV84ldNLjXnt8UXmKOzAFlCsNmMaynyZ85EQkQwgDOYajRaILAWAqvptBr6N82xbLQJbzB9zcIPRo0eezwEsxbZsaTjZ4upQaQm06LYnGutGkEfdItkfNevjfhFsxKRiniIjAXYvDkovP8HCTAD455dmb5jpUfdFgQF9s6dOzNjxgzzXxKa11rILWUx0tJaAtAYPV9zkNZdgcLt27ePq1GruEtQxbM27a1ITYWioniENDr+xr5aolgn0BqzBD9+4FkURJ6OtPvHmPZNzfFG6HfbHp/E56Dv2QprNjUz+ClhN+HHZNk1oy5aV4Yi8t2K2ETCwq4Zo5Cb3YnmHEui7DW/RNbPs8x1P0HE8xKkgIkm25MRCQm69tnrJkX9b11FV+IrgmLBxpl0N/1bhYWNLzkASOH4448v14LlkBg2IHvU0KFQAXJRUVIxyvPode21FaqDUdU45phjOOaYY/j666/Jy8vj0UcfjRnU/N///pdevXqRlZUV/szzPJ599lmuvfZa/vrXvzJ9+vSYblwvvvgigwYN4v777ycpKVJBde211zJv3jxOOOEE3n//fXJycgDYsGEDZ5xxBqNGjYo57hdffJHHHnuMtm3b8u6773LggQeGj33zzTf06tWLq6++mmOOOYZ27dpV+D53Jzhi4bDbonLVsT9DbjYD0WZWgG+RsLECafgC8kLznoqEdeszbIWyYLYom8vdZuApxa+AXIy/cRbgB3YnIatKTXN8FZEBnMFFygo2BYH/P8WvHv1X5BaUZvpLR5vu52ZeMohtAQmiBH/DTiHypx/Cr2L8B9JkdsdPi2sDnT1z7fKWDQ9lypqJhJGqiwXw4xqig6a3v++qxPTp0yP+15jXU9UEINi2TZs2zJ8/P/x/69atmTdv3rbdwF6EwsLCChBSSzCg7O+oOvq9WUL6pvn8ZPS7noSsc6X4a8dmRKz/aj7LN5/VpKwFLbrOSS0zhjXotzPcHHsuarwRd0nZgoCd8N3/bNIJ8GugHI6skp8hUnsIWk+sq5RdUzYhK0p0pjGraInlV9/AXGcFkZbPWOiALCKF6LdsLUhtgXS++Wa8i7WoAlSWXCTCriYVlcH5559f5rNQKMQ111zD66+/znfffcevv/5Khw5lM+m1b9+e++67rwyp+OOPPxg1ahRJSUk8++yzYVIBULNmTZ599lkOPPDAMs9saWlp2JXqzTffjCAVoNiyO+64g7///e88//zzPProo9t627sUjlg47JaofHXsh5HGsDfaTK3LTjD7yia0QW9Egl1zpEmOtTFa7UYpEu5XIaE7hF/IKhUJ3ZtNW6v5X2OuWcN8XgsJDMuRNSPF3E81IouTTUfa56uQAHwIEmSykKC+0lw/E/ndf46EglPwXX6iNbBW2LEWmFiaKGuJyEHpORcgS0gwsN3Gn5SHSGuANKOnELuKdTQSV3C2iB80vf197ygkSl174IEHxsw6VVk4EhEflSOksdaDICG18QNv4JNE0O/IWkS3ImG5Haq9sQy5o61Av7NoRUYGWpeW4leuzkZuWa+a9s8iJUpr0z6YQe0rIgsChvBTXtu1wFotHkdr2lNI+H/M9H8jkZXh7Tqy2PzfOnDMEmKrtAlmlgJfsbLUtLGKmFiw/S5EAfU2KUUOmt+2wApHLqoAVUEu9iRSYTF37lzGjBnD3Llz2bRpEyUlssitXLkSgN9//z0msejbty/JycllPv/222/xPI/DDjuM/fffv8zxTp06xVzXp02bxrJly2jTpg2HHXZYzLGecMIJgNyw9lQ4YuGw2yEyALui7i6zgYvxN/kgubCb64+otkJ0ZdhYsBtYCGkZs5FgvwY/u4oVuBeY9rVREPevKKaiBb5rUCoiMgVIc51n3kEb9+fAx0gYSEFk4neUFakO0nj+gIKei5HrxVFIuD4eP0NLNKw1wtbNsEJGEMEg9uHmPl9GaR9rUHGUtQZUr16dvLzKxwJkZaWF3Z9iwfM84yZX+b7jVYfeWagKEuFQeewIQnrYYYcxderU8P+HHnooP/74IxCdjvh8tAYkIwIQy/UStCako9//4yiY+nrgJxR4/jYSuMFXIrREwdbnILcnu0bZehvBZ93GXvwDpXt+FP0u2hKZMS84JpskIhk/dW5BoI29DzvumviumynmnoL1LaJh599aSPPRuplvPn8IZZBzVbmrAttDLvY0UlFSUsKAAQN4/vnnEz43ubm5MT+P53K0ZMmShMftsei13lqU582bV24GrdWrK5IAYfeEIxYOuxWkHYgVgJ0IK9Em1Cnq8xR8i8VmpNnri0hLZRBCm2oT874Kbd51kBXjJbSRn44f3GljGFogLePZ+LUabLDVEiQQf4+sGqeZezgUbaxbkUbyZ6TpHIG0fzcjYeI2VBX3aeT6AJEVvWO5W0XDujp4yN3qdzRXL6LNfPuEr82bZc1JSUmhpKT8WICUlJQKF8WyWicXZ+BQUXieR1paGkVFlSekqalQWBj5/FgSEe9aPpEZjxQfp6EMc0XombSxYLY+xmIk+P8P/daDRf2eQmvNUYio1ELrgI2FKkVW0Q1IsWFdqjz81LPvo9S7p6M6KqORpbcH8S2SuciaMg+tpdn4RUQtCvFdR9chC21D0zZxjRifpNjMfEXoO5hj/j4GFdF0VbmrCmeccQazZ89m1NtvVzhQe1NhIaPWr6f9uefuEaQC4Mknn+S5556jYcOGDBkyhKOOOooGDRqQkSGr2IUXXsj//ve/uM9TtWqxYgl9JCIHsY6Vlmp/btiwIaecckrCvuvWrZvw+O4MRywcdisoULsTclWoKL5HG1K7GMdSkaViKdL419rOETZAm90vKJPJJCQQXIjcEFogQlGKTPuzUPXoEfhuCo2R8DAEaffuQylbVyHyEUxN2wkJPr3Nfb6DSNdNiKzchNykGgFXEt8KE28j3mTuZxSqxZEbEIiqLutQcbFfU+Lss8/mvffeC/9/1lln8e6778YZX/mo6urQDns3CgvlvrMzCKn/W/oRkfYXkVBvXZqCRSFnoLViGfot7YcvaKciy0UdFLsxB/02W+IrB2xhvkb461whsoYONX3ehiycjyJScQWKTSvrBiKMQm6mBWYu2hIZrG0tL9WQdbNBYCzz8VPsBpNpRMPGBbXAVwYVI9fQ6mi9+QWlBp4JvOPIxXZi9OjRqlNRq1aFSAUooLtvrVqM+vxzRrdvv0eQixEjRgDw/PPP07t37zLH58yZs039NmmieMuFCxfGbRPrWLNmijeqU6dO3DS0ewMcsXDYbaANuA7y9a1MQO4W/NiDWJvN1ch3twD5/PcsbyTmPVZfIeA7tDHXQ7UJ2qJNMQc/53wGygDUG1kYvkQBk39G6SknIU3kTaat3YRj+SLbOhjdETkZiYT9DYhMrEP+18tNfxUtoGXJzyvIX3x1eLPekVmHtodElAdHIhwqiuDz2r17d7766qvw/yeeeCJjx46t0uv4AfwLkOBfi0hhuzkqCvkYsmDcg4jAY8hNaX9EGq5DLlG3oPXySERS2iOr6iKUXtdWr1+D0lL/H/q9X4KskrejoPSXiYyfsHgZrSt9UNamPyhLDqK1sjbhRnVkSV6Pn1o3PUZ78Av/2bi2VLSmf2rGeUygrYd1+7QaYUcwKodEFbXLQ6wK3bsaaYYYBZVXFra2RosWLcocmzlzJj/99NM2XfPYY48lFArx448/Mnv2bNq3bx9xfNasWTFdXrt27UrdunWZNWsWM2fOpGPHjhW+ZqL73N2QyMncwWEXoAUSxisDG8ewJcaxr9EG9w/gaOQOEKtdLAQzxVi8jFJW9kLxCJ2R1SGNSDek4GaXisjMW8BxKC9+JxTDkGnGV4LvIhV9fhLalG2w91XA39CmPwppHW9HgdznowDsWO5EQXeoQpTdpj9yj9gYN1DaFiI79NA2BAuRHXpoGzxvc7iNg8OeirFjx0YU6KsqUhGE/zux7kIAF6A1ZRxSGFyOXDWbIzeo11Bc2AeovsSFyOo5FRH+6cgKcidaB/oA5yGFx+dI6fE3ZCXtj9ac5mgt6o0ffG0zy1mMQuvL1eaco1DWKZvQIRiDFuu3n4RIUD389NOYv21w+xb8BA9dAn0VojVsEVo370ZxW2+b+XgcrYGHAHUIhdLCri0OibE9pMLCFtH7cNgwRo8eXcUjrDyamqJ/M2fOLHPM1pIYNmxY2A0JVIPi0ksv3WYhvWXLlpxxxhmUlpbSv39/Nm3yi7Zu3LiR/v37x9wTU1NTueuuu/A8jzPPPJPx48eXaVNSUsLYsWMjLO/l3efuBmexcNiNkIXqVCTy5w/CFmQ7CAnSc9AGFcSjyIe4F9oc/4xckAYTH8GAxODiMAp/s70CxUjko43aLlrROd2DG28q0hbWQkL9KER2NqBNOI34VpIstAnXRUSkDxLwH0NuVH2Re8VDSBB4HOWw70Cky8U8FFz+ETAXzd0mfAEjPhL5kzs4OFQMvnvUTPR7fQPf1TAHuW02QnEZq5FLYgmqRn0iUjBkAofhF96cjNLR/opISzoS5r83r2L0O38OpclNxk8fDRL07dqzDK2RfZBF1EOB4W8T2+Jr17hY1og6pu81Zgz5RK413yIXMJvRro+5p6fN+9/NvAT77ozW+esRwXqcgoKZzj2qHFQFqbDYnSwXZ599Nl999RUXX3wxf/rTn6hVS26At956K//85z8ZM2YML774Il999RWHHnooubm5jBs3jtatW3PmmWcycuTIbbrus88+yy+//MLYsWNp1aoVxx9/PJ7nMW7cOOrUqUPv3r354IMPwpYGiwEDBrBo0SIeeeQRjj32WDp27Ejbtm2pVq0aK1as4KeffmLDhg08++yzHHHEERW6z/32228bZ2/HwBELh90CRx55JBK8K2oatNWzQ4g4VEMbFEQK87OQVjANae2iYxISBTUH/47ebNegjbsEaQ5t9iS7AZbib94WK8xnN6IN9iHz6kBkhdpYY6qJBIYQIiZrzH1NMn08jdwgniaymvN7UX3Z9LNrUOG6/ZEm08HBYWchvquhTT9dC60vDdDv/BwUTxHLigoStF8yf0cX9GuMCEcfcy2r/LBrZwi5YtrkFw+h9eamQP9tkOVkGLK6Bt0tQ8SvaWGz5eWZa9dDypE0tAaONGM/GllE3jF9/IGsFDYVdrDvJETANgHnIqLzL1zWqPioLKnYVFhYbuzF7kIurMXg9ddf5+OPP2brVmUxu/jiiznhhBOYMmUKgwcPZvLkyXzwwQc0a9aM6667jsGDB3Pddddt83UbN27MDz/8wD333MP777/Phx9+SIMGDbjgggv417/+xbnnngvEDsJ++OGH6du3L8888wzjx49nzJgxpKWl0ahRI0444QR69erFWWedVeH73N2IRchzv8JykZubS40aNdi4cWNEIZR9CTu6+JafYnY00rwnwla0kdkCeCAt3AaURtIuiGsQUXkCuQjZDTXoO1yRmAQPuAH5J7+BNrQN5rwN+BmgGqENz7oLBM3zNkCzCbI+bESpZFuYsQQFhmhXA4tFSGhoYfpbae73MbRBx1pc8lCmmS1IczkSbeg3oGDIM4EpeF75FgsHB4cdB79aezLSyp+KlAMfEelqGU0uglt4LNJRgoKfT0YWyvfx17wQEs4PQy5W89Faea+5frDvpeZYT8pafO2aFT02m/K7AMWAWeuphxQ8nyLrbWPT9j20Xq9E7p1dUByIJVwW65GyZz98/ehjKNB8xxW+3FnYunUrCxYsoFWrVtvt5rV27Vr+2b8/nZYu5bpO0ZkTy+KTRYsYtX49fWvVqhAJeXrGDGY0acIDzz5LnTp1tmusews2bNhA69at2bhxIytXrtxtMzxV5jmrjBzsYiwc4iIUCplXdebPX4cE/4ZAfebPX0coVD3cpmpgq1Yngs0wYgOarXb/VqSlC/p8TiEyW5TdbCoak2AxH6Ve7YesDrbwVD1z/XREdpYh96J1po3N956PNlVboK4IBTdej7LFLKCscBDLFSvTXGc90mi2RhleqiE3iQeR0DAdaUCnIz/rkWZ+njNz8SZKe/kIcseoWHpXBweHHYfp06fjeR6pqUlozVmF1pIvTYtYa0J5pMJDa88itGasRpbXYPtuqMBeEbJ01savaRHsswlSxIzCt44Er23XYvsqRaQmBVl0bU2LEvx6GjbT1VZEJjqgZBKXo0xWo5AiJ/pegy5cFgNRxrwcF3MRQO3atTn1wguZkZXFF6b+QjzYOhXtzz2XUaWlfLJoUcL2XyxZwoysLE698EJq165dlcPeI/DDDz+U+Wz16tVcdtllrF+/nl69eu22pGJHwrlCOZSBiEIqWvBboLgHm2o0E2m/barRz4A/CIUkMG+fpqiUxIHVJfgblbVU2OudbMb7hBlvdSKzRUVvun2peEzC84gUtMWvQ2FN+aDNLRPN1QZEKDbhF9ADbdq18a0bIbR5P4E281so66ccvVGvNeduNNevZ8bUF5GHqZR1fQJpAw9BFpJDzFiewFbH3tO1ew4OexMKCwvNGjwarbnW/chmTbJCe3l6QQ+Rk7XIeloNWWlHIsXIVabd2Sgd9pcoJfeJxK9pcTYiO88hJUvQ4htNfOx7ijmWjazILyEFyN9Q7NsmM8aCwL3djNbZYcgd7Goi18dYJCsJreUTKCiYEn9a9jGEQiH69OkDwNvPPANLltDDBAIHEV38bnT79gmL6H2xZAlvFxVx6jXX0KdPnypUMO45OPzww2natCkHHHAAderUYenSpUybNo3NmzfTvHlzhg4duquHuEvgiIVDBLQ45CB/2xuJXxzNuivdjTKWPA5sewDdEUccwaRJvyHCEu0KtRFZAjahDXJ//EfXbkTFaLM7G9WFeAhtxDZblPUDDi5+jVHRqfnEjkmwG9lalI1lf3w3K3tdzLVboHmyFWY3I1JSDRGARYjsBH9y6WgTnxx1v9HzZ2M8tiKLwxakeSwy1zsQfQfDTXvrX22JTTGyNtVDlhT5IwfTyzo4OOw+8AO8i5G2/zGU+SkYfxVcz6KFuhJkqViL1nMbHH2Y+T9IDNqglLVPI6vBJXYUUX3aa19m+ngKxXgNQEqS1Ki2RfjkpxAFo1tryU1o7bNrlYfW1hy0ds5FLlfLkevqyWac4RmKc985yHLRn6SkpIhMQPsyyiMXsSpqJ6rQ7UiFMHjwYL788kt+/vln1q9fT1paGm3atKFXr17cdNNN+6xrmCMWDmH4cQ6Xok2sIrUk0qmKALrvvvuOUKg6soKAqk3/F/gJbYDRaIhyvl+ABP5klJ2pF6oo2xhp6IPZouJlL2mNXIXAD3zcYvqsj9yMulG2xoSN9Uil7FxloyDFtfgbbjRB85CF5D1zvVhuYCF8rWMyEjTqoJ/uSmQdqW/GsQT5HLdDBGIF0gI2NX2/iwSUyOrYDg4Oux98cpGPrKY1kRCfRKQlM7iulSJFzEr026+Dfv8bkWJmIVrPWiFlkCUGN6MA8Vxi17QArT3F5lpno4DrB1G8xRBUZ+cAc36yGfcSFDg+FpumWpn6DkZrVzZy0Uo1Y96A1s1CtOadbcZ4D1KG2PsMVuuOhtxCPW9tnPvYNxGPXMQiFRaxyIUjFT7uvfde7r333l09jN0Ojlg4AEFSMQhpfCqLbOSz3xB4OC656NatG5Mn+xr6rl27BvwU84APUUaQGWiDOQnfPaka2qzm4xd/GoM09jegzekeRCYeRlr+TCKzRcXLXmKRiTZd63I1H21msTbbfHOsZZy+GiHT/2rTVyzXhdamjyX4sSDBzFJW65iJNHpLTV+WuOQiv+VVyL3pcPM5aPPdjGIqPkNWk03AVkcqHBz2AHieR1paGkVFK5Aldi4qkFcbv/ZEKVJybEXrQSFaI5qhNWceqta9FiVyKAF6oCxPlhjURuvVcqTkiI6VKDV/2zixJERYhpr+RyDL6zuB9snm1RJZHA5D6WMPQAqQJLQ+fo2vWGlpPmuE1vHpyA1sOHLTOtzMzFa03scSYTJQ+t7Z5U/wPoZocvHrjBnMyM6OSSosguRi7owZiqlwpMIhARyxcAi4P13KtpGKIAYizdQLYXLhLz5ZaDOoj63ePHnyHGOpyDNtFpvjD6LNKFZth4NQXMGtKDj5aeSD2w/57b4A3IU0XAVIqL6LyGBviO0+YDVyqWhTDBaQCqIUbeIp+L7PQdigSRvcXYA2QxtjYWGtGNZtwG7kGxFZ2Io0eo3wY0w2ofmyYysw549AwY4p5v+QaZ9p+i7EkQoHhz0LhYX6nWsdfQVp8C9DQnZ0rJldu5KQImGSOedXc/wb4K9obWmDYhjmIkLwJVovlqD1pg6+C5UlCfGUIzfiK0jmIbfPBvjWUpCCJ2TGcidSDm0x47XrsnWLSkbrXgZ+7N35SMnUBWW0OyDBrHUEUujZsydjxoxJ0G7fQ5BcfPLGG/S65JJyU8Xa4x++9hqnXnihIxUOCeGIhQNa2DuhxX574QfQwXcmqLsiQeD/RUL0ucBfULrF6DoQULbg3GlIo/Uo8sVNQwTpAVSB9jxk/h+FUqsGC9jZvmJp5OyiaWMqgtlHPCT4FxLfbcC6L9VBm/SviADYvu0mba0eJSgoMp9IrWNrZJ2wPsugDddmpCo081cfBUT+GwkJ3ZF72mrzmXN/cnDYk+EraX5CltRmyPJwgHllofXjFyS0f47IRQHwDBLyLzDnd0auR41RAohByBJyLFJabEZrXn38GI14KMCvU1GELCG1YrSbhEjMLWbsF6L9oB1ay1eY4wuA75DlohWqsxPCz7j3JVrPD0Uxfq1iXKs9kM6nn36aYNz7Liy5OPbYYyscB3DGGWdw1FFHUbt2bUcqHBLCEYt9HFog6iCNU0ViKiqCHCTMVzQI/DXkP3wlyp2+xZwfS2iPJRinA7ehdIjPo02tL9p4Lgf+DwUanog0adGwgr7NXhJEdAEpSwJWEhlbESQpwaBJGyBXzRyz6WaLzeezzf/5SAiw7gep5r5K8EmQtaIU4Vskkk0fOWgDXo3mMANtuosQUdnejF0ODg67GpGF9dYh60ASWitkBfZTcmcgN6cbAz00Qhbe15ELZSp+9e4spABaCvwZCfrLkJIkG61h1g2qFCk1tph+rHKmMVp/g1mbPLTGP4ji4W5BcRA2scQGRCJsEHk2Wt++QK5WtyNl0zGIQN2CSNNQpDi6Hj/g3CIDl00/MUKhUKWDi/fVYGSHysERCwe0mfSuwv5sZqS/Un4Q+AIk9J+N4iSC2UwWEj9+IQibevEqJPA/ifx5xyIt/j0o5uJRlC3Kxll4+K5PJeYza/q3SEfatVkoMBz8oOh2gXbxgiYtspCA3wDfrcoDfkNxKU3MOdatqhBt6ssQWYi1seeba74L/I5IRhpyfSjGupc5QuHgsDeiHkpJm43WgI0og9TZxHcTegk4HVkwBiLSYLPLJSNFz1gUzN0MWS024BOIaFgrqiUk1u3JrjklaH0fjsjE9cjCUITWyQ2m35pmDJYMpCNr9PEo4cRQ0/9laK08HSmKHjWvNUS68drEGns23NrtsCOxo54vRyz2eWQhF6VY1oRtQXvzPohITVk83IOCjW8x/ycjgdxmPVqANq1Y2qdgnYck8xqIzOgXIeH8WmQJWYW0Zo0RgbGwWjP7igVLUm5Cm+A6tBFCpPtSkemvLWXjLmqatpuQdQHT/iugK8poFUQq2miLEEHII/bGPgERML/+x4EHHsjPP/8c514cHBz2dOy///789tsKZK28mLKVsOPhMBSL9h/8zHmrketTfZQZysZb9MSv22PjtQrxY702m3Pr4buMBtfpYuTi+ipS+vRAFpMC058lK/XMK5bba3UUH9cQkaEcRChsLaOr8NPnpqH1HjMvBZxyyikVnJfdC8nJisMrKiqiWrVqu3g0DnsrCgqUXS0lpWqpgCMW+zDatGmDFueOVdTjUEQG/ooE/Oh6ENGYhYq6PUhZQbwB2pjWIN/aBmgDCfYTrCVhs6OUII3d7fgB3SBLxQr8bFF34LtF2cDE6D5tv2eizXYE8k0uQdrBjfiBhhlIw1c9zr1mmGOrzHsSMvWvQYJBNKwLVBY+EbEbu53Prcg6sdlpthwc9iH8+uuvUem5K4OH0Dpt18K/I8WIzTh3EFrLj0EWiGB2qJB55ZpXsmkTtAKD1qoFKDj8TOSeaYO07Vq7Gilc6hLbxTWIv5ox/w+5pLZGa+FqFGM3F635dyDCVBso2GMDt1NTU0lPT2fjxo1kZ2e7mAaHKkdJSQnr1q0jKyuryolFyHMSSbnIzc2lRo0abNy4kZycnPJP2EPgp5gdTdmidNuCaiiuYQwyzwcfrVgL42BgommfFnXMbmTz0AaShJ9mNegSVIK0+cVIg1Zsjl2FcqX/HWnJLAYhDVobZIHohZ95ygrswbHmo/m5GW1YjyP3LJtpKZNIfp5oAyhEsRo55nUO2lRfj3HvNlg8nraqFPgHyoDlgrIdHPY1aP0+FK2h22JxHoRclOxaeBJaQ+eb/09Hawz469oW5KZaimIp1iBraxKynDZG7lgFwDVImTMUrX1NkYIlBWX/K0AEoaKxEJtRco9aZuztzGcrkSX4RuRamoUfaxLbJbRjx47MmjUr/H+HDh2YOXNbSNqOQ25uLkuXLqV69erUqFGD1NRURzActgue51FSUkJ+fj4bN26ktLSUZs2aVcgqVhk52Fks9nlY4Xh7MRgt6DdS8SDwKWgzi0Uq7NjqI01aEyTkR7sEechdKBMJ67XMeccBHyFrzLEob3oIBXKfizJH9UfZo05GaQzboo1vKyI0s1Cq2j/MdVNQNpIr8c3/FRXoPXN+fRRD8iTalIfGaFdk3uMJC6WI4PwHRyocHPZl/AF8gNa0yuIhc95VyIWoKXKL7YAULiOQ4uM8fAG9GK3zts7EarQepSOLxFa0Fs8GfkRJNQpMP+loDbT1dZpQuQDr6ihz1W0oKYV1f01D5GYgqvUxBO0Dv+Gv4YsIhZLNWMumPZ81a1lE2vPdYU21wtuaNWtYunTpLh6Nw96E5ORkMjMzqV+/Pmlp0fLX9sMRi30QnTt3ZsYMWzTOaqg6bWevTyDhvaJB4JuQgN2hnHY5SCO1GWnDQJtDEX4mkkVo09mEXI1KUSD0AhQ8Xow2u44oS9Q35v9maCN5DWnUrKneZjyx7yXIYtEKEYKaKAtJsxjjjeX2FSw2VQP5Aw9HsRUNotrZWI/oehcWucC9iFSs2i02QAcHh50PpZ9NQ2tvT7Ytq99hwDQU6/Uo8AZy8UxDipz7kIB+KVrvauMrPLaitdG6N1VD61MN008mWt9ro3Xc1tzYgG99rix6oGDur1Agd1NzvSS097yI0treidbXIuRqeiNyG2uAFEkHETvtuRRJmtddn0kvJyeHnJwcioqKKCkpKf8EB4dykJSUtMOtX45Y7CNIVKROC7R1ixpMbJ//aCwAPkXCvN0gKhIEbl2cxiPLwxx8V6wGMdrb4n2bA5/Z9IrgF4pbhTbD2miDOxJtOnea4zOQy1V/tJn8C+VRt5vdGqRhsxVgD0ZzNRJ41tzr1WhuXkak6G5EYOw4gzElwc/saxNwPyIFKchi0xPfJSuESIXN7hTEVjNPj+NqUjg4OAhFaG37F/DIdvRzIsrA9A+k+NhgPt+EFC+TkbXgfPx1zVqOS9CanIMsGKPNOechMlKNSFEjD782T2WRhqzc4/EDuK3VIzNwrMi0HYGUQXWR2+iBaC+pS+R+Y12B70YWIK2ztsjrrkZqaiqpqanlN3Rw2A3giMVeDhGKVCpWpO4zZE6+GWnUT43q7VWkwVqL76pjheFUlOKwkMhgaPCDqt9AgdqrzflpaNG37j/VzLj+jrRKFhkoo5JNK2ux1IwlA5Gimvib1VrT/37IGrMGbYrTUUzDMDPGC805tZE2LFgsz0NVXl8zr6dMHwPQRj7FzJWt0REkFwT62Ircsh5HpvnuwHtos74S+SLfj++GsD++S5b9bj7F1aRwcHAIwi+aZ7M8DSznjHgIuldGJoNITk6mtHSq6ftZFHvRHq2v6WhtzkWF+d7DT8fdBRGIEvz9wMY+1KHsellRdECEoRCRm+qBfjqZYwuQIull4CyUddC6/K5BiqgUfMWQRTpyD+uJ1vj/7DbkwsFhT4EjFnsxtOGUV6SuFG0AISK1NRfgC8AfIhcikC/rOUSSk8+Qtqw9PomwwdbFaIH/qznWHC30HZFrUQ4SoOcgzdtnyOxeB20K3QJjLsAPZl6INjNb2To66LrAvKch7dtqtPEeiLKU2Pznq5EVIzxrgbGvNeNvaeamEUp5eDnQBxGl/shXOUjY4pECD8V0dEHkZqa5Tj6qhjvb/B9d7MrVpHBwcIgNn1w8hLI8lVc7KBqJ3SutC46u8TMS5regdb4aWusaIOXOMnxr6374e4GFrXtRXnxa0N01hJ+sA/zCqbnIqhzspzUiMf9B+9a1aO8Jtqlr+l6JSEksK3s22tMaAg87cuHgUAnsFsTi7bffZtiwYfz8888UFhbStm1bLrroIgYOHLhN5r8ff/yRhx56iG+++YaNGzfSqFEjevXqxR133EH9+vV3wB3sfvAzPl1K4o3GxhSEiKWtkSa/FAnNNxKbnMxAi342frXoYiQUX4BiGjpEnW81+anoMexi+roL3+XnDGRReMgcs/ELS9Cm0tDcoz0WjG+Yb/5ugDRoNdGGAiJDd+LnRq+Pb7mwKEKarSz8gnY25eEbiFikIYH/J1SgLh4p2GL6yEYbeHzC0KZNG+bPnx8eRevWrZk3bx4ODg4O8eCTixdQbZuBxF6rg6ice6V/jWVoDT0LOAUFU9+OhPpL8TMDWvIQJBY2LW2soG1bMC8PXzEURBp+qm7wLd02Jg10v6UoycZ5aM2GstaRBsglahlScMXDQLR/vODIhYNDBbHLa97feOONnHfeeUyYMIFu3brRs2dPFi1axD/+8Q+6d+9Ofn5+pfp75513OOKII3jnnXdo0aIFffr0ISkpiaFDh3LggQcyd+7cHXQnuw98UjEIaV0Saa+spt8K5qDF+2FUSC4VZVgagywVwdSstr3Ndb4FX+OfilyLJqGsI2MQabEbnb1uadT/aagOxRhUg2IEigGxbfKQJaEOPqmwxwiMaRZy/bK51mPFb/wFbY5PIotCECvQphWsnp2EXJ/qIDemdPwKr3ko6HGVOXcVBx/cCs/bjOeV0rZtk4hjbdvWM8e8iM1q3rx54c88z3OkwsHBoULQOpKLXDT7oziz25B19AfkqvSD+f824CjTbgoVjdnyr7EFpXtti6zKLyHLa8hcFyS4RyezsCJHsFZQEUo/O9/0nYmsyy0RWWlp/s8yx+ejtdbKBoXmPYRPTuqgtToektH+sSXQTywkodoYHfHj8RwcHBLC24UYOXKkB3jVq1f3fvzxx/Dnq1ev9jp37uwB3s0331zh/pYuXeplZmZ6gPf888+HPy8uLvYuvvhiD/C6du3qlZaWVmqcGzdu9ABv48aNlTpvVwDwIMeDWzzwKvgq9aDYgxLzd6kHazyY7sHfPejiwfzAsejXUg/qevBf00eJB309qO/BkEC76OsWeJAfdSy67yEeNPTgSjPGmR78av6ONZYS0+fJHtxv2m9IMPZNHpziQb/AZ8s9+MmDlYE+g2P80IMDzD3v0p+Qg4ODQxkQ1vxkeVDDrMUNzXsN8znbtH7pvPoefOLBjx6M8eAwD6714BAPPvegngeveZAXtVbbNXy9+X+dWc9nm3U6uAfFW9//a+7lcA+eNGu4XZtHmHX5nRjrdnB/8czx3zxYEmNvin6N8KCOW+8d9llURg7epRaLBx54AIBBgwZx6KGHhj+vW7cuzzzzDABDhw5l48aNFerviSeeYMuWLfTo0YO//vWv4c+Tk5N59tlnqVGjBpMnT+azzz6rwrvY3ZCKYirurMQ50VaLQuTyUxtpa+oA9wTahZCWZymKjViPXH1mmPPHAF8js/iNget4UddNMZ+VxDkOsppcgmI/vkHarQYkNraNRVaNk4if1jCYJes6lBHqV3NPK1EKw3hucz2QW1XlrGkODg4OOwNe2Oq5mf33b0TQWrr//o1iWksrhyRkzT0UuUNdgNb8TfhWh1lEWpI9fKt0PnI1XY7i7Fqb9/IyRYVQ7Y5uwGnAv1FiDYupyJJ8Rjn9ePiZrLaU0xbTX/MKtHNwcNhlxGLp0qVMnjwZgAsvvLDM8WOOOYZmzZpRUFDAxx9/XKE+R44cGbe/6tWr07u3aiy899572zrs3Rp+sPaNVC54zy74llwsQwJ5A/yiRFOR4L0VmaIX4KcNbIxiKD5Dgv/l5v/yyI0tcFRMWVIRCrwPRmb3axBxqhEYbzS2oIxPh6MiSfHSGgavZ4nCS2izq4HM78F2wT7SUWrGEEcffXSC+3NwcHDYtfj1118j3Ct//fXXKui1lEiB/GbkVroRuVYdgPaDQsq6Q2WhTFKrUaHRRlRcFClEiqMjgX8Cf0PkYhRyg/oCKdYq4rbkIYWYrVWUCBmIQGVVcJwODvsudhmxmDZtGgC1a9emVavYwVNdunSJaJsImzZtCsdP2PO2p789Fy2oeJE6iBSwk9DivAW/zoWHL3g/g7RDn6CiSbVQAHRNVIthCcqDXorITfUY17NZo2xK11T8ILx448pGQXTr8APFLUJR5zyOfGxvMn3aQPF4mjk7hu6m7yaUJRWxoOwjhYWFCdo4ODg47F047rjjkDA+O+rIv5Al4VOUlnsJsl4H4+hsko8ipMCpS+UwFu0D55p+bYa+x4C3UKxc3zjnxlrPgxkHAx5kMdERSKFjx46VHLODw76FXUYsFixYAEDz5vHNi82aNYtomwgLFy4M/x2vz8r0t2cii4oVqbMILqBWQF+PrBU55v9NaLHujLJE3YVqUVyPNEOtUXG3zchl6gVkMu6NbwGxGaK2mvfCwP+F6DG0maTioRcqtvRsgnt5CWmuBuK7MaVHtQm+gvUqOiAXqAzKItriYSt7p4Wtbg4ODg77AsaNG4eIwcwYR+9H67qtDfQUco2y8BAxyECKqUSIXnfzgKHAEWjfKUVB2APRfnWXaXdk1HmJyEIww1R557QH0pk1a1Y543Zw2Lexy4jFpk2bAMjKim9arF5dGu/c3Ny4baL7S9RnRfsrKCggNzc34rW7o3Pnzkjzvr3alDy0SOcj16cFaBM5CFkn3kIaqVHAE8i3dhXKF16AFvo/mbEUmJfNZZ6KX1QpDb9qql3Ei/Bzl1uh3yLd9DuPsot+HtrQnkfpBc9G5MhqyKIR9Pe1bdqYYwuj2kDZDc5mmtrlSdUcHBwcdgHykKtTdFrYg4CDgTcRCZiN0oVbV6MCpIRqiNbVaEt1PASt0beZ/jy0zmehdOH5yBpSjcTWZotYWQljXdciA7fmOziUD/criYEHH3yQGjVqhF/W0rE7Y8aMGUhYb1/BM2JZK0rQQr8JBWUnIStBe1RnojoyXzdCi3cHFKD9GdpEctBC3wHftJyKTySsMG6F+WR8smErs1rrRtCqUWDG1RFtHqtM2wLklnU+Mrnfhp+3PKiJCgadQyShsOOxloqtJCYVJeb621o11sHBwWFvwB8oqUY07kbxE98CV6D0tk+aYxvwLeKp+JbqeII9RFqjb0buqsX4a/catDe0QYQnXlKNUIy/LTGKToMefX3wU4s7ODgkwi4rkJedreDivLy8uG02b94MQE5OTtw20f3ZPmvUqLHN/d12223cdNNN4f9zc3P3CHKhhTZzO87fhBbPIvyK1lbgX41chW5GpuwgQoiANEVZlWz109TA8ViwVgpboM9aGaLjJqzbUkvT7/1mbNbf9nDgOSJrTthrF+JX645Fpiy2mvcMIklF9FhWok1tDVBA165d49ybg4ODw94Jz/MIhdKQ1bonkclC2qGg6qeBAcAxqC7SCqSIqotPCqyrrFVCRVwFEYXHEam4GjgT7RnWWrEckZUmwOmIwMxHrrtBRZLtLxpbiVRsWUSf4yHrSwEdOnSI0Y+Dg4PFLiMWLVu2BGDx4sVx29hjtm0itGjRIvz3okWLjGvQtvWXnp5OenpF4xR2J0Rn6qgI7AK6GpGCZCS02xiFZciUPQ5liToOOBCRhwy0MM9DblNPow2jOpGbRKwF3VpHQqZtcjntPWTyDgHDkVXkDFS0L5aVJgk/rWENfIISz0hnq123CHwWTSrWoBiUhuh+i/nhhx/i9Ofg4OCw9yIlxaO4eAYK2n4k6uhfkNJnKHJNzQNeRCnDb0YB1qn4yiSrYEpBe4HN/jQUEYd/oqxTxea1FQWHl6D9qhayWiQBP6GAbkicvrYUubXGSjISCzOBYmbOjBVb4uDgYLHLiMUhhxwCwNq1a1mwYEHMzFBTpkwBiKhxEQ85OTm0bduWuXPnMmXKlJjEojL97Wno1KkTM2YsQVqVbuW0jhbclyOhGaRNqo8W5JHAEJT16U6kiepEWRLQCegKvIKsHuVlSrKbQ9CqEUTQzSiodbJE5AzgdzPWZuZ6yURuIqXIUpGLUhoG0+kGtVH2s1lI61WdspaKEmSpWG+umYXiTOJb2xwcHBz2ZhQVFZkU5/9BKccHRrX4Bwrgfh6tm92A71CQ9XOozlAHpKRKR0qxeSjj4NeImHRDAeANUW2ifLTulqK1uiFSIGH6yURr8+3EV27ZtT0X7UO1E9yl3R8KcGu+g0PFsMtiLJo2bRp2I3njjTfKHB8/fjyLFy8mPT2d0047rUJ9nnnmmXH727x5M6NHjwbgrLPO2tZh77aYPn068TN1JMJqtIDbnN6NzOcvIZejP6FAvKPRQh7PZPwQclUqRvEZ8WB9alNInGs8lvl6DrrHx5Cp/UXgQ9OfjcmwryJEAIpRsGDwUfei/i4EvgQOI5JUlCJt2Tzz3hBZbUajTFkODg4O+y5UYG8VWv9vRYqlIP4C/A+51i5ALlM3owDv8Sge41IUJ3c5cC8wwRx/CBVIzQfmAovRWl4TxVM0xycVHtqbMtB+9jmJXaBsrFwmvqtsPIRwa76DQ8WxS4O3//nPfwLw0EMPMXXq1PDna9eu5ZprrgFgwIABEfESI0eOZP/99+ekk04q09+NN95IZmYmX3zxBS+++GL485KSEq655ho2bNhA165d+dOf/rSjbmkXI16mjngoQMQiEy2+tqL1SKRRuhoVp6uGNox4GbzmI03UQNN2Rpx2NqYimUhjWbzAvejPZqCNIx24Epm7n0K+u9asbl8ZaAPKRBuIjeOIrgTroaJKa1Bu9HxkmViOiMxS03cbtDnmIp/f3O2oWuvg4OCwd8AnFy+gInJvE7kHtQP+C9yD1uX6wCUobfm/kVX8MbTnfGrOvw1ZM0rxA7VtcdSGlE0jXoQsHunIgj4U7YfRa7S1QNhYucYVuEN/zXdwcCgfu5RY9O3bl+uvv57NmzdzxBFHcOqpp3LOOefQtm1bpk+fztFHH829994bcc7GjRv5/fffmTdvXpn+GjduzPDhw0lOTuavf/0rRxxxBH/+859p3749r732Gg0aNOCNN94w5tu9FfEydcSCrbBdhL9oL0OLaB8kvINIRTHx846/i8zJJ6F4B1txNRrBmIpYSBTkXWD6bYk2BQ8RmZpoUypB2iv7so92YzP2lVHXsdfajGJDrIvXAjQHm5CFpjXQCm1YpUijNpNQKFHNDQcHB4d9B57nkZKyBVXd7o9qSdyGMkL9APyCr7haixQ+SWj/qWteVgk0H8VPrEd7RVNgP2RNX4+UYRBZI6kEWTRsco81wKNRo7SkwsbKNaD8mk/+mq8YDld528GhPOzydLNPPvkkb731FkceeSQTJ07k448/pmnTpjz00EOMHTuWatXKM1NG4txzz+X777/nrLPOYv78+YwcOZKSkhKuvfZafv75Z9q2bbuD7mTXQ5qjXJSpI9okHY1886qPAuFy0OPwEH4lbdDCugoJ2fEW4e+BE9Em8He0KYyOHh1+cN62ELsP0cbxd7SRLDdjutZcf36Mc2wMRR20kayJce3H0UZ3JyItrYD9EUFqgm8mLzVt/wPkUlrq0g46ODg4WBQVFeF5hWg9nYafFeoMZMm4ECl4fkFKrP2Qy9NBSGhviR/fB36NilKkALJpalegvSAfX1mVjmIzWqL1+3rgPeQui2lTgvaNVYjI1CnnjoJrfgly4Urh9NNPr8y0ODjscwh5zp+jXOTm5lKjRg02btxYodS3uxKyxuSgeg7RmTqCWIxMx61Q4HIzFEdwPtLQHIcIwlIkyB+JCEee+bwQWQZqA6ehYLk/IWG+G7JufIKfccNm/UinbA2IeHnDLTaiVIIbkOZoCdq8Gpox9QJOQBqyYP0KGySeatqvMeNqYK75IvAMcAsyzcdDrpmT/wCrnAuUg4ODQwVw/PHH880330R9eigwkcTWgo2IQBTgu7Haddf+n4HW8ppof+kFdAduNG2fRWv2mWg/3Iz2gwaUTyqCa/460/8PiCS5PcBh30Nl5OBdlhXKYcdA+cXjZeoIVrPOx6+wbRfpF9CC/iLS4JfgB1vnosclCQXgWReq9cg6MtL00Qxl/RiAFub/C1zb5i4vD8FNxAMeQPEOo8xnTc21V5j3E5EJ3l7D+tyW4MdcNDDvK5HG6i3gIxQwGI9UbEWWl8cRoXFxFQ4ODg4Vxbhx4yL+195k3XXPTXBmDfMCreWb8dOpb8CPqViE9oEfzd9tkaKsCK35LYFhSMn1F6Q4C9bciEb0mm/TooOrvO3gUDE4YrEXwicXD6F4gTvwfUOtSbgILZTW1el5tNi3QKljL0I+r28APyPLxjGIODRC1ooC0/8c0+Z2VKzuZrRpvI7IzQ3EryERi2gEScWTwGumvyMDbVrhWy5qos1kPbJIBAsuBR/xLGSGfxylq61t7v115Ppk63LMRpvKp2izyiUUKqa01JEKBwcHh21F4sJ68ZCKH99XB+1BoP1nFdqDnkZW+AFonynFt3YUAlOBQchCfQpyvUq85sNVpr2Fq7zt4FAROGKxl8InFy+g9H0DkRk3HT+wOh0Rh+eQducOFLRdE3gfBcHVQlaHkxCZKEIao7WIoBxl/m6CzNvDgH4oRmMdqri6FMVG1CZ26r+IkZv3TcB9SOjvhqwWweOYa9ZC8RXrgeOB3mjT2A8Ro3xEfGag4O8/kFWmyLw/jX4G6URuSMXYnOXOSuHg4OBQVShC63GswnqVQTraA55CiqJ8tCedjBRc5wAHBNr3QdaI2cRf87eg4n3vxbieKm9XNP29g8O+Ckcs9nqE0CLeH1kw/oSE7hrIEvABIhyXI8KRAbyK3KH6IIKQafoqQotvDUQSViJSYS0EPVFsxhBESvojwjIckZubzLVs7vEgrPBegFyUhqDc5RcgopIZ4xzM503M38sQESlAWi7rUlVk2oWALaSlhSgo8MlCt27dmDx5cvj/rl27uoraDg4ODjsAid11K4PIhBrqtzrQGSnJovF+4O+Pzbkb0X42EMUKJoIqb3/00UfbOF4Hh30DLni7AtiTgrcttHDXR+bfgSiP+H3IdFyCXxDvWqTlt4XvZiEt0tXIFGwfD+s+ZWMWLNbgZ2gKVk9/CblXDUaE5Uok3DdD5KYTym9ejbJWhSUoMPsl877etI9X8OhLlHHkX2bcK1GmqC3mmt3M/T+MC7xzcHBw2PXw96hLUUxfRdyiLGIn1FCfFQkOryy2Iuv8NLd/OOyTcMHb+zj8zFCX4muDLjIvULXTx4GLkYvSIkQaViOzsq1hEU0qbN7xIOoiC8Fa/ExRmPNXoBoTr6AUgL3Rgv8/5GYUbVXIMu0eRW5NHkohmGH6tOMJxmWUmjYFyHcWZCXpbf52GZ0cHBwcdjfEdtftTWJCkDihRigUwvMqEhxeWajydlKSC952cCgPzmJRAexpFgsFx3UFxhCpBbJfdQ4yF3+IXJpyUZzCIyh+4i1EEGwWqWiBPoQsHMn4weBz8CtUW+QBf0aWjGVAF2RBAVklpuG7KXVBJCWI9Yj0NEdxH9GB3iHT5i7kvrUxcMxldHJwcHDY3SFykYr2peZUPLi6KOaaHn//21bkIjffKaZOh4PDvgdnsdiHoUW6DsrlnU0kMQBl40hHmZoyzbFs/MJF9yHCYFO3Wg1N0A2pFD8NbRLaFOqhIO2t+BaGLORqdQdKCTsWZYxKRZml6pixWDesIEqQ21aqGaeNm0iOarMYGIeIxw/E2oCiYyocHBwcHHYPRLoxrSVxcHX5CTUyMpLZurUqgsMhWHk7IyPWPvX/7J13mFTV+cc/s7C7lN2liDTpsJSFZRW7YjRqRFGRYIzR2E1iTSwxJhqTaPwlptp7L9hFsWPXGGMvCyxlYQEBQem7LGXr/f3xPYd7Z3a2CbiF9/M888zOzLnn3pmduff9nrcZhpGI+fVaJf2RSznatwIkDv4WeT1AxjnA28hj4Dtop7t7Ly42o2Tq2cBCt12qm6MMrTalIkM/ysGE1aDWoHwICL0dFdQkQHkbZagOuRcfPnncsxx4F3k/1qHE8PNRpafPgdUEQTllZWXJPybDMAyjWRAEgbuVMnp0f7Sw9DWwgtGj+xMEpVvG1MWmTZuQl+FB5LH+tsQnh2tewzDqwzwWrY6OKDk61T2OJdxvdq+3J1wFWoIaDB3qtveN7IqAKSgRejE1RUp/lBg9wf2dRdjh2oc1pSGx8jFKfrsVVY7qgMRCVCjg9rHCzbMTYf8NX+rWj1+DBIVO+r6J36GHHsprr71W/8dkGIZhNEvy8/O3avuavZy2TXK4YRj1Y8KiFTFw4ED0Lx3pnokRn5cwBxnz/vVUJBi+QWLAV176CvWueB95G76PEsEHEca8LkAVpF4HngD2AX7htl9J2Pk0BdUSn4KqMp2OSsleQegw82FXVcgLsRqJlD4J77BtZMwaVOlpDlBpJ37DMAxjC9sjOdwwjPoxYdGKWLRoESrfN5TkUW5PoZX/oe7xJhRCVO7GD0K1vq9FOQtXo8Z4qdQkF1WP+jUKb7oFlXo9DjgerfhsQLkXvgxtFepl8ReUY3F65PliJHDKkKciUVRUuzlXuHmfRoLGTvyGYRhGTYIgoH379mze/Anqq3QNDU0Ob9euDZs22bXFMBqLCYtWQlpaGmFORG3N5NYnvP4lOrF6r8YLyLOQ2BgvGf6Em0p8Y7z7kFC5CLmgv0JxsiXopP19YBIqa7sAOBUJi3J3HD2Qt2IzYdLeZsJwJ1DVqnuBlSYqDMMwjFrxuREpKSkEQf3J4SkpKVRVVdUym2EY9WHJ262EigrQKn+AGsMlIxOdRDfiy/XJkG+HPBePIq/DFdQtKpLRAbjcbf8w8ih0Rw3xUpE4uBN10Z6CvA53ASejBOwO7rYaJYcXuftlQCnytMxHfTf+gxryGYZhGEb9VFdXb0kOHz9+f6LJ4ePH778lOdxEhWFsHeaxaAWEJWYvQklqhSipOpEfoYZ1hUAXZPB3QsZ8CQphOnMrjiRAwuEbFKO6Nwp5Wo1yNV5EXpMytFr0EupBcRsK0zoYNcjrg4REOUrQnoNK1a5BuRx3oNCrm7biWA3DMIwdkRdffLGpD8EwWi0mLFoN/VHy9B9RrCjIK7CUsCN2XxR2VACMRsZ+DLgBGAacsZXH4JOwL0SVpK5FIU/zgN5IVPRAogZUcWNnJCqeRJWjplCz+lQ/JDqOA4a450cCbRk7diz//e9/t/K4DcMwDMMwjK3FhEWrwJeYTXd/PwTMRWVkE430CpTrMAmVnF2Aqj9djERHQM0O1w2lGlWdykSJcle743gbJYFnoYpOlW7Mq6hi1GDgMjfHBpT7Ue7eTz/CkrNRhgLpvPfee9/yWA3DMAzDMIxtiQmLFs7BBx+MQpq6AychYzwTlY49lZolYp9BSdofI6P+KeS5OBoJkfUoPAriRUl9eFHSxt0fBNyMOp+uQd6G3u5YVyJxsxSFZkXp6I7LC5zaRE47LEXIMAzDMAyj+WDCooXz1ltvIU/AvSg34UYkMjKBbGToe3yJ2D7AA8jY/whVaspECdEr3H10uygxkguOSsIu3dUo9OoglLA9AZWcDVDjvGKUH7ETqiZVG3V5T3zVKMMwDMMwDKM5YEu+rYJ2wETkfTgShRZtQhWVkpGNQpSuRTW7c5AR3xN5Hr6pZ3+JnoQKt73vd+GFR28kAC6IjA2AycAi5E2poKZQaUgoViFQxv7779+AsYZhGIZhGMb2xoRFC0bVoHYGzkU5Cj4Xoav7ezUKN9qIDPwqVFHpU/f3Eyj0aZDbLhUlV69D4UrJCBLuK91cvoeGf22tO46d3Ov++btQONYpKJdiDqoSVUa8ByIW2SYZBUClJW4bhmEYhmE0E0xYtFAkKrKQgX4cSnYGJT8viYxchXpCvIBCoX6N8i+ykIhYj0KTfI5EJxSutBIlWicLN/LGfgUSFm0JcyuqUG3wVcgD0gGJmvXAn4FbUfWp/3P7eQqJkoCwSVF9bEY5GhsaMNYwDMMwDMP4LrAcixZLKhIIf0KhS+tRkvQm9G/NQt6MGHAV8AjKreiODPq5SFBkuu0WEuZm7OzmWIGMd/98CqF4qCQMf/KiYr3bZqPbv8/H+BD4LRIbpwE/cNsfArxHKEwqCEOj6vpqPo9CuAzDMAzDMIzmggmLFkjYEO9CZPAvQN2pOwC7IKPeO6NOBV5z9/NR3kU1cA1K9E5Bxnw68BX6SmSiUrS9kRhYigz/DJSUnUaYqL0JeRpKCEOi2qBQrHfcnDcCe6Kmdl2Q+JiPPBpfIUGS6eb1XpAYyR1qJaj5XglB0JiqVYZhGIZhGMb2xIRFi6U/qrY0Ha3wd0WGelpkzG+RqLgUGe6PAicjL0cH1L/iDeS9mIQEwjrkpVjr5vDGexkKt/IGfzTBOtXNl4HCoCpRKNVMJICeQg34/HyZblxnt59FKEwL9JUMkMBIS9hPNeqNUUCbNlYRyjAMwzAMozlhwqJF4hviFSFjuxcy0qsJS7R+gkrKnoJEyNXAz4Ffuu0DJDBeR/kXlyCvRQ+3j2pCMZHmXou55324Ugph0nZAmJMx0G3zGXAsoajAzdEGeVZ6ubn+DdwfeT3VzVPp9uuP5zrUrbuEykrzVhiGYRiGYTQnLHm7hTFs2DBkeA9Ghv9OSAz4BOgKN/JnwBB3fy1qgHcsMtS9+BgAXETYOC9KCipjm+5u3kuR4h63jzwPqkC1zh1LJ+BNFEZ1IjXLx/rHHZGn43kkhKK0RWKiGoU//Rb4B7DCQqAMwzAMwzCaISYsWhiFhYXIG5BFmAcBofegCngVhRpdhBrRdQbOj4wDiYvAjRkOXI8ERn0lXxOfq0aeihUof6ILyve4CdgdeSuSCYEYShjvhPpq/Czh9TZIOD0JHI4a7ZmoMAzDMAzDaK5YKFSLpR2q1pTYITtAHoo+qPHdNSgMyve4SJab8DAKrboBCZDuhFWdfPhTdD8xJGBKkaCoRJ4Kv49/Iw/G7fW8h9nIa3I88CvUPTwXmIfyM15BvS5KaNOm2sKfDMMwDMMwmjEmLFokPpeic+S5ze75GErGPgGFGHVFZV29MChD4UdRI7036itxlfv7aFTCNgt5QdoiIVONci42ofCkSjdXfzd/OXA38DRKGO8f2UdiOFQZCpc6BImaXVCjv64onKud2/8mcnKyKSgoaOiHYxiGYRiGYTQBJixaGEOHDqWw8BvkEfBhTZsIez8Uo6pOo5CB/30kDnD3m0kemjSR0MuwCVWPKkUCoAoJB7+/NFQBKtPdQPkV1wNTUQO8ZLkVUd5EFaF+jEK6DkOhWL8HdgX2Bz4CjmbWrFl1zGMYhmEYhmE0B0xYtDBSUlKQob/IPeOFgvcsfOr+7ovCiE6ObN0ReRp6kNzoPxMlg1/r5jkb2As10KtC4VUdkcCoQp6FclSy9kaUrH0p9YuKUtTTYl9gkJsjx20zBIkKkNfC0oAMwzAMwzBaAiYsWhhz5sxBxv27xHsq2iLDfKO7L3GvDSI08rsiz0IJSppORmfkkXgP+ACJiQPdPLsQVoIqQU3u3kGejhwkLrLdPN4rkigwfNnYYuB3hGVrB7u5ZwLj3Fgf3mUYhmEYhmE0d0xYtEjaI2P+FWSEp0Ze8/kT693jdgnbdUAJ15nEewM+Q5WZliNvxzkoiXsBytl4k7Ar9iYkYNoCPwIuQ8JjQJJjDRL+vgd4FriCsKJVzB1bLHLcAIVAGTk5Ock/BsMwDMMwDKPZYMKiRZKCQoZuBw5G4sF7BnZHAuAr93hzwra9UWO9RUgklKPSsM+5OW8BjiK+gzdISCxCoqINMAu4FeVxVAI3R8bGqJnHsQF5Kp5FIVbHJIzfhLwTmZHnC4BKS9w2DMMwDMNoAZiwaGEMHz6cOXO+RgnOz6MSsVdERuyMQqW+Rgb7AlTCFSQopgD/RfkXbYDF7v5k5HnIomYJW5CnI4cwt2IEMB6Vs52MxMW9bqxvwAdh9adbUPjTFUhUREOkqt2xlaOKVCBB9AoSJIZhGIZhGEZzx4RFC2P27NnEYhkoUfos5LXoRXyDuRzgbRTSNAvYA/gb8D7Ks/g+cBryUnwN/Ab4JTLwq5BI8A33Uoj3PvjqUGUoJ+IqlAz+L+AIJAzaIHGzFHgLJX/vi3IqdknyrgJ3nABD3f3zSPQYhmEYhmEYLQETFi2SDcB/gMvRyv9tKDfiYmTQX4L6WIwAHgdeRB2xr0Z9I1JRTsU04BTgQjev91R4cVHmxkY9GMm6cJ/l9n8X8LHbpsy93gP4A6oUVRubULfwzu5xCQqbKrFO24ZhGIZhGC0Eq+XZYlmKqjadgXo/vIo6WL+MyrX2QuJjBXAQ8BhwOGGi989QTsUfkszdBnkj2hAmbAeRWyVhkz5QPsblqHdGd+QJuQXlUmQgwbMbKmGbSAC8hLwTv3PzXo3yKyoa/nEYhmEYhmEYTYoJixZLOXAHSqr+IRIOA5FQOAoJi02outOpxFeHeht5GC5Chn9tPSd8b4xK5MXwosKHSqW5eVORR+Ri1PRuE/KY/BXlc9yCcjSOROIhyjqUJ1IN/Bx5Kh7EvBWGYRiGYRgtCxMWLZY05I241j3eBVV3ehw1tZuOSsEejcKSvols+y+Uf3EUNSs4VROWk92EviJtkJioQAKjLRIUiUneR6K+F/9wjwPk+TiWMOzqQZTf4ff1F+SduBb4rdt2hYkKwzAMwzCMFoblWLRAunXrxqpVFcABqHyrT97ejHIs5gHDgF+758pQ34tUoBtKlD4BGf2BG7MG5W4kCz9KRX0mMt38tX1t0oHDkPfE4ytEZSLR0Av4J6pA1Q0JjT7AnUhgmKfCMAzDMAyjJWLCogWycuVKVxmqPcpjuBmVZl0GLERhUn8mvtlcBcrLWIoExCgkOJYRNrvLQp4I31272o3ZjBKqS1BIU29q9rnwjAJKUdWqbu65qFC4AIVh3eme3+D2Mw+oMFFhGIZhGIbRQjFh0WLZgBK2x6HO2EuB/sBwFLZ0IhII1UgYbEJ9JN5B//ZeqHdEWxRGlUXyXIv27r4HEhYr3HY9UOnaRLKRh+NTd2yJVKOqVe8Cn7hjXW2CwjAMwzAMo4XTpDkW69ev5/LLL2fYsGG0b9+ebt26ceSRR/Lmm29+q/kOOuggYrFYrbeePXtu43fQ1OSjxOgxqOTs20An1E/Cex86IAHQG4mODMI8is7AYLdNbQncnpgbN9ht9zUSGYm0d2M3II+E741RgQROOQqnutDNh4kKwzAMwzCMVkCTeSxWrFjBAQccQGFhIb169eLoo4/mm2++4eWXX+bll1/mhhtu4Je//OW3mnvcuHFJRUSnTp229rCbGTuhhOg/IsGwEXkuTnWvRw32AOnIF9zfHZHXorGkuO1SkbBoS7znYpObPxUJiSgBSvhOBSYAf0e5H4ZhGIZhGEZLp8mExS9+8QsKCws55JBDeO655+jQoQMAL730EhMmTODCCy/kwAMPZPTo0Y2e+3e/+x0HHXTQNj7i5kMsFkP9In6LSsZ6liLjfTA1O2aDwpPeRp6MNVt5FN2QF+IbJGp8zsU89/yukbEpyItRHRmXjkKlCrfyOAzDMAzDMIzmQJOEQs2aNYtnn32WNm3acM8992wRFQDjx4/ntNNOo7q6mmuuuaYpDq9ZI1GRBZxMvKgAhRlB2LMiRnyI08+Aoagj96xtcDQ9kDZdFnluJvKG7OKOo727T3PHUhk5ppFAW7Kzs7fBsRiGYRiGYRhNSZMIi2eeeQaA/fffn/79+9d4/cQTTwTg+eefp6LCui/H0xYZ5FdQUzh4b0BiCBLAWygv4iJgb+ANtr6zdQrynGwkLGv7Ksrl8F4KCEvO+kpT/riHAunMnz9/K4/DMAzDMAzDaGqaJBTq888/B2CPPfZI+rp/fsOGDcybN4+cnJxGzf/MM88wdepUNm3aRI8ePdhvv/047LDDSElp2f0A5a3YCZVsjeaL+CZ3fdzfC1DZ1yj/cq8fBeQAU4HXgPFbeVRZKBxqDfABCsf6e5JxPsfDd/COIU9Gy/6fGIZhGIZhGKJJhMXChQsB6NevX9LXs7KyyMrKoqSkhIULFzZaWNx44401nhs6dCiTJ09mzz33rHf7srIyysrKtjwuKSlp1P63L/1Qh+vEKk4xVAGqPwpzmpDw+mxUgjYNeRR2R/0vDnLbfVt8aNY3qHt2D2A/JCASO3MnejA2Iw+GYRiGYRiG0dJpkuXi9evVuK1jx461jsnIyAAaZ9QfcMAB3HXXXcydO5cNGzawdOlSnnnmGUaOHElhYSGHHnoos2fPrneea665hk6dOm259e3bt8HHsH3piDpbt6tjzF4o7Cka5rQKhStFvRh/cs//axscVzpwN0rcvgcJigrC5PFEEeSfL0R5IXW9H8MwDMMwDKMl0GiPxaWXXspzzz3X6B3dfffdjB07ttHbNYarr7467nGHDh3YZZddOOKIIzjggAP4+OOPueyyy5g6dWqd81x22WVcfPHFWx6XlJQ0ubgYOHAg+nflEG+oJ1Z+OhZ4AuVQHO6e+wSVeI0mSQ9AIVX/ROVjf/4tjywAHgGeRN6P3d3z1UhcpNexbYHbPp28vDzy8/O/5TEYhmEYhmEYTU2jhcWyZcuYO3duo3dUWlq65e/MzExAORT1jc/Kymr0vhJJT0/n97//PRMnTmTatGlUVFSQmppa5/j09LoM4u+eRYsWoUTpwYQ5FckYBOwL3Ap8D4U5bSQMlfIEwE9RH4lbgOWoI7YfU9c+PBuAfyMhsxk4PvJaWyQsqgkTuaMejM0o0TsDqGD69On17MswDMMwDMNozjQ6FGry5MkEQdDo2+GHH75ljgEDBgCwePHipPsoKSnZEgLlx24tI0aMAJQ/sWrVqm0y53dPCjL8kxn80QpRvwXWoZyHWGSbjUm2+xXwG9Q471jgJeLDmJJR7sb9yG13AhIIUeHShrC8rMdXhAJ4HvgSOBtL4DYMwzAMw2j5NIlFN2bMGAA++eSTpK/75zt27MjQoUO3yT5Xrw47PHuPScujGomDqNGfWHI2AHoDFwPPotyHPZBYmFfLvD9FoUy9gMtQCNVfUeWo6cBcdz/VPX+EG9fLbTfIzT8KJW17fHlZf1zee1ECXOeO+zAsgdswDMMwDKPl0yRVoSZOnMgVV1zBe++9x+LFi2tUh3rkkUcAOProo+sMWWoMjz32GCDPhU8Mb0kMGDCARYvWAkXAntT/r/shKgF7Gwpz6oCa19VGfyRC5gKPAu+hEKdEdgHGogpTw91zdyKPRQ9Cb0db4svL+vs2wF9QfsWDKIG77Ft1WDcMwzAMwzCaD03isRg5ciTHHHMMVVVVnHnmmWzatGnLay+//DL3338/KSkpXHbZZTW2PeWUUxg+fDg333xz3PNvvfUWb7/9NkEQH8JTXl7O3/72N2666SYAfv3rX2+Hd7T9Wbp0KQormoVW+JOFKiVWYToT+D3KZdjo7suTbBfddjiqGPU0SgB/AAmO+1Dfi2eBqwhFhc+VGIESxNu646yKHEc1YcftG5CgCFAOSAFQaYnbhmEYhmEYLZwm8VgA3HnnncyaNYvXX3+dwYMHc8ABB7BixQreeecdgiDghhtuSLqKvXjxYubOnVsjTyI/P5+LLrqIHj16sOuuu7LTTjuxcuVKpk+fzjfffAPAJZdcwplnnvmdvL9tTWVlDNiEjPvL0L/O//uChFu06/UPUQnac4H/oXCmH0bG+KTq6oT7FKArasjnKSf0SKS6bV9ETfH+7ca0da9XEHYCr0ThT/9EoqI3Eh4zgFdQErhhGIZhGIbRkmkyYdG9e3c++eQTrrnmGqZMmcKzzz5Lx44dGTduHJdccgmHHHJIo+Y78MADOeecc/j000+ZPn06a9asISUlhd69ezNu3DjOOuss9ttvv+30brYvYcftA4F3gGnAMUgE1Oa9iKGwozbIkH8GeRluBA6lZlM8Lyb8Nom9J0BCoYLQI7EZJYj3Qt4Hv99UQi/FZiQebkRN+g5GoVA/Bv4DJE/gNwzDMAzDMFoWsSAxdsioQUlJCZ06daK4uHiblL9tLBIWY5DHIQv1ipgKZCIRkOh98J6HqPehLfAx6sh9GvB3wqTpcrdtQ0vsVgNlwB9RmNQPgR+gPhntkWdlLsrpeBWJh7Yo32MwCn+6wu1vPkFQW3iWYRiGYRiG0ZQ0xg5uMo+F0Rh8x+0TkDdgFgor+hsSBD5VJiD0NLRx91XIc1COPBbHAJORl+EC4kObauuUnUgMuAN4GOiCvCgvEoZH+VCocmC92+cm4Eq3/WagFPXQSHPCiRr5MYZhGIZhGEbLwYRFM+fAAw9EBvsUoBg4ByViP4xCnC4g7A+RrKldG+RdWIQExkVunn8Cy1APi05unN82KjAS51wP/B8SJwe54wiAlajD9wbktRiD8i7uQT01DkFdwwcRiopiJJJeBb4kFlOolQkMwzAMwzCMloeFQjWApgyF0mp+GtAZ+B1wITL2T0CegpNQWFEGNT0NMZRYvRqFHXV382xEvSpmImP/IhQilRbZNtF7UYaa4V2H+mGcgDwmifvznpJ1wCPAzSgEqzNhiNZ85IXpE5n7OTd3AVBi4sIwDMMwDKMZYKFQLRwfGiTjuy3QDjgFiQqQgf4IEgT3o54TFwFHEYqDGLAQVWPaCYU+VaLE71vcHGeiHhTno5yLw1CTu2iuRCEy9l8FlgDdUNfsvZBIKEXekHJ3nH3cMawGhiFvyDfuPkAej0riq02lA8chsfNn4EFisZiJC8MwDMMwjBaECYtmhARFKjK6+yND/yZk7P8hyRbXIc/BGcB5yINwGDDSzVGOkr0XoZCjN1HTvL2RJ6EUeT0uQELlURTKFM2V8IJhMAp7OhRVd/oT8kQsS3JcPd0xfN89LiP0VqxAFanaJ9kuE4Vo9QT+YeLCMAzDMAyjBWHCopkgUZGFRMSFKDTpMWTUX4iM7miokze490IhTU8iEfIIEgwpbr5Obrt+KM/hOCRaKoAvkbHfGXksUpHBPxuFS3UA9kDCwIdTnQ58ijwX0bwJ7+Eoctu/AbwMfI1Cn3KRqKh0+6+Li9x2d5q4MAzDMAzDaCFYjkUD2N45FhIV3VG40x+RiACFJGUB/0VGfbJG6f7fl49CkIYDee7vm1HVpr4orMqPXQA8DnwIvI9Egu9r4UXI3sCxSDRUu7keAnZGoVM/oGZORvSrVIlK4v4beUyOB85C3ohoGFRtlKDQqE+sHK1hGIZhGEYTYTkWLYhQVPwOrdRHWQ38iNCAj5aT3TKDu69GIuQdVKHpFmCfyLgAhS39DYmJLqhZ3XhgKMqN2IxExyzgdeAJYF8kJp4CJgGXIJESnTd6748nFTgAGIEEyWSUa/F4rZ9FPFno8zjHvBaGYRiGYRgtgGRL4MZ3RBj+dAo1RcVCZKyPJHkZ2Sib3Jj2wL+Qh+Io4kXIVOAnyHtwNeo78VvUY2IXlEMxCoVg/Q5VgLoaiYybgLHIm+I9G6tQqNPTKCF8JTVDtdYj78RfkSB5183dUI5G3hPDMAzDaF4UFhbSvn3NfMGlS5fSs2fPpNu0b9+ewsLCGs/37NmTpUuXbvNjNIzvGhMWTUoqMub/mOS1aSj8aSjxDfCSCYy1hJ2zZxEfphQD7gX+ghK7H0MhRqnu9c4obGl9kmM7GPgHyqv4CAmRo1B41EjgZyjx+2coh2KQe/0tFMpUgbwbFcDZwInAXcj7EX0/0VuUdsA44j0khmEYhtG0FBYWMmzYMDpv3hyp5ChR0bdvX2LffBP3PGgxscvmzQwbNixOXMRiMVK++Ya+ffvWKy7qEi21YaLF+C4xYdFEhN6KCwlzKqKUIlHgPQQp1DS+V6PE7a/d2NWoqtMo93oAPAPchgz7KyLzedJRD4wVKJwqyjJ3bGcjI/9+t68TgOuRF+QVd3+9e34FEhD7I+9IJhI5acBlqATt6e72ZZL3nSgyRgJtGTFiRJKxhmEYhrH1FBUVkZGR0aCxXlSMQNmDOeia7kVFLroa+udx9yNR3EAubBEXic/XJS7qEi210RjRYhjbAhMWTUp/FHqUjAxkXG90j6NdsNehZO0l6F/YyY35Ankast3jZcC1KNzpzDqOoydQhXIgPJvdvt9Fp85MVN72AeBKdNrcA50693CP/4ISzW9GnpAzgcvdMbYBugK/RuKmAFWoeriO4wqQxyadOXPm1DHOMAzDML4dRUVFDBkyhMwNG+o12L2oGIyu3gcCv0TZhF5UXISuvL8iFBcj3bhr3eteXIxI8nwyEVCXaKmNxogWw9hWmLBoMjqi0KT0Wl4/HJV5jcZipqDci4Vuu36oClQ/ZIQXE+/l+Bsy8C+u51jSgB5IsKxyz61B+RM3ovCmp9DprB3yWtRGKkrynoZyRx5EfTY8R6IckK5u3n+6fcQityjtsK+pYRhG62LOnDmkp9d2/UtOevq2X2TyoiLRYE92bFFR8RO0ZFeFyq8cifzr30P+eFAdxAPcnBPdY9zrP0SxBWOBn0eeTyYuoqIimWhJRm1ipqHi4vPPP2+wVyS6z88//7xR2xitD7PYmoCRI0ciA3xkHaMGIiO7IPLcbBQi1Q15Jbq4MW0JvQLey7EAVX86l5rhTxAfUhW4uXZGSdjLkah4EjXe80nbKegUWooa59U1ZwbKz/gNCpe63D2fjgRVIWqydy7KAYl6LqICYzM1Q7QMwzCMlsqcOXMYMWIEXcvLG2S8+pChncrLGTFiRIPERW1J0lGiosIb7OejciZZCceWKCpORDUTR6GMwj2Ac9DV91G3zaPoSvkrYFfgNff8a8jIvxBlOD5KeJVLFBcffPBBnKiIipbaxEVUVJxVy7zvvfceXbt2Tfq5fP7554wZM4aeCXN37dqVL79MFsKscb2AMWPGmLjYwTFh0QTMmjULeQmG1jNyJ+BV5LmY7+57oRX/xH9dO2AASpSeB0xBXoFDImOSJUv7W7Ub3xOYi0KejiVcS/FkIiGzNuH5aLnZWOS5C4CTgPuAz9zzo5A4Wenmn4RyNBYlzBlDAqQM6NLo1RPDMAyjeeFFRX0hPfPmzaNDhw5bDPqeqP5hLtQqLjp06MC8efOIxWJ0TpIkHSVRVJyOMgQDwvqIQ9yxJRMVOW7scpQtOQL54A9HwuFqdz8JGfcjUUzBE+5+JAoWPh61k00mLkYB++67LyOJFxWeZOIimajw+HlHAmPHjiV97doan70XFaPce/Vzx2Ix2q1dy4ABA2qIC7/P490xm7jYsTFh0WSkkNyTEOUKYDHyHPjSrT3ca4lhQz2Rl6ADSuj+CPg+8owkJn3H3P5TiBcBPqH8XuS9+HWSY4ohcbGBmonWyQz/mHsfg1H1KJC3JRX4xG17kXtvVyXZvgCdbs8EskxcGIZhtFC8qBiOQoZqC+mZN28eQ4cOJXPTJoYNG8YoZLT2RsZ/MnERi8XI3LSJoUOHkoM6QI2EpOLCi4pBaOnrdOADFJT7jjumS90+h7g5uhJmE+a4efJR8HBfFJTcEbWWPRyVJpmExAZIeIxES4Aj3WNQ3cPRyK+/PHKMp6ISKP1QMHGiqFiGroyJ4qI2URGdd0/3GR5D/GcfFRXnoyDqc9yYDMJcjai48Ps8240/HxMXOzomLJqMasLE7No41d3f6O57JxnjBUYW8iT0R/kNiwjXVKLjamuw5+8L0OnyPGov89oOeRH8+kqyeaNkolPOcuA/qN9GDK3blCORcQ4SGv9F61KgMKhX3Ot/QKfjVAzDMIyWRVRUlKJ6haOpaRh7UZGDDPZRkTG/QEtQieIiFovRBxm//dAy1EVINIwiXlxsSdRG/v2pKGj4JaAIGdv7oniCk5G4GISu1h+h1rPVKDB5jTvGUmToZxGKiz+gvIsoI1Ax+GiNwxeAD90+1rjnAlQC5W03NjFL8iEUZPwgobj4JRIidYmKanf861GswERC4RCLxeJExZHoSn0MEg0DUPyAD6caMGBAnKiY6MYfiYmLHR0TFk1ATk4OMqjrjv8UR7lxT6IciroYgE6Fi5FHYaB7vi7DPyo8Ktx+ugGH1rEfn9RWWce8iRyJunv/AwmHAFWzSkdhYT9A+RtTUX7IQvf3YtQ/w3fiNq+FYRhGSyJRVHRBBns1EhcnI89ANmwRFX3Q1eFC4Ai0pDSBUFwscNuMGDGCPshnfzDh6np75CP3IUVeXGRnZ5OGluCOQ7UVz3TznY7asm5ApUy8uOjtju0IJEJOR0JiELAPEhcL3XMd3a0NChhO7M4Ubaf3AsouHIEM+OXADCQqbkdByUuBOyLbPIRCrMYAbyJxsc59Ng+5zycZXlS8497HMBRYfQ7y7nR0n5MXFZ3Q/ykLiYaz3Wc1BXltMtxn7UVFFrIcOmHiYkenbVMfwI5IQUEBsVgG8YnZtfE8Oo2+gk5jP6tjbEfkhL0Cnb7TiA91SkYQua8GPkV5GXV5BrwejXos6uoMDmHS9mMoB6QC2D0yVwckZj5GqXOLUHnb9cgpDTrlX4P6dRiGYRjNnWSiwucL3IGy6zJQkO/XsEVUlCKj9YfoarEB+conoCWndSjzsDcSFUcBP0ZXl6no6vdDwhCi65C4AImE81BMwAgUE7AYXYH8FanY3T+Erm6/BMYDj6OOTm1Ru1eQwIEwE3IAuuKVuOd9mZUoUVFxittmARIKjxIa7gXATW6bDkhU/Mh9Di+hK2oZyojE7TMrYX+JoqIv+rxHAS8j62IwEhnjkTjwgdqZbq5j3OM7UGH7AUjEHOP2lxUZD6G35mYkLgAWLlzIgAEDMFo35rFoMjYQJmbXRTk6XZ6D1jCudtvWRgydksrRWkdtYxJPc1Vu3mWEEaS1iQUvKBr79fFJ2x+5Y+yWsI8cdHpviy4NK9Bpai/3unXiNgzDaEnk5eXRDS0HRUUFKHfA10f0jeZWES8q0ghXzqtQt6SNKN7fhxSNR1fJwcig/RHqwPQMMrRPR2FRO7l9/Aqtupe7/V6AlrC8FyUTGddXAm+hsKGfu2M4Hi3vfea281fDbm67hejKm4muWJuQMIqSTFQEbs53URDwroQhYgXA/7ltvKgAXQ2PB95DAsFnP5YQXlnrEhWvArNQB6vvu88ui1BU+GxMLxy8x2gA+t/58R2Jv5L7z2888iL1cJ/PwIEDWbRoEbURi8X44IMPan3daBmYsGhSvgSeq+P1WcihOhKdyi5HawvHo9NIspKvf0PrMe3Q6WhFwuu1JVhXIaMe5BmpK9zI7zfq1WhIeJJP2n6R+ChTzyB0evo3uiRchd7zl8hbAb4T98CBA5NsbxiGYTQnvvjiC1Yh4/JCQlFRgc7qbdzzV6HwmW7oCuNFRZobn4VExQyUQ3Auqjc4Ai1FDXRztUEG7bGE4mId8Dkyqs9CIVC+UHuArqjnI4+DFxd/R1l/P3P7CZDB3BMZ/D9HGYEXoNyIde4Yert5ZiGB0CHyHgJ3PMlExRTkqZiECrHPQEtw3yMUKbujECbQFbsaxSicALyOluMSxcUS99oQaoqKze5/kO7GvETocfCiwpPh/gfjkYfnJ+6z9sKqmprxD0+7/ZS7zzCX2sWFL1e77777mrho4VgoVBMRBAGxWBpyBB9O+HOO8iTxZWknobSwv6HUsOvQOkMOMspXoHWJP6LyrkuRc7kCRVOmkNwL4Ss7VbrHPrm6muSCYRM6FaUkvF5fSJSPLl0J3J3k9VR0OnwYrVed4o5hOXI6P4/vxF3XqodhGIbR9MyePZucnJwtPRtOJjSIj0Yr5RegVfgUZMRXAbehq92rkbl+iVb0zwIOQle3k9FS00PAnUgceMa7+yeR0b4MrbKfgAyfNHQVK3O34934m9FVNg2tzv+U8EoYI8x0PBcZ5g+7bX6BsgS7oyW6QpQE3sWND9xzk9GVMCoq1qAWtLugEC3P7yLHei6wG0oaD5B3xnevOsKNf8zdT0QeGFAye2d09eyEAou9qMhBn7fPCbkHWQvnRo7ZWwK4+zZuziw3x2L3fGd3n4Ku4g+hkLE1KIPyKpRPcx0SF9GwKJ8EfhgSOPvuuy/vv/8+++yzD0bLwzwWTUoFKg3751peL6FmWdpdUMTl4ygi9FPkJD0dRYymIs/ACPdaJnJCzyN5KhmR53xS9ibi1x4Sx67n24UjbUKnsZ3QOoynGp1+5iIn+P5ofQP0/v+AToFnYJ24DcMwmj+FhYXk5OTQC12pzkBn7nLiRcVp6KpVja4O3huxDBmaIFHxkXvtBCQquqBcjAnISP8PMvCj7VQPReFCa5FwOAl5RDoSXu3S3a3ajRmPrnAnEnoqfHH2KItQAvVh6Errl+XeQKFaAwiLwwfu/aQhwVSKBI9f2c9CXpRlSCCBsg37uucHIA/GYPeaz1KMlnM5AshDomGDm3ODO84i5EGZjLwhXlQMQh6QI9xci5B18SfCZUL/eRa7+bq6WyqqZjUdeW7WuXFeVNzn5vatcV9w7/1C4j0XtXUIN89Fy8U8Fk2IvBYxlK7VG/2komRRe1nawYRJzRvQqfZ8ZLCPQnUuXkXrGwehcKIlyIORhdZM2vkjcXP4SMki9NNOIT5BG3TKrSR5Olp9zEWntPuQyFiHBIWPQF2E1o6moXCpDqg6+CXoszkHOZKtE7dhGEZzJdrUbjm6Et2BxMVE5Fv3ogJk2Feis38q4Yr5bWiJLAuJijPQFaofWilf7P72OQcPuvvz3XwVyNg+w82xAl25EkVCOhI1L6Mr6W/csfkV+MR6jIvcvuciL8TRSOS8gZbxhqIcCQhFRTkyyH/i3uMj7vUL0ZXUJ18/ijwPGwgTzB9Foukit/1wanbBeg4lVY9H4VoQZnCe5I5rlntP3VCyu39vV6LGfT1RONczbrs/u+P3oqITWqoMkGeh2I3/ComLPdy296HlzUuQeBno5i8hbNl7PRIXOdTsEA7ybOy7776ACt6omqbREjBh0cSE4uJv6PTzR8KwqOPQz6+QMIG5xgzoFLMLWpcZjU4VI1BE5s1IWOSgn/U3btwqataNaI9CpmajtQXvCI16L1agaMs06g57SqQSeWfaonWYOei0loaa8bVHp6m9UIL6PDf+VXRa7I5Oh7cBZVZZwjAMoxniRUUu8hi8QljZaB66mk1CBrO/wvisvTQkBjYRiotHUciTFxWgUrEBWi6LEYqLAmTcH4mMZG/8j3TbzXb3Pk/B45ezXkKG+Vi0hJZJ3aLiNWA/t++GiIpO7nkvIqLioso9vwCt+B/o3jPoCuiTsF9G+SdRnkOhVEcgjw7u+MvQcuHZ7tjuQCLqESS0zkBX5klu3PkoCf5SQnFxMbWLipFI5BQij8pN6IodFRVdUMB2BQrHSkHLn5+693QsyleJMsG9di36vEeOHGniogVhMSXNgCDwBvudyHH7JDol+AjIZGVpfV6EZ4l7PCjy3J+QgPiXe5yFUrhGoVNCf3Qqz0Snm54ouvQNwlO9jywNkCipIlwPaSgVyPn7GhI+lUgM9UOnJd/a6L+ohsSeyBH9V1Tv4hZ3jCvR6bGShQsXNvIYDMMwjIawYMECMjOT5f3VTmZmJtOmTdsiKi4ivrN2ATLeOyPD1GfZlaMrRCpaXvLeCy8uHkBCoYSQAIUH9SM09KcgY/VQtAS1EBn/vdBVbW/CZbO5kbnKkH/8RVQ6dSK64iWrvej3NRcZ0Iej2ID5yPc+mPpFBUisHIuucu+h5cNq1CMjGxn4m1Dysz/GSe75uchor3Kv1SYqfMCy3+/vkQjyn/XtKHNxErIeLnDvpS3qNjUU/Z+modiGDGqKCl+CJRt5Jd5xn9HF6P/TBVkga9Hy5o/ce3zAzXGGm8c3BgRZAW+g5cdzkIXQDYmLWbNmYTR/TFg0EyQuSlAdinNQ2tdl6Kfsy9IG1BQUHi8E2kWeG4BOF08TRm56L0VbJDQ6o9N4GjqVnYBOBa9F5okhg34tOkU29GtThUKfKt17WEoY5ZpNGE4VoFY/a9CpJ0o6OgVPQ6ehrtRdbtcwDMP4tixYsIDBgweTUVra4GaksViMjNJSjjjiCEYSX1I22lm7ABm2KciXfiehoesrJ/mcBy8uBhGW9agi9HLECMXFg8iXfRDyhJSjgN4KJCg8ieIiUVSMc/vxeQ9Rb8Ui4kXFOJRsvgoFK3sD2GcyFpJcVPjwpERxcTVauhuBEsEnIg/Kk+59pCHPzbko/+ISVAWqIaLC8xgKcl6HlvouIxQVp7kx7ZFXpDNh/sv/3JzJRAXu/e6GrtC90VU+DS2X+spYG1H25LHufe6H/nc93ecGEhUr3fMb0HfmBygrMxsTFy0FExbNiCAICIJyZMR/jhyLFdRdltafYv1peXPC6yehn/stqC7DBuKb5nVGp/C26NQxjDCEaiNhVaZVKGQpE50uN7tj8zU+fJpXlXt+M6HYKUMF6rqiNZ5uhJcI3DHdgsTUAJILp0wULnYJsLN13zYMw9jGeFGRiwzVHEh6ri0qKiIjIwPc6znIMM9F/uZTE8afhYTGCEJxsR5dZSYTXr086e65SsIsvCzi6xD6q8SbyLhei0RDDF2pfEHy9xLm9uKiALiXUFQchUTFZsKwH88mJCq+IF5UbHTPd0dXzg5ujseQh+B94o37Re65Re6xFxf7oCt+T+QpCNzx+AZ2JZHP6EQUqjUN5TJERcUiahcVnsfc60tRAPWvkGDxPIXC1ya59zgSXcmfJz78CcL/gV8ePBOJlHSU2L7JHcdO7t6XZjnPvddKwpK1XlSMQN+hkeh/+yzKYbkMfb9MXDR/TFg0QyQwAoKglJycwejnfD36WUIoJqIn/H7u8YIkM16A0tFeQKexF5GxH0OngAz3uBKdlv6EfuJ/cfOVoFPxzsgj4vMrKtEppwydjsvc48rI3GmoW/Y8lI7VnTAp3Bf6u869x9/6T4D4qtj+uRharzkFyDJxYRiGsY2IiorEMKboubaoqIghQ4aQuWHDFlFxEjJEL0JXkFuIL7ExFRmSRyDDcS4SChnI03AX8VQhg7MSGe+ZbizEX/XuRXkD+yPj+iV0tSpGS1Qj0ZXsw4T5s9BS2aMoIHgCurqWu30lJkb7gOEn0JXoZ+759ihkZzUqi9IJCYcn3T6ecDeQ0b+QsIneIvf8myiU6sfIoPa8gioujSM++Ph+FHLUBnkJvKi4A/0fnqF2UQFwK1pG7ITCxnz+RhUSFT6H4wj0WQ9Hn2Mm+i74/JTo1TkaMH0mWspsg+o5eoGW5fZZ5N5/XyTyStH3w4uKbDfXHFReeDOyHk4mrBhl4qJ5Y8nbzZyCggLatGlDdbUvS/vPWkZ2RD/VWWj9JZGT0DrHVUj7d0P1GUagU+tKdFr9D1oj2oiqWndEosQ3w/NthUDrSG2JP9Unrildj1LRfoicmtEaG5Uo0vYp5InYJTJPspAvLy5+j5yzn9TyWRiGYRgNJVFURMOYQP7mWCzG/PnzGTJkiMqFIoP4SGTg7oxKr+5EWJ3pPORrr0D5Bz7EKcvt4ywkSG5x439O6APfgFa8o52gIbwKeFGxBzK+/bJZV3Qla4MM4TYo9OlDQkM23x3nySis6HHggMi+vChqQ7hcloPKqdyHcgiudccxxI0tQl263yaszfg2SpReizwRfd34+ci4/i8SCYeiK6S/Mr6CxMlhKJncByvfj8LHDkBX86eQONqI4hsKUJhTW8L/XZRbkZA5zX1+r7njOgMFTE8jXlT4K/lwJNTSifdSRPGP56Blzh+7z+MKVBAfJOZ8Z61qwjK6Ve7z8qLiBZQzcyr6DtyIgqSfcvNchyV0N2dMWLQAqqqq6ilL69kLnTZ+TU3nMkhA/A2dSt9Gp7XH3WtlaK2nAiVTn4Yqh9+C1n/+QJi+BWHPUu/MbINOvwESD5tR8vVk5Oj9O6GDLIZO79chR+epyHG6Ank0/JiouIhWp+qELz8bi8VcfophGIbRWGoTFR5voN4AW0TFgegMPx5lyq1BZ/cFxJd+XYjO/qPQSneWe20sYWWkp5Hh+hAKedkJiYoN6IrTgbCPhL8irEZXlnaEomIiWroK3PO+fK1fYffiArRMNgwZ1T2QERtDxrCnGl0NKwgrV53t5r+fmuLiHbRMtj+6ohW7Y56DDONTUXUk3Pj3kEgYi4x531fDi4px7jOqcPue7OYfi5KrPbeifI7ehHksN7nXzkoY9yYSKwNQ8vYv0RX+C8Ju54miwl95o9mbJeh/mSgu5qKlzTS0VDjYHfMVyJNRRNjYbzb63APkERlKvKgYT9i0EOLFxQy0FJqXl0dFRQUNZcaMGYwePZpYLEZ1dcPK1qekpBAEAdOnTyc3N7fB+9qRsVCoFkJYOepvyIOwPsmoH6FT/BtJXqtC1aa/RqeffyGn55tIANyJTptZyOjvgn7Cp6BLxOHoEuDzJgI35xrk1J2DLiOF6LQ7zs3ZG61b+UtKudvvT1C06hXoFN0drWdE60Mknrb8iSAFXYL6aVQstuU2YsQIDMMwjPqpT1R4foZi/n18/WZkyJ+FRENbdPaeTyguJiBD8htkNG5GK/a5yLD/AJ3RJyGj9SRqFxV+Jd8bvF3d+GLUg2Eioahog4z0VOLFxQh3bEsIV8fLkbE/CSUm+0xGH/fvS6ZkuPdY7MaehK6cF7vxL6Olut4obOc/yHifjoqmz0JX0Tvc+NeQENnNfT6+VEqiqPAF2R9DIWOjiRcVw5DHpp37O5okf1Nkf4miorub6/fu/b2MBFYyURG9B4WbXeLuo8xx+81Cn2kpEipj3fzvus9xhDvWYeh/5BPBU6hdVJzm5sxHFsMrOM/HtxAVXYFOW8r8100sFqNLENAZGD16NDNmzGjw/nZkYoEt99ZLSUkJnTp1ori4mKysrPo32I7ox5AFW2pvTCBc6wAZ8V8iT0RHdHpcR1ifYSd0eo/yJTrFDkKn1kvd2FOB36H1gXPQT7kPOoX3R5Gf7dHpYRm6rLxF6HkYjKIkv0Kn5e8Tiod93dw+/ClASeLr3HZRj4tfpypHp7gl6PT7CEpBSyfM2/DFC1U5yr7ehmG0FAoLC8nLy2PTpk0N3qZ9+/bk5+czdOjQRu8vMzOTzNJSfoJW35NRhfzK1ejM/TEy5I+MjJmFDOhKFGTbBRn/TyFxcQUqE+IrPs1DRugNKEzoXCRqArQSnY780sma0+HGLUJejrdRKNElhL5zj78atEWiohBdcXLRFcZnCKagK9fzSEAdTni1qXLblyFPSbU7vieRF2GE22d/t+1/kGga7uYrAGbPns2IESMYiQKVl6OqSMehZcCXkNiZSbyogDDkaxBajstBomguWvX3iei3u/e1F1rdn4WshJNR4HAOugJ7UfEqCqHKRrkd2aifxukkFxQgw/8ZFAa3EAmHI6kpKlLQlfwpVFp2H/Q/XogsiMGEpV+8RVXs9j8QxUdE930X8qy0QZbJLBp3bfeiYjDytvlQtCV1zBOLxRjkxi9w4xfDDuu5aIwdbMKiATQnYQG4nIs26CfZD52KvDPRJ0Efjn7KJegU2RatVWQkmbEc/XSy0JrLV6jg3cfIaXsOWuv43M39JTq1diZe1PRGVbyPR+sR1YRF6m5Fp8E2KEr16CTHUY0uQ6nuWKNUop/1v5FDuysSJ11R5GwmOk0WolPcq+44Fd5lX3PDMJoz0W7VX1O34TRz5kzy8vKorq6mFzJU586dW6u4WLp0KX379mXGjBmMGjUq7rVYLMZI4rsfg87+uyLjuBoZrcMIw2AS8eKiFF1l1qK+Ej9AVyhfygNkNB6JloXORTH+MXR1WEt8QzY/3lONzuzeSP0M5RnsDVye5Lg2o1CfVcgI7++Owx9Porh4Dl1ZfurGbERXsVJC78V6t+2TyD/fGV2Z9nRjTkGBxouRqBg+XHWUYrEYQ1A/itMIk6yfRiVVDideVKxC4VkVyMPxIVq28x3NfY8OkABJJi5GoKXE5Uhk/JpQVAxHwuZ6JAR6U7u48KLiB0gcvYwE0Z6EVZ8OJbzqP+6OaV8k+nzDwvnuuIcSbz3gPvtEj8W2FBUnov/rPDdnAcnFhRcVx6PPbDHyAM1gxxUXjbKDA6NeiouLAyAoLi5u6kOJgy1L+R0D6BRA9wB6ur93DuCPAcwJoDiAoJ7b6gAKAljhHlcF8JsAMgPoEcBuARwSQHYAvw/g4wCeCWBqAO8FsM5tUxVAdeTmH68P4LIAegeQFcBvE8b529oAZgawMeH5xwLYL4AjA3gpgLIASgP4wm2T+H42B/BEAPu6/dlX3TCM5sncuXMDIMiF4CIIcty5PRkzZswIgKAHBF3d+Fw3fu7cuTXGL1myJACCnm7MjBkzaowBgpEQ3O5OoKdBMAKC4yB4AIL/1X8BCQIICiCYCsE/IdgfgisgWAfBBgiqI7fjIMhz+yuDoAKCcgg2Q7AegpUQLIGgJLJNAEGVm+t1CD6BYCMElRA8CsEECP6acDzl7rX9Ifi7m3eduxW7/VW7+2K3v8sgGArBze75byCY525+3uUQFEKwEIILIBgFwRz32r3uswSC2bNnx33GORD8BYIVkbmq3Hv4xv0dPf5iCG6AYDcIznavvwfBUxB8kOTz/437v33iPlv/Perh7kdB8CcIprj/1ZMQ/My9hx9CkAHBrhA87/blP/vnITjDfZYb3edaDsF97v/4G/ceqtxneDsEe0Fwofs/rnPzVEEwH4JXIJiVcOx+X89CcAoEj0Fwp5t/DHX/Jmpj+vTpARAMhuAPkf9R4N7T4RD0TZgXCAa570H0GKdBcCQE/dz46dOnN+pYWjqNsYMtx6IFE0TK0u6zzwi0lvE18lqsROsXd1PToZmMrqiux0q0pvBnVCPi9yiisQ3S6z9FuRxtUb+L8cjRmUnNErhRMlAux5+Qs/xOtE6VSJabO5prcReKLD0crYMcgdaL/Nc3WRJWOnI0v4K8L92tPK1hGM0O76mor8wryFORm5vLSLSSPQCFk/gynMOGDaOwsHDLeO+pGInKko4CcnNzmTlzZty8QRBsics/EK3kHoU8A0+6+4YU98xBZ+O5KNzla1TVxxcmB60CFxKGP/lwp4Awp8FXZyp2+4awAHkFWh1fi/ITAjfnCSgE6Ro3vgKtsj+EfOUPuvfShjDMyld88h6Mx5CHJnDb3UPY8dtXfwKtvmeiFfY30Er/EOSLv44w/CnqqRiJ/q9nu/2ud/sJ0FXTF2L3lLjb8Sgo+UO3bX90xY02/gOFZb2DPAh5yPvkS9V+A/z3v/9lJip/+xX6f76KvkcXuM+ov5t3Ksr7CJAn5RlCT0UqukKnIGtgf+S5uMq9p0cIPRXnu/F+fTtA39khyFs1O/K8ZwLy2tyHqkltS0/FsMjrR6HA8ZHIo+PzNKOeimjG5jjk1ctFcSKWc1EH21nktAqaq8eiPgC3Wr+vW73fXM+C06YAHgxgr0Aej/PcNh8H8lr8JoDFzkuwPKjpnUh282P8PqoC+HMAQwN5WH4Xea00kIflzQBecY+fDGBMAHcmmbsuj0Xi7V9uf/aVNwyjeRD1VNybcNKKrjgHQeipGIlW09e6FeN9IPiV2z7qufCeipEQ3OHmvButWkPDPBcPuZXno9xqdUE9J9oX0Ar479z9C24VuhStvr8LwUAILib0VFSj1e4ytBpe7MaXufe5vzueSrTyvQR5HmZB8AbyGlS6ea6GYBwEb0HwtFthHgHBoWg1PifyWQTIO+I9F/ei1foxaCU7z43/dy3v9d7IZzmceM9Roqci+plWQ7Ca0CPjPRZRb0Wxe32Ne7wGguuR5+J0CJa6bf14/386xX2m/viix9OhQ4egu3tf2RAcDcGr7vM7EILR7hhLkLfgTAiuI95T4f9f3ptRiTwX57r3eCqhp+JLd5yL3fsJEt5rkdv/LOK9UtUQzIbgcve59qF+TwUQ5Ofnb3lcl6ci8eY9Fz0gGEBNT0XibUf1XDTGDrYciwbQ3HIsGoNWvPyaQWI+Rju0FuDzEl5BEYTFaH2oi9tmOVp/egqtQXVDazR1eQCi5WITozVLkdejyM29rzvGlW58NVqHiyEvTC7K0RiUsI81KEJyJGGfjdqoRvkhdwIllnNhGEaTkuipSFaR6Q7CWHnQme4ctPra2T13vxu3F8qJuA75lv34XyGfreceFFM/E+JyLvyq+tkoKTcLrcpPRp6M7m6/o5FnIpEX0Up3N5QbMJEwwTtAMf3laIV8JYrlP5XwbO89F96L8DCqhFSEEnrPRavZ7ZGHoQ1aiV/kXv8ceSX6oyvb+24/fdFVbU4tn0kpytG4GV3lxqPV983ufSTLP7mP8HP+6quv2GWXXbbkxiTzVCRuHyCPy0bkPelIeKUsRZ6KjugKjPs81rnjfAD9n69G/6Nn0f9nOPrftk04Pn888+fPJzs7e0tn7++h//Ul7ljOR9W/yt3tJWQRHIYyItMJPUuJOS/lqELWS+7xFPdZdnb/g0qUV+FLBvs8nXz3+n6Exew3ogpWVe4934T+f6sg6XU7FottyTPKz89n9OjRpKSksHMQMA79XvatsVX4fwjccd+NvtsnEO+pSKQa/SYfRd+9lY0oXduSseTtbUxLFhae0KXeEZ16opWUfNft+EpK4TY7odOuLyw4hPhGeEn36O6rI49nI0fpJyh9aj1hhfJUFIo1Gl3WeqGUuzno9LOWsJJUbzfnV0hcjKJhlZNLUDjVJwRBeX2DDcMwtgt1iYpKlDjtjaFbUZWcnsi4jooKz7/Qss/e6Gx4LmH50V9Qk0RxkZubS44bfxZazqlw+2xD/eKiBCUFV6Az+UTiq0aBDLiZ7nYTCpG6AIWppBA21QqQULoJVWHqgs70fZGReKo7pvZu/CJ0VXkTlV6NIaOvFC1FlaKrSBAELFu2jF122SVOXNyDjPDFKDj3TUJhFgRBDXGQKCp699b1KD09nfz8/HpFRfTziIqLzUgwlSAjOyoq/Oe6AYknLy72RAbxKOoWFR4vLvqiq/omZAmcj5oTggz6MndfTBjk7Ev4Ji4TVrl5UpAQvN8d/73o+7KJUKSCkr6nIjGxi7t5Q76asITLLBSg/QpKbq9yz0dNVv8ZH4ZKxMwgFBd1hTX5z9/vL8V9tqmE36tkVCEBfRu1J363Vix5exvTUkOhamPQoEFeqAdAMGjQoFrHasyYAL4J4PNASd6JIU7JQqCCyLiiAE4NIDeA7wdwRQA3B/BsAB8G8FkAUwL4P/d6bgAnBvBGoKTwskAJ20cGMDaAp928BQHMDaDSPW5IfuETAexUr2vVMAxje9GuXbugFwqfiZ6gKlAS8gAIjnWPfwHBHhDcgsKfEk9q/4ZgPAQnujCULi4s5TYU4lPbyTAaFjUChdqsdvtIDLUJqD8s6l4UtvSHWvZXAMHjbr+PQvA9FJaTGAJ2BwrhyXY3UEhMP/e+biEMmwkguMcd/84Q/NR9dv7a1p2aYTRfffVVgJvroshnAGGCe2J4S3S8Dy/66quvav3/tm3bNun/N/Hmw6Kuh+AAd/8NCi8KUDiYDwvz26xBoW8DUKhPZ/eZ+P9BsnCsKPPmzQuAID3yPal02/uQtGgi/WZ3DAvd3z4Myh9/lXt9HUqsv90dwz4QfIzCoHyo04sQ/ByFc+2FQt02ER9W5Y9lNgR/dP/biyC4lfjQQP8/8SFm0ffuw6IgeSJ2dH8VCfut7VaJEsuPIEz43lHCoILAkreNeigqKiIIgi23oqKiOkZ3ROsBa9F6SucG7iVw9w+jNYNvUFrdZORs/KGbdzRKNZuIigVOc+NWo34aT6J1BJ+4fRjwF1QsrgJ5NnzYVUPckWFjPcMwjO+a+fPnk5KSwnKUPOubmFWipNXFqAnbfFS+8wOUUH06OhtHuRY1HtsPrXwPQGFBp7mbbxCXjDNRaNEQ1PDtp2iFupj4FWbPSYQ9AL5EPmffbWMRCn35GQoHejZh201u/GZ3XGXIO5CCelnc78bdiXzj7ZF/+0zkGZmDVu4L0Jn/Tjf+PuR5mY1Cal5zxxIE6pS8MharsaLcu3dvvvrqKwqQZ2Mm8NlnnxEEAd/EYklLiQZBsGV8oqciGfn5+SxHq+j31TpKV64n0XfgA/e+nnbvdTOhpyLa1WkqShj/Gq3gr0NerYup3VMRZcgQpaF3QQnZP3X7qyQMSWvr9unDzh5G//973DEFkZvvR4G7P8PNuwwlk69yr3lPRXfkRRqDPHMvou+D9xz4PiSLgI9QXMP7yFsVLWqQ6A06nbCIQV5eHtOnTycIAhYgy8En8XtLwe/PJ6L72I1keE/F7W6Obm4/o0eP5oEHHqhlqx2Y7ShwWg2tzWPRUHJycgLoHMBDAeQHsMSJ94Z4LKoDuDaA0QFcGSjRek0QlrStDqAiUFnZZGVq17vtcgP4W8Jrd7p5b0w4nsrIMdV1+10AHZv64zUMYwfDrxb3jNhmfhV+Akoevs+tqN6BEmq7uFXbf7gVY7+a/W8IJkLwhHs8FYJjkIfjSbfi61e8k3kunkfJtje6VeVilGi7grAMa3T8PchzMsHt03ssNkLwDgTT3WO/Kj01YfsCt92lEOyHStKWuTnzUEJ3LlrJjm53UeTzmjVr1pbPLOo9mDVrVvDZZ58FjTFpvOfis88+a/A2ffr0qdNTEaWgoCCA5In5/hZN0G/fvn2Ae3wbNT0ViavyQFBYWBgEQRD3narNUxHFFwIYBcFd7n8fTc6O3rwHqXfku+o9F75Mb7E71jI332gIDoFgBkrQvsd9J36PSg3f7raf4r6DTxEmnQcQvOyef9h9FqMg2Ne9drb7jC6v5TONfkaTJ08OYrFYAKHnooCaHopq5KFbhzwz0UT6qKdiAErcDtx77OX2c//99zf4O9RSaYwdbMKiAeyowkInr+4B/C9QGNSqyO/Xi4HahMWDzvi/wz0uC2B2AF9Ftql0wsKHMiWb53YnLu6OPLc5UI+OfYOaYqchIVEPBdApGD58eFN/xIZh7CB4UZHYqwII2jtDxYuKSne72Rlzg5xx5cXF/xEvKqrd80+hfgSXu79rExdeVNwTMQy/csZVGWEIjBcXtYkKv+8FSFwsoW5xETWkfRiLFxf9qCkqEitdeaKG9KxZs5rwv1o3dYmLxKpfHi8ubq/jsygoKKixr7S0tAaJCk+iuPD/+2SiYm9UgWssobjYQLyoCAjD63aG4Bn3vO9pcQrxosJ/b+6E4GRkvFcTLyq8gX+7m3cUqlL1bwgmu+9sbeLCf7ZRET8QVSxL/P766mBPojCtRwgrWCUTFYeh32tU3LZ2cdEiQqFeeuklrrzySo4++mh69+69pYbw0qVLt2re8vJy/v73v5OXl0fHjh3p0qULBx10EE899dQ2OvIdDZ+kHUO1NhIJqJnEvQjVTZiE0uNiyHHbBujhtiHJdsn4OQoQ8PVBvMP2QuTM9VXL/TH6c0hdqOfnnDlz6hlnGIax9fik2cReFb5P9iAU0nECYUBnCeob8EsUepGCEnePRz0Njke19QLCUJajUajRXNSRugBYgMJpfFjUCyjR+3tue1BoSqa7pblbhZv3XnT27Y3CYQYQn7gdc8/1dftaiiorTUThIz4s6kVUtuNAt73vm3Ef8B7wT/e+PPcRhvYkdhUPgoDVqanMmjWLESPqquHTtOTk5FBQUMAM9F7uc89Hq30FCaFaQRAwC302Pkwu+lkUFBSQk1OzLldZWVmt4U/JGDVqFDNmzGAmCkd7gjDMCdQ96mYUFvc/lMh/Fbrq+iRtCEO17kVhaRtRMYGn3bwfoI5X64FD0BU9BYV9nYTC5I5AlaSuQ71EDgN+QliSxffkKEbd1vuhoOyetby3k90xjXLz+E+rFIXLTXf7DVDSdjUKW3sVBWa/hsKn1rn3MQdZLy+44/ga9cC4ljD86rTTTrOwKM/2Vjm10alTpy0qMnpbsmTJt55zw4YNwX777RcAQefOnYNJkyYFhx12WNC2bdsACH79619/q3nNY/FGoH4RGyOLAtFwqMTbqQEcHij8KQjUH6MgUCJ2dFxDPBY+LGpcAD8NYEMQ9uN4KVBi+Xz3uNK9XhqZN9mCRn6gDuXmsDMMY/sS9VREV63LIBjmVmHvQHX9F6Nk1jVo9d/X/7+HmonZvt+DT66Nhs08i5K/Ez0XU9zKsfdUVLhV31VJTpRlbjU56qm4nbA/ReL4ZJ6LG5Dn4hG33RRq91zUtjqfrJt4SyPquYh6q+rCfzbRVfFknoqtJeq5uAet1HtPxf6R/0kVWun3notc938rJfRUpES+5w+jZPp7CEOM/HfU///bEoZ+PYo8Fw8Rhvv522SUDH48KgDwAjW7lPtbBUqEHxX5Xt0MwRAIdnG/Cd87w3sqprjv6YsoJGwaoefieUKPRXfkqYj2QlmJPD6+CMCnn366zf9HzYEW4bGYNGkSf/3rX5k2bRorVqzYJnNefvnl/O9//yM3N5d58+YxZcoUXnnlFT744AMyMjL497//zQsvvLBN9rUjoFWRcmCheyaaHO29DYnegdlorex81DsVpPvbUjMdsKGei45o3S4frcWlu+cPRgXp7kPejLnIW/KlO+a5KNVwOUqF82xOeC+GYRjbnkRPhS8rWwVchs5E49Dqfsw9Xo5WVrMIOxaf4bYdiTwSQ9w474VoQ3yC7wS0SjwTnXlnoxXmF4CDkKciy22X4eYoTTj2R1Ap016oJOwa5HEYgpJwX0wY7z0XPZDn4hG3/51RsvERyIcNWoH2ibjec5FsdT7RU9FSiXouHqVhXaSDID5hvDZPxdaSm5vLMPQduBH4DaGn4r+RcSmoV4b3XHQiLIV8PeqGVYK8bze6+f6N4g02EJbP9d6aJei7NM/trxj4B3Acsgx8QvjD7vUh6PvTEX2XkhmvlcgqWIssEJ/YvYfbNht53oYT76mYhn6DI1BC+TDkEXwdeVp8+eZ05DHxJZyfRQXwO6LSybnA7rvvzmeffZbk6HYcmkxY3HvvvVx22WWMGzeOnXfeeavnW7t2LbfddhsAt912G926ddvy2u67785vf/tbAP7yl79s9b52FAoKCpBzdB76qW9OGJFMXDyCHPc/iDy3AYmKxK9bNWEIU30cii5Rj0W2BTn0P0Uipje6tA1y973RT349utR9iYRSIdA4t7FhGEZjqEtUXIvORnsAb6OwiwwkDiqRGIhWil+L+kI8hColpaHeDlWEDcqinXmmoLPiOBROVYbq8fUDTonMHUNn5gxkFHpxscrtqxIZZF8hUTEetRkdT3JxMdvdPkXhMlWoHuB4QlHh8eEtPQnFRbSyUWsRFR4vLla1bVuvqPAEQcCqtm23m6jw1ZUuIhShT6Hvw3+TjUdGeQoShh3Rd60MhRy96Lbvj8TDy+i71g59v+4mFBW7oF4a5ej7fRMy1H2r2yr0HfSi4mi+naj4EC03HosqS32BKnElExVLkABajITEj92YYlTxbBSyJC5zx/qCe+4NFJpl4kK0mnKzL730EuXl5fTr14/999+/xusnnngiAB988AHLli37rg+vBbMBRRz6NjhRooLAnyg/QZGU0dNDGcnbzlQT9vKsjzQ37xeE63QB+lmvQqevzm4/6e6+M1pvG4JOY+XotJAPVDJ79uwG7NcwDKPx5OXl0QstiZyKlje8qPgQxZf/AJ1hH0JGWWd0ptuEDKTA3ZeiFeJO6AxWicq7ZqAzaDVa9ilHht6jyMg6F5WjTXf7+wp4PuE4k4mLLMIY99tQ/Hw7VCQcd58oLmYh4/S/brz3VycTFSDPxOsoXn3+/Plxq/OtTVR4cnJyqKioaNQ2FRUV21VU+JKtaSiX4jzU7HBusm3c8yvR9/kclM/wZ1Ty2DezewSt7PtSwm2BZ5BIWEgoKrx4+T83740otyMNie2bUe7O1oqKgchbcRwS2tNQ2eJEUTEICYMBxIuLN9Fv8Tz0u3sIeWkmuM/vx+69d3LH0ROJix2VtvUPaRl8/vnnAOyxxx5JXx80aBBdu3ZlzZo1fPHFF3XWoDYS+RIJhgMIqz97fMI06FSznPjUPn8SjTrqQZfYgLBqdX3E3LyPI6d8ZyRehrh5lqLTQDJS0E8+A50+XsN3GTcMw9gebNiwgVgsxivIOBqPjP7PkJH/DVrp/AdKcL3bbTcJGUhr0fJJBTp7VSFRUYyMm76ESzu+U/IUtBp7KKGoWIUMpx8ig26q22Zi5Fi9uMDttz3ybFQTJhmnIDHgPS9eZExFZ98uaNnnVSQwKlESbm2iwnsm5s+fz+DBgwmCgPbt2zM3P79ViormRG1dwdOAS5Dgm+meG4YEZxbyRhWgq+8EZBmciAzvtsBeyFivQiFMB6H/cz666s5GfSyiosJ/FyphS9J6IUro70X9ogIUmlWEQvxqExWd3fPj0ff6OeJFxQAU4wBhp6tF7v3nueP/K+qt8Q3qct8dWTCHufHXo+/019Qf6taaaTXCYuFC5QH061d787M+ffqwZs2aLWON+gmCgFgsDa07jEb5El39q+7eX94WuPtBkRl8yFIK8Z6JSsKKUwHxVZ1qw8+7CJ3CIMy3KK8xuiZt0GmxBOhKLEnzJMMwjG3FlClTOPbYY7kHeSE+APYhFBUnA0e52xVIXMSQ0b7WbbMTtYuKGOGSjfdUDEeekKio8EGfR7r7qe5+YuRY/Vm8ElX2ibnjqkBeC1/ZCOLFxWtIfIxBBuQM1IR18ODB3IeWc6LGazJR4dm0KdErbmxrYrEYvZAxfFaS11OQMPXi4h1kTO+Jvoud0PcrQEZ7FVqlf9Nt3wuJyxXIi+abF37tXk9H33uI/y74pn6xWIxitIz5oJunJ7WLihXAW6gh3xsoh+PHSPR0R2FZnd1Yb2lMAPZHS5SLkaCIoba8We4Y+7ptHkMN+k5HHp3l7u/ebtuhSFT3Qb+BQred/w3siLSaUKj169cD0LFjYm/SkIyMDABKSkrqnKusrIySkpK4245NBVpLmIx+xlVJxsQIjfto2JP/ikWTpX1Ru1TihURdRn5AKCKirmTfKzPRI5KMUrQesi9Kh8wiFmtIfodhGEbjePrppzn22GPpjXy5LyGj7DEkAryoAJ09/4JKsd6Fwkm6oJW/TdQuKjzPI09FNjAWGYIriBcVniMJS8FOjTxf4vbRCXkvNqOV4vMJE62TlU19B10dXicUFYMGDdqSfFxbYnaiqDC+G9599916u4J7cfEZEo2F7v5zwuzJGPp+xpBFcBD6vz6HBO1dSFQ8//zzfOMW8YIgYAk182miomIkMtwfdPP3pm5DtTv6LUULATyOficrkPFfTM3lyxIUGtgLWRabkYVQTGhVzESLARvdZ/ACEjyD3Jg+6PfiwwB3B36PwqmGDBlCUVFRHUfeemm0x+LSSy/lueeea/SO7r77bsaOHdvo7ZqCa665hquuuqqpD6PZIK9FDF26uqFK1H2SjPSGf3TVyedalCPB4ftQpEbGJDPuk3kv/M892k9jgRubeDyJc1ajGhWr0KmnB3K2fpJk34ZhGN8eLypykSH2HyQuZiLDZV9kfEB4pqpCserHoDCSo5DRMo+6RcUadGbeBa3Ufo58ysMI+2QkEvVcrEfhLCXorNwVnXkzCM/SfmX7RkJxMQPFlfsKR5mZmRTl5zNoUOix9teOm1AMvRcfJiqajrFjx/Luu+9ywAEH1PBARbkL5UXMRh6Dr9H3rh/h98GLi0okBJ5G3/GVyCu3aNEi+vfvT3V1uLDovxOr3ZyJoiIxPKshRL+fBSg343wUwuRN+2g41GYU/pSJrIkyZL10Rp5CkKB4AVVha4t+KwegEjLF7nPYC/W4mIl+Kz48cIb7DPLy8igtTay31vpptLBYtmwZc+cmS+upm+394WZmKkJ0w4baY+f9MWRlZdU6BuCyyy7j4osv3vK4pKSEvn371rFF6ycUF7ci4/wSFJUYxV9QFqCfNGitIR2JjQ7o8tmWcA0iUQDU5UHwoVZDIs/NQs7OjtTu8ahGp8kpaO0t2z1/EXCOhUQZhrHNiIoKXw3KN5krR/7W/6EE0JPR2cubXX9EwaY/R2EnfhV1Z+JzKjxVyDj6EUqY/Ttaof0JYQnb2jgShXj8HzKmTkMG0xokLlITxp+FxMEaQsMpGkvuowYSSTQkTVQ0PfWJC18SdjYSB7vtthvBmjVbRCLEiwufzDwTeUQmTJjAos8/p3///kn3HwQB6enpzM7P32pR4UkmLs5Gv6NEceHLu3yJfo9dCZc8uyCBNA2J/Alu20EoCNuLit2RqChA1owXFXcgwf01EOyAogK+RSjU5MmTt7i0GnM7/PDDt8fxb2HAgAEALF68uNYxvqu3H1sb6enpZGVlxd0MfwFZhdYmfoJCo6IlaDORY3FWdCuU27AW/XRTqZmwHaPmOlwyZrn5O7nHZSi6cq9at9Bl+WpUA+JEVDTOczRhmpZhGMbWkUxUgAIvz0EBm22Qd2AKMsi+dGP+iMKKfo6qSIEMoH7ImP8qsh9f59+X0jgaha7MRQnUDwIPRMYm414UQlWFzuRPoTPrBre/RKJVnN5++23Wd+zYqLKp6zt2NFHRjPDior6u4P3792fNGn0jagtvu55QVIwdO5Y1a9bUKio8vlP4thAVnsT+KLcT/r4WIO/fOuStWImsg2Xod1mNRMN/UL5INlrCXIeKEywiFBV7I0EyH/2WfQf7ujqq70i0muTtMWPGAPDJJ8lDWxYsWLDlx7Hbbrt9Z8fV2gg9F58CF6JTypEobWkw0vWvostjFXK0V7i/ywnDmBqb21COUrPy0E89C/3816CfdfRHHEOi43V0ClyFTjdRUYE7lnGE6VaGYRjfjtmzZ3PssccyChlJieElZ6Cz5jtoWaYEGfMBMnI+JxQV0bIY3jzz/to+yAjyoqISGT2DgZ+6OVeiFVvQWc/Hl3vuRaVAO6E4+nMIDcUfIQMKwjId0dyIJUuW0KdPn0ZHIeyIISHNnUTPRWJ4WzJqC2/zoqIxNFZUrEA5FXWR6Ll4AVVeW4w8FxsIyyr3QhbLp8gj2BPliIxGfS9movyKEnfvRUUMeTyykaXxCLJ0TFSIViMsxo8fT1paGosXL+a9996r0cvikUceAWCfffaxUrNbif/RSGCsQ7o9HXksqtFP9mm0hpaBRMBKdFrwldfr3UvC49fdHEehtbuF6HTRE/2k56Kf+AL0037DjR+NxEU2yRkJtGX33Xfn008/bcBxGYZhxDN79mxycnLIRbHd49FZKTMy5glk1PwSZXi1RWexm5DB8ktU4jXxzJdMXPR0915ULEIhH+e51+5AK7HJxEVUVLyFzsZ3om7CycTFs9QUFUbrISouEsPbaiMxvO3bioq6qlMlcgdh+GB9489y43OQ2N4P/VaWEDaW7IXyn15GlsMqJFq+h7wWM1AeVD4SFQMJRYX/LeWgKlGvot/3IkxUQAusCnXIIYcwfPhwnnnmmbjnu3TpwjnnnAPAueeey+rVq7e89tlnn/H3v/8dgN///vff3cG2cvQDSkhArQAA2gdJREFUqkY/s25IUKxFl9QH0aXL1zbpjn7OK6gZ9lTfD9FXc8pDTfIGojWCOWgN4mSUtngy8Cd0WtgDOfgfpnZRAUpvTN+hu2QahrF1RJvh/QJlfJWgMyHI6HgJ9XQ4C1VrApW37IEERbJ+D9Eg0f5oiWYBMmYqkTBZhERFtnv9DLeP3sifezNhj4xkosJzJzrD3kEYFvU4KnlhoqJ148VFY/ovBEHwrUUFQH5+fr3VqTw+xOg94sOwauMQFONwDsqz2IB+L0OQODiAUFRsAHZD1dPuQknaWShMMB/FNBxBTVERQ80u/4B+Lw0peL+j0GQei6uvvpoXX3yxxvMTJkwgLU2lQ8eMGcOtt94a93pRURFffvklxcXFNbb961//ykcffcT7779PdnY2Bx98MBs2bOCNN96goqKCiy++mKOOOqrGdsa3J+xz0QW1pNmIErxvQB6FfxCKiJ7okpiK0hGjlZ+izv/EErTXovWEm5GQeQitEfwd/dxnI43ci/jaDw2hHS1QXxuG8R0xb9488vLy2LhxY61jysvLicVivIqMnoPQGa4EGSpvIOHgE0GHoZ4TC1A3YB8a1ZGaZS382fBBtEziKzp5H3BXQtHhtzkD+XBfRPHjN6NlmNdRSMhfSe43jnou5qIeFT7x1URF62bs2LGNXm3fmtX50aNHk5+fT15eXp3VqRLzFpIlkEc5BFWkOht58GKobEwf5K3IQlf9F1CG6Ei0vOgF9O4oF6MnYVf5ieh3mCgq3kYxEf9x82aCFYOhCYVFUVERH374YY3nfQdtgHbt2tV4vS46dOjA22+/zbXXXsvDDz/MSy+9RFpaGvvuuy/nn38+xx133FYft5GMChSN+Gfgn6gPZj66FPZGaYwg8VGJLokVaK2uDbX3sggIqzmdj37qV6Mwq5+ipO1NKL/j2yZhbya+x4ZhGIaYN28eQ4cOpSf1Gwze6LkCeRCOQZ6JN1Fo0YTI2LuRETMG5VwMRqUlEpc4/N4ecNt0QaujoDCNbsigKUXGkg+9ehgZOwegJZ5DkddkDVr6ucXtK5lh5sXF0278ZqCw0PLQjG1PfeIiWTJ0XeIimajA3fu4ha+QtRIjXlS8hwT+0cib9xSKs3gRWSvHRua6FjWgPAn9hvujGIr2aNEgeq746KOP2Hvvvbc8bt++Pfmuu3ybNm3Iz89n1KhRcZ9LLBbjww8/ZK+96ipM03yJBTu6tGoAJSUldOrUieLiYqsQVQvKt+gO/I5QSOyH1rtOQTVP/GVvLfr5t3HbZFIzmXsD+rlPQZfoXOTIX+Xm+x7Sxf2Jj2RuLJOB8xkzZrDlWBiGsQUvKnKRYV5fUivoPOg7X7+Plj1OAy5AhnwbJBBeQUbMamSkdHJjjqdmoKgXFQeiJnq/R2LkNOTtWIeM/zQkNJ5DHopd3f164B4kMJYQGmZ1Jc3eh7wa85GoyM6uK5zUMLaO6dOnk5eXF1dNrb4KS4nf4dpERZQAhT+tQcuU3ycUFccjUZHhxj6PxEVXVFnqUBRadR3wX+AE1I18CfKIvIbExSbkfVwJfPjhh+y9995b+oCA4iqWJ/w9Y8aMLeLC554sd9s3F3HRGDvYhEUDMGHRMEJxERUSE9FlcAQ6ZUxAid7l6KezAQkE36omhhyMtyCn/TB3v46wY3Y/dFrwZWe3hsuAmwgCq1hiGDsqRUVFcc2soqKiNkMnIyOD/Px8Bg8ezJw5cxgxYsQWQ8fHb9+DVjuvQbHaDxCKijXIGDkOdcp+HziXeHGRKCr8xfpy5JH4OQqxWovEhY9X35NQVNzvjn0O8QZabeIiWgHKRIXxXREVF40R8iOR1+AN5Bm8ntprTs5CQuBVJCYGIg9eoqjw+I72ZcgrOAqFSUVFRRf0+2uPfs+TUXC2x7+fp1AQ+DHIC7IReUHeRL+1GTNmkJuby0iU0O6rbTUXcWHCYhtjwqLhSFxkISejFxJvoEtzFfIwHEbohGyL1u0K3e0dVDV6M1rjq0biYzjwMTV/+lvDZuRV+XyHj4k0jB2VoqIihgwZsmVVsbCwsIao8ETFhV9VfOGFFzjqqKPIIayjD8qfeBkJgwrgUtTJeG/CYtknIIOmDAWSfoBWRY9HouIOFPB5E6GoqEDLMv+HVk5/hsTFZLQc8w0yro6idlHhSRQXJiqMpsSLC/9bbMh1ORaL0R3lG40CLiZ5rsYsFAblf5cvomXLc1HZl0TLwudUPIiaW6aiEMeTCEXFINTxfgkqpNAOCYLJKEcpxx1PR/Rb3A1ZPoVoMeEkZIXcgH5zOej8kfhbbA7iwoTFNsaEReOQuEhFl89+aK1uJPpJ34e8DyDnvRcPZSj/IuycPmDAABYuXOjm2wn9vLdlnsyT6DK+2oSFYeyAeFHhVxWnodXGZKLCczaKr94HGQczkUFwHqoe0wFljwXIoH8LnfU2otCLd93rv0CJ2FWEjfMuR12593Pb5aBVzVy0JONFRarb5gq0FDMWGTRF6Ow6BtgfraDWJio8XlxEV0lNVBhNhRcXjbkmx2IxJk+ezEknnZT0txsVFa8gq+RJ9J0/HsVTRLM0vaj4ClVdm4V+G8eh39YylBe1S2T8Yje2I1oIeAeJhN7odzgJeUgK0HJqe+AxdwzXoxCqc5DQ8R6X5iQuTFhsY0xYfDskCEA/tbYoBMoLiY1ITFQAMHz4cGbPnp1kFj9XGlonmMbW5VR4SoDDgU8IAisUZxg7GlFRcREK4LwSeRUOIuwBEeVxtNq5L8pn+ACdkfZHtfBKCMMi1iLjv6sbcx0yPtKRAPmdmyNwz6WiM+N5SHzsiVY4H3fjT0SCxIsKf3b9JfJ+7IHq4T3h9t0JreI2dNU36rExUWG0RGbMmMHo0aPjxEWiqDgAGfkHIWHwUyQsBiJxkSgq+iKrZSaq9LaR0FOREhkPirW4B4mWA93Ni4rD3Jj57pgy3HZ/QvkYv0UezAD9dpubuGiMHWx1No3tRhAE7lZKnz6Z6DL3NbCCPn06EwTlW8bUJSpEtPLU1lKNqksV4IWNYRg7Domi4nTCUIozUG7C4wnbeFExCeU2tEN9H05AvtYpyEfbxW3fFomKLm5MCjIW1iKvxMPIoGiHzkIVKEl0Ikqc3g24HZ31HkNFtpcRLyoWoZCnq5HX4i10hi2j4aICN664fXsTFUaLJjc3l+nTpzMDGeN3ojClmYSi4iIkFi5CpWCmIC/lQiT8k4mKhWixYDkSFN1RCFM58XUs30Xhib687Rvo93ww+k1WIxGTQ5jgfbh73B0tMMRQc0o/7+nuWHOBvffem48++mirP6ftTavpvG00b5YsWbJV2/tKJjVL2DaWanTKeRAosRAow9jBSCYqQDkTOyGPQEfU0A4UKhEVFUehROsKVMIy091eceMPQQKjjPACeyRK/jweJY0uQp6FFLRi2gsZLwtRyNOH6Ay1DoVcfIn6XoDiwQe7OeYir8dSFH4xA4WS5ObmNvpzqatPh2G0FLy4GD16NDchb+J/kUfPWw0pqIA9yNP4FGE52WJUWMGLikWE+VS7oIWAUnSOqHTbtEX9ap5Agd8jUJ+MSUhUVLoxVei8kI2Ew3x0TuiGvJrfQ6Kk3I3zDRdOR7/tlRBXura5YsLCaDGE4uJvaO0uWsK2IZSgtb0HgRXN/sdpGMa2pTZR4TkUxVKD8hxeQsb7AuJFxUqUNZbtXve8jIyCSciYKAV+jFZNz0JhS4+gMKsKZIiAhMdKJGzeRYmehyID5Q5UZWo5obiY4MZ3A74gTP78tqLCMFoTUXGxCnnxUlBYkf/NR8XFDUhcHIe8B32R58AL/YHIU+h/6z2QdyEd/Y5fQuFPg9zYIcC/CL2Rae5WTujlGOrmfhOYjoo4ZCJPCISiAnQOeIWGJ7Q3NRYKZbQo9KNagZyc4wiLwdXFZjfucLediQrD2BHJy8ujB0q8TpaYDTLos1A89X7IuDgUGf8fEC8qQJVlhiNjpAsKR1qPVjR/RryoAOVKnI8MjbZIXDyPjIxPUGWn3YG/o14VZxHmZnhx8QwmKgyjLry4+CYW44svvtgSHnVfZEwK+h1uRkUOnkJhUG2Qp6In8lIEKNahEnkzNqDfeBkKd3ocCYoL0G96JvrNVhDmRFW7eVOR56IalYx+Hi0cHOyOI0C5Up76+nk0R8xjYbQ4Qs/FJ6iOgq8S70vYtsP1i0V5FK+gn3kJUNFifpyGYWxb8vPzGTJkCK8AZ6JEyypkRAyOjIt6LnJRZ52lSECMRMmVVWhVcw066/wHGRnZyPMwEZ11znU3zyrk0ShHORSgsIl1qL7+ENjSVRgkLmYiQbHe3aYgb8gbmKgwjNrIzc2luroagC+++IJdd901rsP3Hei3tsw9V46ERwZaMBjgnvvSjRmNxIIP7C5FoqIjynEC+KF7bhYSLjlIRHiroy06b7yIfvfHIOtlsxsX7c7VEkUFmLAwWij+RyaB4ftgJFaeii9h25J+mIZhbFuiYVB7oZCmM5BQeAetGF4YGX8oqhL1BUqk/hB5MkChD+Pc368QxmAfhLrt7I6Exznu5nnO3Q5G4gIkLlYjn+ooFAYV5V63j2XAlClTmDRpErFYjLUoNMJEhWHUT15eXpy4mEF8E75YLEY3lJORjX7TPpH7K7Tw0Csy31IUEnU4EghFKJSpBGWBliAvSAqhdxMkKl5323wPVY5qS7gcWozExV20TFEBFgpltHCilafGjBlMtPLUmDGDCYLSLWMMw9gxScytuJswkfpBVD72XVRP3nMHMhaOQeFLJ6LSkbciMfKQG9MbeQ52Bv6NPCEZqHKUFxUBEhTPu23fdNsch8KtNqOk7EcIvRIgUXEd8lh4UQE6730Ti5moMIxG4MXFDOQBjIoK39L3Nyi3CeSR+Ap5IHsjURCgsrR9UDPKsagAwydoEaIYeTqOQqGRs1C3btz201Ci9zFuzAa0DOpzNlLQYkNLFRVgHgujFfHpp5829SEYhtHMSJawXU0YfvARKgt7AmFydHtUSeZwtILZBuVA7Ic8He1Q6cp30EX0DJR/8SJwKmqEB8rH2BkJiudRguYRKLTqSRT69A5a3RxI6BFZj0rM3khNUeHxIR6GYTScqOciKip893kI2/RmIhGxBAmM/oT5Fv2R8FiCzhOgHI0sdG4AnQemImHhG1xORaGT3yfMxfJ5FzEUEvkILVdUgHksDMMwjFZKbaLiY+TXPB0lWL+CwpFOQAb9FEJR0RkFVBajakwnIW9De+TFmOvGTEDhDS8iY6QzMlAmEy8qUlGy5t5IvKxEK5e3Iw9FlpujLlFhGMa3x3f2rk1UVBF6EIagwgwLUa5FKjKcv0QiYIB77mjgR8gbORkJkP+6+2x0vshCuVezgaeRZzMDnV8q3X47o/NKDtEmwy0L81gYhmEYrY76REUOuuAPQRf0Z1By9k7I6B+CLvJrURL2UGQE7Ik8GL5D94/d30eh8IZn3f6PRGEPryFjwosKUOx1JjCeMDcDJC5mI0+GiQrD2H40RFR4hiARsdA9DpCnYgAKifJMcPdPIeExEoVVDY+MORRlf76AQqWOdPusQh6NFFTsoQ1aXIjFYi3Oc2HCwjAMw2h15OXl0QtdyGsTFQEyGA5EF/JpyIMxAomJhShMaQAyAjagVccjUP7FG6ibziS0Ank0obgodLcfIeFQiYTFHBTm0N4d25coNOpgd9wvoNCs4cCxxx7b4owKw2judOjQgV7AYdQvKkCJ1A8h4VCKyscOR6LC/zp9+M8E1JfifdQsc1hknnIkHo50+5jq9nm4ez5aFcofV0sUFxYKZRiGYbQ6SktLWY5W/+9AAmEpqk0fFRUbUM7EeOCfyPMwEImJte5+FzcuDQmLaaiQ9XjkxdgfiZXnkbdjIhIVE5HYSEfCogCJii7u+cEo9noXJFL+jspdvoMqVLXkcAjDaK7k5+ezHFVnuo/6RcXNKFH7XrQ40R39hj0+7wJUavYTJBb2jrzuRYXva3E4EiG+7GxbZJCvi8x7BvArWt55wDwWhmEYRqvEx1H7vhC7odCjucg48KIiDRkU7VECZToSFDsDHdCFMgOtVk5BHbbHIWFRjQTFF8gDkYo8GmNRuJOfbxNK4qwiTO4ehLwVb6Aa+bu4G7TsFUvDaM5kZ2dTWFjI0KFDuRaJ/p9Su6gYhPKmpqK8iSHo/OHPDVVIPDyOCkAcApyChEJAKCyq0fnBP3cQYcU4LzY2I3ERQ+ebH7tjaUnnAfNYGIZhGK2WIAgoQI2wPke16GcAC4gXFWmEhkCVe9wRrTKWIWHxLjIc9kKiItU99wqKp94HGQMlhKICt30bZJC0Qd25QauV76MwqGJUBjfKWbTMFUvDaO54cTETGe2PoN+pJyoqrkILAMcBY9Diwnvot+w9FS+6OfZ12/wG9bsBnVPaoPNBGTqnFKIFhUGoCeeLqEpUqhuzHp2DSlGo5WHI29oSzgPmsTAMwzBaNd5zcSPqX5GNykdmIM+EX6n0K23eWEhzf1cgI+EFVEpyIOqYMwflU0xC3pAvE/abhVYqy5FhMRJddGcBf0MejKNQt94pqCIVKM/DY54Lw9g+RD0XN7jnTka9bbyoeAYtNPiy0T9BoY9PovPGUWhh4Ul0DliPijAciDwcvrT1OpQ7VYE8pnOQd+JR4DP0m89ACwxl6NzRAZ2DHkFFIL4GPvzww+3wSWxbTFgYhmEYrR5vQDyMmtftjxK026E8hygpyCDY4G7vohXFg1GYQymq/PIBMkQmoFXJEiQ4OrkxqSjMIoUwzGo48B9kLHQALnf7PBYJjRdRSES02sxZyBhZA2RmZrJ+/XoMw9h6EsVFAfIkDEaiAuRJ/Az9Xj9wry0D/gXMB/KRkOiJ8qRWogWMQYThU8vQ73pXJCrK0PngBfS7Xk5NUdHGvX4PEhUAa9as2eafwbbGQqEMwzCMVk9eXh7dUQjCFLRymIUMgy8J46A3ITGwEV3oNyEDozsSFVkojOp9lNxdilYpl6FeGN2QKGnv5om5W4U7jpdQcudBaCX016jC1PPIeBmHuvxGuQ8lmn6NEk8Nw9h2RMOiHgMWEZaOvQOJ/57AZcjw/x8SEmnoXPI9FGL5HPo9H4hCL0uQV+Mt5OV8HrgfnUOWAjegfKydgVvdvqOi4jE3z+7Iw5kLHHHEEUybNm17fAzbDBMWhmEYRqtn48aNrED5CkcgUdAWGf1FSCBUoAt6CRIVHZGgGI+8CS+48c8iz8NwVHWqwN12RuEMKYRVXjKQ56ICCY+pKBTreuAcFJJ1MjIyDgZ+QfyF+T7gOpQXUlRUxKBBg7bdh2IYBhCKi6/RQsF1wMVIVPQAzkcLAptQ+eofoLCo7shjMRWFRY1FeVH9gCvReeNMYD8URlmIFhc+ROeaj9H5pQR5Jp4kXlTkIdHxM9SPpyWICxMWhmEYRqvHN8T6FfB7tAI5D4U69XJj0pCXYTPyJnhvw4EoVOlNFAJ1GDII5ri/S9FK5gZgFTIavkQrjzE3byoKuzqKUKScgVY7v0TeCjBRYRhNRXZ2NkEQMGPGDGagXItUJCI+RrlR5yCh0AstEByMQiVXI0/lRchzsQ55Lfd0t00on+swNz5AyeDpaGHjVPTb/zvwO+JFRRt3fKcjT0p/JC7mzJmzXT6HrSUWWCZYvZSUlNCpUyeKi4vJyspq6sMxDMMwGkFil13fLG8xukgPQFWcKpA4yCDMmejo/t6IDIInUWhCCgqJ2A8ZCAHKg1jnxnYGRiGvhs/ZqEDC5Q0kLNqhajMz3HFGj9FEhWE0HTNnziQ3NxdQ0vVA1FvmR2gRIQ2FJi5B1eYmowWJY4E7UUjkmUh8lKFGeR3QOceLls1IRFSjc8II1GhzCRIgdxGKClD+1VSUx/URsCYtjbKyaC2r7Udj7GBL3jYMwzBaLYmiAhTSsBwlV3ZCF/4UtKqYgUSGx4uLjqic7EYUK+1DIV5GyZyHotyIDSgkahMw080xHAmPVOTBOMTt/yYUQjV16lSOOeaYLT035qKcChMVhtE0jBo1ijlz5jB8+HB6IZFwDPIwVKFFgt7I87kbEgD3o3ysbsC5yAvxNbDQbdcXhVWNQcb3PcAT6Bx0NMq72gWJjn4Jx+NFRSrKC/saCL4jUdFYTFgYhmEYrZJkouIjlGjdF61EdkHGvu++m4pWEFNQIiVIXLRF4uBQJDxeQrkax6NVxlIUptARCYL21BQXKWils8jt+xC3z4kTJxIEwZayuKuR4WCiwjCajry8PHqhRYTjUd4VaPFhI1qQqEK/81OQN/IpJChOROKjJzq/LHLb7ozONSeg0KoXgd8iUVGGQiU7AA8Af0Fhm9MIRcU7brvmHGxkwsIwDMNodWRkZNALhRRERcVyFJ7QDl3AO7nX3iTsSeErwpQTios16MLeCfWdWIbCF650455F4U+T3NiZqLzku4TiIgd5I2YiI+LfaNUy2qMiCAIyMzMpys83UWEYTcjmzZuJxWK8hhYGjiNeXKxGiw4d3N+7osWCoUhk+K7cPdw2C1Co5EAU6vgOqgI3ibAJZyYwEYmR+4HTCJt4tgRRASYsDMMwjFZIfn4+Q4YM4XV0Ef8hil3ug3IZlqNVx3Wor8QzKA76BXRhHImSqvuj8IRSdHHPRJWgZiLRkoU8FwuRuOiJQqVeQZ6Ps1ATrJnIKFmKVjJPQOETyXpUWJ8Kw2geeC/iTe6xFxdlyFORgs4TpcAQ9PuvRIsQaYQNMru7+8Xo3PA4qiD1Z+JFhe+rfQwqBHEDqmDXEZ0nmruoAKsKZRiGYbRCBg8ezPz585mBkqCnIFHxNarK1BN5LZ5Bhv+RKOzgKNQQ63HknVhE2KW7HAmPKWilcTwyIN5DK4m5SFw8jhI6b0J18E9AK5fzkag4njAp03pUGEbzJggCCtDv+UnkodiEjP1uyPDvhMRDVFTg7tPQ778X8lo8iSpF/ZHkogJ0rslGeV1VtBxRAeaxMAzDMFopXlwMGTKEG4ALUKWmWe71D1Ds8q7oQg8y+EtRxadRqFLLIvfaF8DDyFNxLMq5eBMZCnshL8hU5L1IXOk8C9WwH0y8qLDKT4bR/En8PZ9OKB4GIk/EElRhLq3m5qSgfIpXgJPc9ptJLipwc8xGixbLaTmiAkxYGIZhGK2YZOJiJMpt+BiVjzwIJVS/jTpqn4sMgalujhEoKfM95KnIRjkWs5GoGIPCFj5Cde3bEuZMJIoLj4kKw2hZ1PZ7HubulyJBMCCyTTnyUr6IPJkHA+e51yrRIkOiqABVi3oZeUEhPJ+0BExYGIZhGK2aqLi4HjWlewWFQg1AwuEd4F504Z8Y2XYqyo94FRkCu6BSkE8gr8ahSFS8hxreHYc69q6hdnFhosIwWib+9/wQytvq7p734mKBux9AKCo2ospxvQlFBdRugD8BPIK8qL9H4ZTRAg/NHRMWhmEYRqsnNzeXrujCvwpYiUTCTcjzkI9KQS5CCd2+tvzXaLXxICQsHgf2QAnf65E4mIFK2h6DxElizkRUXFiPCsNoufgS1icTigpPVFxUoTyuVJSHcRwSC7cQLy4SSRQVEHpGWoq4MGFhGIZhtHry8/MZOnQoQ1G37DtRB+0ClEA5GPWeOI6wBO0CVBXqIuTlmIE8GB8iwyKGQqoqkViZjDwRM93+Ro8evWX/1qPCMFo2yfriJDIMeSqK0PlhoHt+krt/xN1HxUU5CqF6AuVwZaOS1wFhmFRLEhcmLAzDMIxWT3Z2NoWFhQwdOpR0JAI+ReJiNsqpOJvQAFiAqjh1RzkZAZDnXit0fw9CguMd4DbkjVhKTVHhsR4VhtEyaYioAC0y7IS8mUuQV9QncycTF4uRl7QLyuPqAPwUJWyDCkhExcViVFyiOYsLExaGYRjGDkFUXFznnuuAqrLsj0QGhKKiB6oYVYFCGyAUF35FcpDbbiy66ANJRYXHelQYRsuioaIClIy9irCvRRo6T3gJEBUXpcC+6PyzEuV3TUOhksehBQwIxcWrwApgNPKypqenU1ZWtpXvbttjwsIwDMPYYYiKi/OQMLgYlYv1jepWEooKUJx0orgIkPgoduOPQTHVN9G8VxMNw2g4jREVoEpzpSi0sgdhX4tEcVGFwp42Ab9Gns4UN+5lNy4qLpYhj0Y6Kmf9DTC7mfa9MWFhGIZh7FBkZ2cDin8+H1VzqkYX/q9R9ZZdE7ZZi8IVqtzYHDd+OWp8lQfshgyDlhAHbRhG3bRr145eqG9NQ0TFP1DDzdOA76PFiHL3WlRcVKCGnIWostyXwFBUpc6HPUXFxWMo3LISeUULgdmzZzN8+PBv/+a2IyYsDMMwjB2K1NTULQbDmUgopCAxsZ4wedtzB/AQStg+I2H8IBTKkOLGnkXo+UhNTaWiomI7vhPDMLYX+fn5DB8+nNdRiejT6xj7D+AB5KV4GeiKeuRAvLiocLeXkHcjmzBkqn/CnC8D81Bo1dtAZ7Tw8eyzzzZbUQHhudAwDMMwdgjy8/NZDryBBENbdNFPIbmouBH1qbgJlZNt624xNz56Ib2PmuVmDcNoeQwbNow5c+YwA1V7u6+WcV5U5KHS1GNRmNNT6LySisTFZiQqnnev56EKddOQJ7QK9cjpBwxHXo/33euDgZ+gfItjjjmGwkIfJNX8MGFhGIZh7FDk5ORQUFDADOB64MFaxnlRMQtVdCpA4uJuknfLjTa+KygoICcnZ1sfumEY3yH1iYub0WLDLu61KhReORYlaEfFRZV77u/I+3Al6qztxUYMeUO9uMhzt51ReNU/UT5Yrjuu5iouTFgYhmEYOxxRcZHMYEgUFRAvLu5IGG+iwjBaJ7WJizsIy0x/jbwQMeSd+CU1xcUj6JyyCXk8XwZGoHLWFcjbkUIoLl5FjTuPAC5w25yO+uqMovmKCxMWhmEYxg5JbeIimajwJBMXJioMo3WTKC4uJjxHTJ8+fcvzjyIRUUkoLh4GfufGp6GmnEeiZpsvEYqLcuAFZJj/EXkx9kfnmtTIsZwCXEjzFRdNJixeeuklrrzySo4++mh69+5NLBYjFouxdOnSbz3ngAEDtsyT7LbPPvtsw3dgGIZhtHQSxUXUYKitqlNUXFyMiQrDaA0UFRWRkZFR6+tRcfEoOkd07NiR5cvVzm4ROic8TpiofT6qBPUoEhUfol4XxwDjSS4ufgs8h0TFjcRXWfJV6U6l+YqLJqsKdeKJJ1JcXLxd5j722GOTfjkGDx68XfZnGIZhtFy8uBg5ciQrUVhDfaVigyAgFoux2o03UWEYLZeioiKGDBlCT+ouFZ2aKt9BpXucuWED48aNYwQy9B8DbnCvnYiEwWxUPe4RFCrl++FMcPdT3f14lKz9BqGo8OPbIEHhK9K1QXkXM1HVqGHDhjWb8tZNJiwmTZpEdnY2Y8aMYcyYMXTv3n2bzf2vf/2LAQMGbLP5DMMwjNaNFxd5eXkEDSwRGwQBqampFOTnm6gwjBaKFxW5wKHAKyQXFwsWLGDw4MHkAgchAdAR6Iaa4p2BciDGIXExA3gWdcvuCDyJ+lJ8g7pngzwXMSQu5iOhcD4qbd0WCYl85JlIIRQVoCISrwHrIu+jOSygN5mwuPfee5tq14ZhGIZRg5ycnEb3nbA+FYbRcomKiouQMPA5VlFxERUVZ6OE6pGozGw/1LhuV+ALJEzGuddSUTWnucD9wBRgNXAAcCsSChPdsbwAHIjKygbIU3EeKnW9D0oU96LiDhR2leLmuQ4YMmQI8+fPb3JxYcnbhmEYhmEYxg5FMlEBanL5KyAHiYtkoqKzG/cLYC0SFzEkLmJAMfI2ZKHO2n8HSpB34XDgM+DcyLFMBI4H2rvXUpCoyAeOQgnf5yCxERUV0wkrReUicVFUVLRNPp9vS6vsvH3fffexZs0aKisr6d27NwceeCDf+973mvqwDMMwDMMwjCamNlHhOcvdXw9JRUUX9/pp7v5OQs9FhrsNRqFVbwFPIJFxLhIWQ4F73Jy3Ax8BG4G+KGfrJGChO47TUWWpm4FDUGhVWyQqPKej0KuVQF5eHqWlpd/iU9k2tEph8ec//7nGc3vuuSePPPIIQ4YMaYIjMgzDMAzDMJqa+kSF52eoB0Vb5JlIFBWe09z9nSiEKQMY6OY+HjgM6IBExc+Q12K82+YeFPp0Auq2PRSJjSJCUQHwU1Qx6jpgHsrpiHIHCsH6GgiaUFRAKwuFOvLII3nkkUeYP38+mzZtYuHChTz44IP069ePjz/+mIMOOogVK1bUO09ZWRklJSVxN8MwDMMwDKNlk5eXRy/kTahNVAAUoopL44CjSS4qPKch0VCKRMUFKFH7PGADqhh1BBIVnVHp2SOBnyMR8SISFZciz8M5SHCUu/mrkUA5F5WmHQKUudfq6rvTFMSCRh7FpZdeynPPPdfoHd19992MHTu29gOJxQBYsmQJffr0afT8dbFmzRp23313Fi1axAUXXMD1119f5/grr7ySq666qsbzxcXFZGVlbdNjMwzDMAzDML47YrEYI1ETu7PqGHcp8F8kGs6oY1wVcLkbew4SFRegZO5fIo9DibuluPEdgHQUJnU3kAmsR96R05CoKEcJ4GnutWLU9+IWlNtxKQql2t6ioqSkhE6dOjXIDm50KNSyZcuYO3duow+qKeO9unbtyoUXXsiFF17I888/X6+wuOyyy7j44ou3PC4pKaFv377b+SgNwzAMwzCM7Y3vQ3OTe1ybuPgHMt7vRoLgtCRjvKh4D4mK41Hviv8ir8RP3bgsYBPKpchEoiINeU1SgMlIVJzqxqe5e++1yHT345GQeBK4BlhO8/BUeBotLCZPnszkyZO3x7FsV0aMGAHQoM7e6enppKenb+9DMgzDMAzDMJqAxoqLO93j0xJeLwJeRSFTxyORMAr1ungbuBeJh/VIhHQm9EKkujlORdWfdvLH5u5T3TYVyGDPRF28X0eCwr+P5kSryrGoi9WrVwOQmZlZz0jDMAzDMAyjtRMEAQWofOsddYz7BzAWiYv73XNVKA9jZ/faf1D1phgSBjcCe7p570T5F+VAJxQGlebGenZKeAwSFFVIYKQA96GE7+6oHG5zpFVWhUrGY489BsBee+3VxEdiGIZhGIZhNAca6rn4K/Is3ImSqeciT8X+wG9Rt+273NhTkLi4BFV5uhWVh61ASdzH+H0nHoubO8WNjeZY3IfyKcagHIu7kCBK1iW8KWlxHotDDjmE4cOH88wzz8Q9/+yzz/Lpp5/WGL9+/XouvPDCLQnn0dwJwzAMwzAMY8fGey4eQn0iEqkEJgH/A+YDf0Y5FeOAT1EZ2L8gz8VdwIPIU3EuYbO895Cn4WXgWfecx3sqSoFvUPWo2kTF7agD989RpaieQPv27bfq/W9LmsxjcfXVV/Piiy/WeH7ChAmkpSllZcyYMdx6661xrxcVFfHll19SXFwc9/xbb73FDTfcQL9+/cjNzaVz584sW7aML774grVr19K2bVv+9a9/ceihh26/N2UYhmEYhmG0KHyVqJOR8R/Fi4olqEP2U0A7JBqOBx5AYuIK4J/Ie3EVapB3CPAYEgLHASNRNaeX3dzHoBX+AImK9e7vdShkqiM1RYXnQeANYA1QvnnzVn4C244mExZFRUV8+OGHNZ7//PPPt/zdrl27Bs83ceJESktL+eyzz/jkk09Ys2YNaWlp9OvXj+OPP55zzz2X3NzcbXLshmEYhmEYRsunrtKzUVFxHtAehUDtjnIrUgirON0F/AZ1zG6HvBfdgEFAHpDr5tsZCYpn3XbHoF4X65EAqUBeig2oPO1k5Lm4DYmOGBIb17n5/u1eay4hUY3uY7Ej0pj6vYZhGIZhGEbzp7Gi4k3gx0A/oAB5FQ5FYU0PoCTvzcDvkSfhY1RFahQKo0p1fw9HlZ2eBL4PfA95J0rdmO7Is7EB9a24B3ksbkPJ415UfIFEyPZukrdd+1gYhmEYhmEYRkvm24qKcZFxBUggHAr0AY4FPkdejYXAiaiXxUfIi5HtxlW6bSqBx5EwOQF5QEoIO3QHqG8FSFz8AOWAREUF7vjnIjGTkZHRpL3jTFgYhmEYhmEYOwwNFRXnozyJPwOjiRcVI9x9AfI8pAAnobCnp5GomIRExXKUXzEQKENejVTkqZgDvOPm9n0sipGo6OIej0dlZ28B5iHPhhcVoNCo14Gvgfn5+Y37MLYxJiwMwzAMwzCMHYKMjAx6oYpKyUrLfgTMBH6IEq7bouZ1LwEvuL89XlzMQ96IHqiJ3f5Ab0JRMQIYRlhO1veneAN16T6SUFT4bmvFKJ/Ci4uj3ba3AUOBRe55n28xA5g/fz6DBw9u+IexHWhx5WYNwzAMwzAM49uQn5/PcrTCf1+S1/cDdkNG/2QkAsa721QkLqKMQGFNPVBORBbJRQWEgiINeMvNdSQwAVgdmTMT5W+UIoHRBeVdnAv8CuVjDKD5iQowYWEYhmEYhmG0UmbPnk1aWhoffPABsViMwYMHM3/+fGYgozyZuPgr8hZci8q6liMBkExcVKPQpg1IDGSiHIklQC/kXcDNUYE8IK8BzxGKivtQ/oY/lhg1xUUq8nj8HIVNlaMSt81JVICFQhmGYRiGYRitkNmzZ5OTk8POwL777ktPwrKs8+fPZ8iQIVznxp7u7i9HORLzULnYq5ChfwoSAiBxARIaFUhUtCcMY+qEvBZfo4Z6/QlFxSvAFNSB24sK3/TujoRjyUQCohR5KdqiqlCvAytRPkhzEhVgwsIwDMMwDMNoZXhRkYNW+3sAByOjvDZxMQ94FYmEEShx+3XgSvd6VFw84+6PQEb/RiQAvLjYB/gAeRQqUMO8V1CDvUNQHsadqNrTKOQ9uYR4cVGKvCFRUXEdKitbTfMTFWDCwjAMwzAMw2hFREXFzsg4/wVK2M4mvqGcFxeXolKwWW782UhE5KAeEVe6uU8BDkfG/VTUCK8T8moUuzFeXPQAFqPKT5uQJ2Swm+MWdxzdkcBoizwXZyNxUeH23wGVnr0fuB4llkPzFBVgwsIwDMMwDMNoJSQTFeejUrAlwEQ3LrFbdTdCUXEWcAbqhu09FDcCf0PhTx8CC1DlqEyUsNzZjfPiIg31stgF5VwsAPZFTfPOBf6DKkLFkNfiLBQOdTvKobgJiYtzCUXFDGDRokX0799/Kz6h7YsJC8MwDMMwDKPFU5eoAAkHqCkuRiJhsYlQVEDoefgBynV4ELgIlYadRCg6Yu6+s7svRl6MQciz4cXGTsgDcg/yYlSjLtw3udfPcvtYgfpj3AYUoXCsmTR/UQEmLAzDMAzDMIwWTn2iwhMVF9Uo7KicmqLCEwMygGOQkf82cCDyXMSSjO3s/i52++qJkrszUbJ1D5S0HUMejDmE4mIuEhEbgenAQcBjSGgAzV5UgAkLwzAMwzAMo4WTl5dHL9SD4nPUyC5RVHi8uJgEpAOPu7GJoqIUiY9OqETsOuBUoB9hf4kogbvvHNl+MEq+LkD5EgcCP0V9KG4iXlw85sbMQqFUq1Gytq8YFQ3daq6YsDAMwzAMwzBaNOXl5cRiMb5AHou3gXupKRY8WcjgPws4FoVCRfGiIh14FgmLfZHHoRe1iwpQbkZn5OlIjYwtQCFNfYCfuW1uJhQXjyGxkxaZ6yJ3vwhYA2RlZVFSUlLLu2p6rEGeYRiGYRiG0eIJgoACYBUqGXsHEhe10cbd1yUqlqOGeLsQioq9E8Zvivz9InCxu0+NPD8CGOnmnoY6cJ+B8jcWAy8DPyFeVHjuQyFSX6PO4c0Z81gYhmEYhmEYrYIgCOISsn1fiNo8F4lUIKM/FeVepCLPw0soX2JiwvjZqP9FNsrBeAaFP/k+F0dFxo5A4VQLUd7Ea0gwrEFVo5JxHwqHmgEsWLCAgQMHNvCdNA3msTAMwzAMwzBaDfPmzWuU5wJk8FejFfc2yLOwFnkevkB5FV+gJnee2Si8aSPwEOpNcQRwmbt/Bnk7PHOBr1CY1CtIMMxEiea3EYogT0sTFWDCwjAMwzAMw2jGzJ8/n44dOzZ4bHZ2Nt2gweLiI+AN1GMiQAKjFJiCmteNRSLhMOAJJAq8qMhAidyPu8er3ZzHEi8u5iKB0AGFV93gHhcWFm4J4bqJUFy0RFEBJiwMwzAMwzCMJmTBggVkZmYmfc0LhayNG4nFwgKvmZmZLFiwgHnz5tGhQ4e4sb7Z3U7I2K9EYmEyEhoVhMnWHyFDvw/KYfgQCYUy4EmgEIUwpQDHIXFxA/AIEhVfouZ5C4AlSBzc6eb24uI21J8imajIzs4GiBMXF9MyRQVYjoVhGIZhGIbRRCxYsIDBgwfTk5rlVL1QyAUORZ4CLy56AYMHDwbYsi0or2KAG/+6m+dLoDcqKdsGCYgMlBuxHJV6HQbMR0KkAhgInIjCmy5185yOwqNmuG03uGOaBVuOOxaLbWl49wvU7Xsmqvq0EXlGEkWFx+eHrHbH2NJEBZiwMAzDMAzDMJoALyoShUMQBFtExShUcvVUlBR9M/I6HIe8Bt3dtpORV2EAaoz3U1SF6Z9u/NHAD5EYaA98hsrCjkCiAmCIuy9w98e4ey8uZrhjXOqefxQJgKgY8uLAN7x7DVV9ApWT/ZrkoiK6fVZWFgvy81ucqAATFoZhGIZhGMZ3TFRUXIS8AXegsCLvfRgJnIMqKy1CpVnLgIdRCNNA4GTUpTodeSXORZ6GKtQf4ifIS7AW+BTYCxn865GgGILConyQVV3i4k4kTIIgYN68eeTl5RFs3FjjvSV6Hj744AP23ntvOnToQGF+fq2iwtOc+1TUhwkLwzAMwzAM4zsjmagANaurBv6AekacB/wIiYI2KG9iovv7bmAQKvH6MuozcR5wgptjKcp5OBEYBdzvxpWjUKcRSERUu32nUL+4uA2FPQFkZ2ezMYmo8Hhx4UUFUOf41oIJC8MwDMMwDOM7oTZRAfIyLESeiNOQh6IcCYkuSFisQB6MdOAB1GF7MBIVpyChsAgJi4GoTGxfN//9bvwZyFvhA5jWoRKwieJitZtnMGH/ituomQtSGw0Z09owYWEYhmEYhmFsd+oSFaCE6DdQ5aWfoNyIzYSiYj0SDl1RInYaEheTkFchQKFSS1FFqH6EYU5HIQ/Ehyhxu9q99iLwNBIORxOKi7kojKkv8oascvsoQg3tGioudjRMWBiGYRiGYRjbnby8PHqhZOtEUREgD8NhqJpTT+BIJCo6EfaWaI+8CxXAj4HDkadjrZujExIDS5DA6OPmfxrlWByJcjG8qHgKhVQ97cZNQCVm56DKU3shQbMZdd9+HQmO999/f2s/jlaJCQvDMAzDMAxju7N+/XpisRivokTts9zzAQp5CoAxqNrSfchzcBoKhUpBnoYNyND3XS9SkehIQSFNMVQZCtRbApTo/RgwDoVBeVExxT13GDAViYtvkKiJiopiN/5mVDr2/fffZ5999tn6D6QVYsLCMAzDMAzD+E6IlmMF9XrwDeteAJ5FpWWXEJaQPRUJic5uG18zKYYESYZ7/WvkuYBQXDwAvIvK0yaKisNRA7s1wPeBT1CDuhNQ2JOJisZjnbcNwzAMw2iRFBYW0r59+0Zt0759ewoLC7fTERkNIdpl+k7kcXge9Yg4BeVDHAPsjjpWP4CM/BgSFx2RGFiHci+ykEG7C0rqXovEx7vIC7HWvZZMVKxDwuQl4L9uP88hj4qJisZjHgvDMAzDMFochYWFDBs2LGnH5tqIxWL0AoYNG8bcuXMZOnTodj9OIznec3ED8DnKk/gRcAhhZabD3f2D7t57LtoiIdEOJXDHItvsgipLPYjCn36Ays0+BswG8okXFR3cdk+gUrIjkRfkJvf4TUxUNAbzWBiGYRiG0aLwoiIXha3kEDZVq41YLMZIVG0oF4kL81xsPTNmzCAlpXHmZEpKCjNmzKCoqIjZwHvAPkgExJBnYXeUZH048lA8jZKxV6Mk7i5IZFS4m2cF8jRMda//Dnk/vo8ETKKo6O5uJ6PvUQEKqSpAYsNEReMwYWEYhmEYRoshKiouAq4FfkXd4sKLil+68Rdh4mJbMGPGDEaPHk0P531oCLFYjJ5BwOjRoxk8eDC90P+iG6q8BPHi4lmUsH0SqvDkcyo6oMTtFPdcNRIVG1F+xWlIgFzl5voJ8E+Ua1GMEsK7R47rLMLv0dfArFmztlR/MlHRcExYGIZhGIbRIkgUFdGOzbWJi6io8FWITsfExdbiRcXWeI0AlgPTkTHfIToWdeAuRP+341DYU3fkiYghT0W1e34VEhWdUcnZk4Cfo4pQf0NhUz1RGFUnFHq1IuH4ot+jnJwcgiAwUdFITFgYhmEYhtHsqU1UeJKJi2SiwrMjiov58+fTsWPHRm3TsWNH5s+fH/dcVFRsjdcoBzWyG4sqMr1IaJj+AXgH+Bmq5uQrQPnZK4BK5LVYR7yoqHa3k1HVqXeBKyLH0tndNlK3uGioF8YIMWFhGIZhGEazpj5R4Uk0CmsTFZ4dSVzMnz+f7OxssjZubJDBnJ+fTywWo9PGjWRnZ28RF4miIuo1Op+Ge42+B5yLmuJ1QrkPU5G4+COhqDgVhUgFyDORgrprV7jHxSQXFSnudrKb520kLla5/XfGxMX2wKpCGYZhGIbRrKmrY3Mi3nB9CBmVtYkKz+nADGCl28+mTZu25lCbJV5U5KLP8BXqrqSVn5/PrrvuSg5qHvcGkJ2dzdNPP82kSZOSCrx1KCSqGrg1Mn8yUVHk7k9ESdi3uceHI+M/FYkOLyoqkYhIJ15UBMBXKMk7majwnOzubwYeBv6EcjA6R459BTVzLuaisrYdO3Zkw4YNST8rIx7zWBiGYRiG0azZtGkTy2FLx+b6OAtVEapPVODmewXF+LdUUbFw4UKysrKSvhYVFdGwpREkX433omKkG/cP4EJUsnXSpEmMJLmoiCFj/yQkCmrzGnlR0RWJihOBc4D/AdOQcNgfOBaJCi8iEkVFGurQfSEqJVtMclHhS9Ee4+atAq4H7nevdya55+I+4HX0vcjPz6/xORnJMWFhGIZhGEazJ9pUrSHionvC40qUJBzlDuBG1K8g2ep9LBZj+vTErb4ddRn/UebMmUN6ejoAWVlZLFy4sN55Bw0aRPv16+OEwowZM4jFYnGiwouBHwA/BoYSLy5isdgWUeHFgA8nOgcYjiounRrZ/zpkuLdDydftCMXF/sSLCm/8ZyBR4TkR5Vl8DOyLGtVNRjkVaciDUYVERRv33APA3SiR+353KyU0bKvR/7waNct7FPgPcKDbxx3Ei4v2bvsyJCquQ56sefPmMWTIEIyGYcLCMAzDMIwWQWPFhacShdhcCvzePdcQUdELhUdtrbjwxn/HBOM/kTlz5jBixAi6lpcTi8XIWL+eQYMG1Sou/Lz9kAdiuDtunwfRGRiCch+8qFgALEYegZ8SiotYLEZnYBBwNkp6BhnyKajC0knAl+61auJFRbobn04oLh6MzAMSASnIgF8Xef5+lGB9BHAk8hzcjTpgp6KQpxihwLgXuAs4CHjBbXcfEhtevFS7uYtR+NM9wN4oXOs0YAyhuCgGNqF+GQ9jomJrMGFhGIZhGEaLoaHiohJVGvKiYg5wAGp4dizJRUW3bt1YvHhxjbKoDRUXPuE5ijf+ayvLGovFyM/P3yIqcoHj3bgebv/JxIWfdyRa8e+MhMVQYPTo0fQD8lA36yzU8M2LigEotOl0JC4Gun2Ndp9NN//ZuH15cXGp+0w+JBQMUVHh8eJiJ2S0+3lS3L5Aje7WIcP+bvS/2QuFsPn3fwdqUoebI9U9vgPYDbgaeSPORV6Y+wiFQgrqf/EocDvQF+VY9HT7vhKJi5vdNu1R4vj1mKjYGix52zAMwzCMFoVPCr7JPU7MpahERvMnaJW8N1qlnkjtngrvoejfvz/DCUN4fFhMXl4e+fn5jB49Oukx+dyEnoSJy1FR4UOR/P69uOgF7LrrroBExPlo1X4EMrg7E4qLBQsWMHDgwDhR8SvgTLS6fz8SEcOQAf09FPbTHgmLdkhQ7IzCivqgVf/NyJj+oTue9kiIgRNCSFyAxMUC4DXkNfhN0k8jFBubkaHfyc3jxcVCd7yTkSjYE3gJhUS1R83x3kSJ4KDQrQeR52E3YAJKru7o5rwAWO9eB4m4Kci70cONuRfoj/I7dgL2IBQqi1CSuomKrcM8FoZhGIZhNEs+/vjjWkOHvOfiIeKTbr9CBnwhMB4Ztm2QqChHq/onkrzfxXHI8O6JSpRCfEna2jwXXlQkeiUSRQWEpUy7uWP5MTLwhwLnIVGRhUKPTkVhQzsRios333yzhqiIIUP7YGTYHYzE1Guo2lUuMqir3WfQBgmLfBQCdDLwW+TJyAF2BfohcZHoubgX+MDN+zASF7WRjrwMoNwFT4o7tjtQaFUp8DIy9LsiAdQHGOeO51bkdboL2A95FVKRCFiNcjbWAIe4Y78P+D/gTiSsHnfz3ojyOPZCYVPXofKzs1ECuImKrceEhWEYhmEYzY6PP/6Yvfbaa4sHIBEvBk4mTNR+GYXoFLr7i5HnYTlaaU9DxvNY5MGIVi46C7gEiYDNyMivcvPWJS6ioiJadWk4Eg/J+m4sQob+z4HBbt6fuL8z0Sr868gYPpxQXIwEDjnkkBqiArTa3te972Xutj9a9f8PEi79gPlIfM0DvkE5GO2R4OiLPBadgGxqios7UAhaDFVRGgrcQu3iwldxAokFj59nDjLs30Iemi7uODojYdSDUFy8gv5vF7hjX4XCoAqRN6YIiY1jkfh4FXmudgeeRR24C1Bo1CWEeRSFhYUEQUBJhw4mKrYBFgplGIZhGEaTM3v2bPLy8igvL98iKmrruxAVFT9327+MVrZXIeP6cMKV/1S0en0pKp/qeQEZrT9HHo0s1OU5HcXen4cM5zaE4iAaFhUEQZyoOB15BQ5DnoDJyPCNchlKVPbC5R339xC0cj4LhQ6VopChNLfdNDfncCQooqLidZRPMAyJojUoD2IMMu5fcO9pfzd+BhIKo9EK82K3/+5ICLRFxn22Gz8HhVo97D7Lz93zT7jP7yH3+e3s3lMKEgi+sV3nyPtf4cbPAjIyMuhYWspuhOJwP+SFKETCBRQqNZFQ8OW4Y8tAImixG7enm/8HwFFISExGImsOMHfuXIYNG8YaVEa2sLCQ7Gy9S+tTsW0wYWEYhmEYRpMye/ZscnJy4rwTdeUljERG5IdIMPRFCborUY7CwWiFvCMybE9z+4mKi78hQ/2XyAjNREYqKKEZkouLaDO9xOMEreRXopCozihP4jLgGkJR8TMkPn6LQncOI+y/8BEy3vOQUJgd+ZzeQ4JpJLAEeV8+RknQA1G5129QrkRb1G36auT5mOrm8OJiBRIvxW6enkhUpCLBErjPox9h+dflbn7Pj5HwORmJik3uvceQwEgUFSABcTKwFphVWkopEip7EnqXdnJjC5HXp8Idw7uEHpSzUKjYN26uTcgbszMSNV8gT80C91nNnTuXoUOHEgQBHTp0oDA/f4uoMLYdTSIsVqxYwbRp05g2bRoff/wxS5YsISUlhX79+nHYYYdx8cUXM2DAgG81d3l5Oddddx2PPPII8+fPJy0tjby8PM4//3x+9KMfbds3YhiGYRjGVuFFRS6KkZ+GvA5RY/0XyEj9KzJ890RG8V7IU7EUGTS/QqKgxL1eila1IV5cjEOJvmejCkRtiA/VgeTi4m7CZnpQU1Tg9pfq9n2Me+5ulDtRjESFP5bD3XwvI3GzARnzuyBDP0AhQp7RaEV/HVql/xKt4vd1c692285ChvXhyNCe4Laf6u7HoXCoRdQUFT4nArefF1E4VV/kjXkHeZF+jAz/85DHZxMSEz5/o5qaosLjk+19En0BYU+K0wjFRTUSDvNRnoSvJLUSJX5nurli7nOoRv+bRShnYoX7XLyo8GzcuLGWIzO2llhQWz/37chJJ53Eww8/TEpKCqNGjWLYsGFs2LCBjz/+mP9v77zDq6rSt32fAAklVB3pEEoIEGJARbErRbEh1lEUexl1LKNTv2lO05nfOMXeUHRUxDKCINjb2JUWOqGFXg2kAJJA9vfHs1b2yclJk0CAvPd1nesk++yy9t6nvM9626ZNm2jWrBkTJkxg2LBhNdrv9u3bGTZsGJ9//jmtWrVi8ODBFBYW8sEHH7Br1y7uuusu7rvvvhqPNz8/n5YtW5KXl1et5jaGYRiGYZRlxYoVDBgwgNzc3NJl0aLiDlTF6A00O346cA5K6C1GcfLPonyAEjTrfTpKJv4niq2/j3C2PQ95D5Ldw3dgfsbt/3JkxBZR1qj2nZ09v0DG9fmoytB8t7w9yov4KTLMY5NWc5Ch3caN/QUkVq6KWe8FJC4GotCfNGQwf4dm972hvwAZ4E1RUvrrbuzpKDRoiVsnC5jhxnY88tr4ikz3ITFwHwr7KnL7rkhUrAL+gETYeiS+vqZiUdHIXYfdUftuThiyFUt0hS4IhdpVSFxsRyFYT7l1GjduXNodPbart19/Csr5mOv2GSsqjJpTIzs4qANuvfXW4A9/+EOwevXqMssLCgqCSy65JACCNm3aBLm5uTXa7+233x4AQUZGRrBp06bS5dOmTQuSk5MDIJg8eXKNx5uXlxcAQV5eXo23NQzDMIz9hdmzZweRSKRG20QikWD27Nl7dNycnJwACNrJ5g+CIAjmz5+v32wInoJgMQRLINgCwTMQpEDQAYIRECyF4BoIfgjBeRCMg2ArBHkQfAfBRRAcDsGjEJRAkA/BWgg2QLDGrbfbvRZAsMk9+8cOCArd8hUQbHbLn4LgaAiGQNA3auxBEARA0AmCUyAY6/YfxDyWu3PKjdpn7GMXBJdBMNDtJx+C1e78/Dq73XrzIXgFgvHueZY7z3y33pMQ9IfgGAimRO1rCwQTIbjeLff73enOe6e7Nv6R6675Kggec+feGoJz3f3KhOAJt4/tEBS4+1AEQbFb/p277nlR1z3ew+8f98iA4Gm3zcNxrns0QJDu9uHPv59bf+nSpXv0njVCamIH14nHojK2b99Ou3btKCgo4LnnnuPyyy+v1nZbtmyhXbt2FBUV8emnn3L88ceXef3Pf/4zv/3tbxk0aBBffPFFjcZkHgvDMAzjQMd3Y26HZp+r8/PvezusAyZOnMjll19OQUFBtY/ZvHlzJk2axODBg0sTsd8izBvwM9Tno7CVFqgS0COoUVo34HPkEWiBeh/8GHkripBnwfdLuBj1Nbje7S+JsqVVmxN/9rzYrbcdeQkau+fJyEPS3J1/vL4XPZGXZS0KqbqCyj0XreNco6UohKuNG9swFLrk8z1KoLTzdIK7dotR8nIf5E0odNfpLXedZqAwpOtRt+nJKD/jPOTtgPKeGs9WlLOQ6NZZ6vb3PGHo1C0ojCzaU5Hk/i5x42yAwq2au/3W1HPhk/Yr6ozu8Z6L01AS+xxg6dKldO/evcJtjJpREzt4vys327RpU9LS0gBYtWpVtbebOnUqRUVFdOnSpZyoABg1ahQAX375JWvXrq2dwRqGYRjGAYAXFRV1f45HdPfpdGDkyJEkFxZWuV309smFhQwePJh0JCDuQx2WOyND2ucn+FCdQtSj4APgJpTsPAAZ0p8gw70XEiFNKdvx+SXCJO5X3P6TkfGcQGiARxMrKlqhnIQ33X4KqFhUpKMwqKtRrsdLqIFbScwxUpCRnYsM9lh6AKciI3w3EgefuNdiRQVITAxD1aECZLB/jMqongb8A+WkbEUC7S8oiTlaVJS4c29A1aKiEwoHuw6VgO2Gwsi2UVZUQNihuwTlslzqxgW6lhXJgxtRWFs79/8ct11VogL3ui8ja6Ki7tnvhEVxcTE5OTkAtG/fvtrbzZyp4mdHHXVU3Ne7d+9OmzZtAJg1a9YejdEwDMMwDhSiRUV0n4XKxEV0/LoXAxnIMK2uKOmLjMUM4GTkZRiPyp9ejETAR1Hb9EQ5DJ8gMXM+MhQjyEBNcf93QJ6LtYSGaoCSmX+OPA5PuEciSure5dbJJ+xNEU9UNEc5GP9BBn8jKhYVPrb/BGToVyUudqO8j11xrtehyIif6cYy0Y01VlR4mkT9vcat3ws1+ANdr7boen2OvCpnRm2T4M5tN2GfCT++gLKiwieP3+r2uwwJjYCyosLTADWoexjdo0eBcW79YuIzFnkb1gOzZ89m6dKlFCYnV8urBro/hcnJJir2A/Y7YfHUU0+xefNmmjRpwhlnnFHt7ZYvXw5Aly5dKlynU6dOZdatiJ07d5Kfn1/mYRiGYRgHGrGiIrb7czyREG04X49CYLa77c8GLqxgu+jt+6K+A2e77XYAP0PG46XAr91YZqOZdFAozFfu9fNQcvIU9/89KLl7ATJq2yFxsZpQVKxAhnljt+xhlPTbDBk7xcjL4f+uSFSMQUJoAnAz8Tt0e1HhqUpc5CCDuyXly3H6Y45CXoBFKJG7BWEieqxQgdDDk4AExSY3ZlBjwOlu7KOB7oTJ1BCKAp+sXuyuRUskMOYgb090RaoE4Hfo/n+M7kOsqAAlTj+ChOMT7lgPoiT1xDjrjyVsVjd79mwyMjLo3r17jULuAAoKCkxU7AfsV8Jizpw5/OxnPwPgt7/9LW3btq32tv4N2KxZswrXSU5W0bmqhMK9995Ly5YtSx+dO3eu9jgMwzAMY3+gIlHhiScu4omK95EH4WqgH+q7UJG4iBYVA5C34irgFNRvoRMywFsjgzcdxe93R8aqFxWTgaluP2cio/xy1NthOqoA5cXFXEJR8Yo71h2oatE4NGvuS58mIo9JPFHxLTJ+W6HwoXjXKJ6o8JyAKlgdi7o+e3GRQ8U5Fv6YLdFsfVt3/ocgEZVAxeLCh3YlI+/OSHfNzkWdrLug/IoM5DHxRn20DyARiYsp6D3yOQoFa488IdE9NCAUF8e4bR6Mef1JJOh6oepei5GHZj4SG4/HrB9PVBgHNjXuY/Hzn/+cSZMm1fhAY8aM4YQTTqjw9dWrV3POOedQWFjIiBEj+OUvf1njY9QWv/rVr7jzzjtL/8/PzzdxYRiGYRwwVCUqPNH9BCoTFT5+ILad2KuEHbH99tciA7kjMjCXA13R7P+HaFZ9JOra/B0SE2+gXIYLUViPFxVnEIYDjUHx/5cio/ZmlAexlrKiogvyjvR0Y2+CZv99eVXfwG2He80nFx+CysGOQR6VaHEB8iyMJL6oADVw242SuN8A3kVGfSsqTtz2x/wTEiB3oc7f76LSsiCvgc+18InR0aLC9+k4C3k7vkK9Llqh+5eKQqE8Xqjgnt9z4+2IQtFw2xMzBs+zSCRuQaV+W6BGddGi4mX3eBOFruW6fXkhciMmKg5Waiws1q5dy6JFi2p8oMLC2JSpkPXr1zNkyBBWrFjB6aefzssvv1zt5DBP8+b6aqisJbsfQ1UZ7UlJSSQlxXPwGYZhGMb+T2ZmJu1RZZ2rkUH6LZqNjsUbys8hL4IXFe+g8JzYoORUZCgWI4P5CcrP5r+DZrw/dcfujsKLDkUC4F03niuRR6IbmtEe6tYdGXPcMW6fF7r1U5GR+hPU68KLituQF6QTqlp0LqGoCAhn6CMoRGo7yivwFZiuijperLg4FjXum+eOEc2n7rU+yLjPQsZ8KyoWFZ5G7nyOQkIE5NV5Gc34pxD27vB5ItsoKyoiyLvQzY31XZQLcRLKYznM7ZOo9QNk+E9EVagGI2FRmbgYi5LrS1C+zAPA/Sik7X3KiopxSLQtQ96YWbNm0b9/fx5018hXcDJRcXBRY2Hx/PPP8/zzz9faADZu3MjgwYPJzs5m6NChTJw48XsZ9b5T98qVKytcZ/Xq1WXWNQzDMIyDkZKSEiKRCO+gWf3eqJJSW2Tgx3IjCsE5DHkA/otCngYQegw8C9067VESdmNCUeJFymnI67AKeQ68z38YCnmaBPwSiQJQxSFQqEw/JB78rPpTlBUVDdGs/ndIjPzO/f9nN572SBzFiopI1APkLQAZ6lC1uDgceRWWuf+9uIgVFa+ixnRDqVpU+OZ4N6PGeAuRN2Ar8kykEiZqJyBB9x3KbYgWFUuQ8d4HeYfy3X4ykHga79b14mK3O+5E5Onwnbn9/fC5GtHi4k3krQB5iJajXI5/uvWPoryoyELiYdasWWRmZpZ6tr4lTNQ2UXFwUac5Fps2bWLw4MEsWLCAIUOGMGnSJBo3bvy99nXEEUcAMG3atLivL1u2rLTb54ABA77fgA3DMAxjP2TmzJlEIhEOO+yw0mW+DOc/0OxwV+Ql+Ni9noNmvr0B77dcjHIKFqLSp+uj1lmIZsYPRZWiQGLiNcqHCJ2JPAQrcJWVkHfjPeSRuCxq3QZIHPwceTrGuOXxREVjt8++qPxsW3deFxJ2gx7p1kumrKiI5RBkCH1L2VKwVyGx8zESF54Ut8165OmIJypORbP/yVQuKtai65uJvDM9kQDMdmNJp2wI0hvu+nyG8kS2uXPajpLXD3HnvBXloByPxN1x7u/xyJORhwz/F904R7hrtM3t9zyUdD8R5Vx0dWN9GgnFacjL0su95sXFy27cr6IwrVhR4QmCgA2RiImKg5Qaeyxqi82bNzN48GDmzZvHkCFDmDx5Mk2aNKl6wwo488wzSUxMZOXKlXz22WflelmMGzcOgEGDBtGhQ4c9GrthGIZh7C/MnDmTI444gnbA1k2bSnMevv76a0B5BTNQxaK+yMj/HZp1zkAGva8a9BWaFb8SGcqvuGMMRwZptKjYirwBCYSiJJYLkaE5F3kpJiBvxp9i1vMlYC9CRut4d6xVxBcVOcio7Y8M+L+i0KY7kSDIQ8atz0OInUXd7M4jD4X17HbnDaEYuMo9R3suViIR0g6F/3xLWVExDAmmFchQ34rCoXDrei8Jbh8nI/Hyjrsu36Fr2sE9PG+ga9cDeQ5AyeK48+7sjrkW5UosQYb9KUgA+OL9zwBfuLEnuTE3d+PId/tqTui5eBH4Gr1XOqL8lK+ijt3LPa8mbJ43FIVIzaO8qPCUlMSrc2UcDNSJxyI3N5chQ4Ywd+5chg4dWiNRMWTIEHr37s2ECRPKLG/dujU33XQTADfffDPffvtt6WszZszgb3/7GwC//vWvMQzDMIz9nfnz59OoUaNK1/Gioh8KA0rFlSmNRDjmmGPIAO5GYUr/RbPhi5HReQzyQDwNzEJN1Lyh3BuFEl2EYudfdtscgkTFTDSbPZP4pVCjuRAZp88gAzieqPBJyQmowtElyEiOFhVJhKKiC8onaIU8BIeimflL0Sx+S2Qk+yRnL1xw53sxCrvKd/v1SemxTeyucuf7FhIAS5CH5AfABmSwR4uK0e4curpx5CJx8Yw7p2ei9p2HDP+jkZB6CnlC+iIDvRDlsXhRMRx1HR+GxMVn7vy2IW9Hl6j1xyChMtQdvyVwBBIr093+16L7/zDyTjRDIsx7dvqgXJUvkdjKRWFqx1CWXm69ZUiwpCJPUl+gf//+GPWLOvFYXHfddcyePZtIJEKbNm1KBUEsI0eOZOTIkWWWLV26lBUrVpCXl1du/XvuuYevv/6aL774gtTUVAYPHsy2bdt4//33KS4u5s477+Tss8/eG6dkGIZhGLXG/PnzSU9Ppx1h1aVYokXFLShRtw0SAcuQ4R1bEepPyBi/Dhns76FE7edRcnI/ylZ+OhfF6j+HwmZORIbpZjTjvgmJiwFUPFP5ODKEFyGj40mUAwHlRYXnAnc8n2idhLwFOYSiwoc4fYkE0efuPH7klrdy+/LdEJJRjsDjyKh/AHlpbnWv93Bj3IryGhqjkKFPkTFdjISED086DF3nl5DnZ3TU+L24WIHExPNIkPgQr3ORod4Q5Vd8hhKjb0ECKd+N921CUXG6O68RSExMdfvy8RmpSPy85K7XYe4a5LlrtwTd4wboHl6CBN97qDRuK8LcliUojOkkN+5x7nxepGynbk+056Ij6kjekrDaWHUb3RkHPnUiLHyuQxAEvPzyyxWul5KSUk5YVEbTpk356KOP+Oc//8kLL7zA1KlTSUxM5Nhjj+XHP/4xF110UdU7MQzDMIw6xIuKfmhG+wPKG2fxRMUGlFsQQQbgYW57zxwUanMKMmwT0Ox3QGhYDiP0QDRARulqNCM+DxkN6cjQ7okM/UVULC4eR8blfLddIpohB4VgxRMVfsa8NTL8i5ABv5pQVHhB8gIy1i9FHpVH3LbxxMV45K1ojrwB8934ElF+yGpkNLdw+3/evZ6BvCheVPixnoBERz+Ud+AT2D0JKMTpcSTEbkEeo4eQV+Q85DlYiK5zT2TkJyIh8QllRUUD1ODvMXfeGZQVF+8gIdIHJVY/5q5BW3SPGiMBNtVdu83u/2JCzwVIoMxx12G9u26ViQqPD7nyDQljSxmbuKgfRAK701WSn59Py5YtycvLq7JUrWEYhmF8X7yo6IuM5V7IAH7FPQdBUKGoSEGhLiuRETgBGb1/RF6CN5GBPBoZiiCjvRgZtC8Ag1AfCm8cNkC5Ea+ixN6taKZ7BGG+QA4yXA+lrLjY6I73GZQpRXsKmpH/OQq1akDZpm1QttfCdpQX0AzNuHtR8Zw7r+NQgvpulHS8BnkBfuS234mSxh9DxvV3UdfSl8m9HuUVtHaP55Ch3ReV3I0VFZ4SJC7eQV6AUYTiIlpUgYTAULfvQ1ETvxvduP+CvA3t3LoDUVjRcPfwouIJygq1E9359Ubeh5NRfszTyHPRxF2fVMKQrM5uLNORV2SB219nJNpOcPvZgERQZ+KLio1UnFsTTfR1MJPzwKQmdnCdJW8bhmEYhhHiRUUaMnL7orASb9C9QtjpOlpUrEeioiMyyDsiUQISF5ehZnCxogLCvg4jkfH6H7fciwuQiPgK5RkMQUZqdBJyinuO9VwchgzULcgAvhGFaaW4477qjn0BZYUEMX83dtssQzPpvZE34UmUN3CLO0ZrlF9wHBIFScjQfwqJioVuTOsJDVwvLp50x7kRhf34src/pGJRgVsW3fp3nHveTlljevny5XTv3p1NyFOwmbLN4jogA38NCgGbjXIkoj0V0aJi7ty59OvXD9y1XYDeMxe4a3sbypuZgsKxRiBvxlokuG4E7nXX0QufjSjEaj0SnP+jYlHxOOVLDFfEjei9kYv6iOXn51exhXEgY8LCMAzDMOqYaFFxPuoJ0BvF/beMWm8cmnkejAxoLyo6EXoYEigrLuJ5KjzRZVjPQeFOnyAB0RfNyE9CXonRyACOFw6T4p6jxcV77rkLmk3/KTIuL0ShO88goxXiiwvc8Uuixr3Mnc8E5L34JzKcfcL1W8iw2Ykauc1G3oQOyIvx99jzdx6LzkhU5bjzHwjch0KmmlN5pZtYcfGkG+caQgHTrVs3li1bpl4OzrCORCJlmsWtc4+GyINyCarC1ICyosLvM1pcXEkoKhJRvsY0QhEzHr1XgDLH9CKladOmdO/enY7Im/IG8ohUJCr8WLa6ZZWJi7HuWOuBZVlZlaxpHAyYsDAMwzCMOiSeqMhAxnQxCk+J5gkUJ98aeSM6EQqKwD0nIXExDHkYBhBfVETzX2SMnoIETSKqFvQqCuE5ExmSC5AHI7Y6UAoSDuuR8b+DsBdDI+Q5GIzClQJUcWkJ8sQMpnzPBy8qfA5GCsqVmIDyIf6JBIUXI+OQWBnixv9T5CHpi/o3ACxFxrL3/ESHaP0MCZNTkKhoQFlRV1noT7S42IUEQizdunUrM1sf2yxu1qxZNGvWjNTUVJ5COQ5XU3EoUXp6eqm4eAYJoB8hQ/5fSCQuXryYoqIiCZriYnDn7o85d+5c0tPV6m/ZsmV0796dZkj8/Y7KRYUff7TnJRY/ljlu/926xb6bjYMNExaGYRiGUYekp6dzKCo76kVFdGWmWHFxFYqf/xh1a+6CjGCPFxfrUD5BP2R07o6znhcX/0U5FiehMKjmhLkVXlS0dQ+ILy6WoipRycjw70aYSNwDzcJ/hsTKSHfMDSjPoiEKv/GGrBcVEUJvQQPU/foKFOo0FBm6u9CM+OPIWN6JkriPQt29fbfp6JlzUIjTaYQG8d9Rv40+MdcJqhf648VFH3cuLwIJCQmV9mzwxnl0v4fFixeTmppaapC/TcX5CdHi4iGUHP6e227x4sX07NkTgGInKvwxGzZsyNysrFJRAaFXpXv37oB6m0RXFIsncCoTFyYq6icmLAzDMAxjH7N48WIyMzOZO3cuoNn9WShePlpUJLrnYhSuU4QM6XQUcvQyMr7Pd+t6obADNZdrjkrQJlJeWIDExWsoB2ELChnyouJlNPvvRYUnDXkuVqHk8tZu7BvQjH4mCj9ag0rAFiNvw8mo/8NEZPxuQP0kTndj2+nWbUzF1aJ2obyJAHgU5Vf8EAkJ31fiNTfGqYRGTrSRm5WVRSQS4fDDD+c995o3oPtRnpqE/iSgMCIvYGZXI/QnVjD07NmzVFxsomxOSDzS09PLeT+iRUU8du3aFXd5tLj4l1tWmdfEjz9WXJioqL/USYM8wzAMw6ivLF68mF69etFyxw569OhBT2TQ90RegDdi1k9ERvcGFJqTjgz9a5Gx/jIypovc+hFknDdBnoNClPxbTPkcBi8qNrp9TUEJvZOQwBlMWVEBarC3HoVatURG502oypBP3D7NjSHHHduHYZ2FxMfHlK14lOjG+jnycMQTFTvdowHy2lyN8it+igzeb935XUTlouLwww8nIyOD2bNnM8e9Npb4xBrU81COwuMVrB99rNmzZ5ORkVHBmpXjxUV+06bVrqQUBAH5TZtWKSqqwosLf23upOqqTtHX5k5MVNRnzGNhGIZhGPsILyp6Ik/A28hA74sSnDuiGX1QLgKox8ESZJA3Q4a+z4E4BM3i+45Q3nOxCnktfuD+ju4G7YkWFdeiSkLjkVHeCQmI/yGR4MXFYmRgHobCoJ4kNDoL3HhudMc7wx1jBcoXSEN5Fu8jQXGhWy8ZJTuvQEbJEjfO7lFj9aIiAQmVle6aXYZCuHIJZ85Boih25tyLCo8XF4cffniZ2XnPnoT+7Imo8PTs2ZNt27bVaJuarl8R0Z6L6nhNoHzOiImK+ol5LAzDMAyjlsjOzqZJkyZxX/Oioo37vwgZ9KtQGdCjkbhogsTFG0hUjEF9DiYT5kqADPndqCJQO2TEf4sM9OVIpPRETeJWIY+HJw8lOi9B1YfOc8suQYLHN6N7G4U1baByURFvNr8BEjoBMrb/4bY5EfiNe92LiiUo1OtEFFq1GDW889cpVlRkE3Z4vgOJDN+ELd7Meayo8FTkuagq9Cf2XGtbVOwPeHGxrXnzGnlNtjVvbqKiHmMeC8MwDMOoBbKzs0lLS6Md5TsNR4uKbih34SNU9vMQVOL0aEJxcTRK/p2Ich+OdeskojCgtchgP4ywlOjFqH/CKmR0t0BGeSoSHMvcWDojEXEMEhALUNJ1BBn6l7j1phKKi3x3rE5uu1dQXkNVs/leXPw/JIz6EF9UdEQhXrj9/wUJqytRTkWsqGiP8iEilO2T0LBhw3Iz5xWJCk+s56KqhOnYc/WlWw8mUeGJrWRVHaxPRf3GhIVhGIZh7CFeVGQg0fAWobiIFRV3AJejMqcfA0eipOD3UeLz0SgEqQB5G36BEpQno8pEq5GB3hB5J75EouJslOfQnFBUtEQGfLIb5zJ3rFzCUrWPI2/Cb9z/UFZcNELejWHIoJ+AhMUAFIYVLaLiiYv/oNyNBUggvAJcR3xRgRvPOGTY56NE7pvcucaKCihb7WmuS5b246hKVHiixcX3Df052ESFYXwfTFgYhmEYxh4QLSpuR0ZyEqHRDZQTFa+hHIibkMeiCzLY5yFjeiWqpHQjmvHfjYRDMTLUI8g7kY1m9M9HAqArMtZBnopkwrKy3dzzSne8rqh0a4B6Y9wL/JGw2Z0XF1NQGdXZKB9jDnCNG5sPGapIXPjZ/HnA6NGjee6553jAnXsa8UVFbJ+Eh1EoVDrxRYX3MkT3ZPDjqAleXGRmZhJUUiI2miAISEhIYHZWlokKw8CEhWEYhmF8b+KJig0oZ6EB6jexEhnxdxCKiqmoQtIQ5IFIRGLkJWRUp6FSqpchI7oE9aQY6v7/P5SLcZ3bjy8j2x4Z4atQ+FMzyvaCSEH5GEnu/wLkiShGRvrvKC8uTkFJ4K+7464DBrnXffJyReLCz+YvXbqU7t27c+qpp3LNNdfwKMovOS3qWlaWLP04Cov6JdUTFd+XjIyMSvtOxKOm6xvGwYwJC8MwDMP4HlQkKvoib0FLJAbeQnkDIygvKhKB3kgYRNzyEuA44FLkTdiNvB+73fpFyNAfhMKoAncc38eis3te7sbZAiVEe3ERLSrykafjDLcsnrho5563IQFyBmX7PVQmLpo3b87SrKzSpmtXX626S9dccw3PIk9OdfskPO/Wj632VFuiwjCMPceEhWEYhmHUkHiiYj1qGOcb3LVF+RLtUQO5v6Gk6xGEoqIBEgy9CPtQ9EAdqqcgz8d3SCgkovyIyciLMBQ1oWtH2GCuMRIKPlfiORSKdIV7+B99LyqSkVdjKxIMi1Hi8mdu/E3cvl9E3a6PQknbsY32KhIXBQUF5a7d1VdfzdFHH02/fv32OFnaRIVh7F+YsDAMwzCMGpKZmUl7JBC8qPgBykFojPIZGiOjvwGqaPQlEhWDCT0PuW5/SSjXoh2alS9AydzHE3bOTkTN7nqiErC7UG6FT9T2vR68uJiJKk8tR6IgQOFEOygrKopRkvhzbr+tkDekMaoe9SIqeXsk8UWFJ7o6U9OmTdm+fXuF1y89PZ25c+fSr1+/750sbaLCMPY/rI+FYRiGYVTA0qVLSU5OLrd8x44drEMlUd9AoqIYiYMZwBco5GkFMuIPBW5GXoaGSADkIk9EEjL2vTh4BSVKn+32l4RExTKUO9ESVVgaj5KiA/d6Q+RdKEGds18CDkd5DNuQOBiDvBNNCbtxNwL+i3pMbHSvf+zGPQ54yB27PxWLCihbnSkrK6uSNYUXF5saNKhRn4RNDRqYqDCM/RTzWBiGYRhGHJYuXUrPnj3j9qXwrEdhPM1RdaZ3gDdRKNJxqPpSHupFsRJVcuqLhERDJBIaEYYyfYaqQx0PnIqM/wRCUZGNStGeCHwK/NWNYzjyYDREomIcKknbyI2vBImYR5CAGY1CrLyouN/9/yDwAfJwZCGvyTx3jIfdWKK7TXuicx6ys7NJTU2Ns1Z50tPT2bVrV7XW9dR0fcMw9h3msTAMwzCMGLyoyEBJ1L6zs2fFihWADP9FyNj/Bwo9OhKJiBUod6Iv8mh8iqoqPYZ+fFuiHAafWzENJXqfivpZLENeg2WEjeHeQqJjLOo6vQ6Ji0lun28iUZGOhMVc5PH4DoVX+epPz7llLyNB8B3qY3G+O3YO8njMA7766qu43aY931dUGIZx8GEeC8MwDMOIIlpU/ISyVYsikQg5OTmkpKSQjnIs/oeSnjeiEqrnIDHxOjL0z0OJySuQKJgOvADchTwY24CvgHeB090621GFqRko1GkpEhUnoLK1vmlcMTLq/4EEzgxUsamXG9dwlDg+HwmRzu4Yj6Pci/+hcrcXIVHxpDtPn/vx1VdfcfTRRwPxm9+ZqDAMIxoTFoZhGIbhiCcqoGzVIy8qfJ+JXwGfoFyGdsgzcDbyRowHPkd5C6NQ5aV3kLB4FLgQiYq3kSAZjrwUoPyK5cjj8C6qLvUTwr4UecBIFAL1MPIwnEcoKo4GNiOPx8XAmW6/vwf+nxvbVuQx+R8SHt+3OpOJCsMwwISFYRiGYQAViwrP9Sj06FXkVbgQJWjvQCLjEGSkbwC6o8Zzq5AX4ERCEXAaSoJ+AXkcslFDvBHIO+FTxRPc8lXueP8DnkZeEZ+30Qj4EfKGTEGiI1pU/M/t90dR5+FF0v8hL8v777/PkCFDSqst1bQ6k4kKwzA8JiwMwzCMek9FoqIEhSo1Q5We+gMdkMH+TyQ0LkAlZBcDS5Cx3whYiJK3b0EejdeRxyIJeTQaAM+jfhGLUH+K4e6Yye5RgpK6fU7GVCQ+fMfqQ1A52G9Q+NVq1IyvIlHhuREJoseAIUOGkJ2dTWZmJkElJWKjCYKApk2bkp2VZaLCMIxSTFgYhmEY9R7fl2IoZUXFPNSEbpd7pCPPw2Goi/a5KGehEBn5iW79L92yHm6fuSjM6ES3bQSFPkVQInVblIC90+0zAQmI1W7bQ5AXoiUKYSpCoU1PIc/HESgMqiEKvfofypmIJypAXpABwDXAs0CvXr2qXfLVU1mfCsMw6icmLAzDMIx6T2FhIZFIhHdQYvP1SFSsQ8b8ctT0zs/Nj0RN5JqgsKQi1KiuHfJILEeiohsKUZoDnOVeD9wD5LmYB3yNvAvvIG/HWUhULAdau301R2IBJC6mu+1w400CJrp9HIPCp+KxCuVddAJOcucX2zHbMAzj+2DCwjAMwzAIcwd8VaQjUSJ0CkrIXoMSm/siYXAoynP4Dhn9zZAHogsKl2qMRMVE5F04zx3HF60NUM+K6SgEajTqsj0ZhTJ1p6yo8FzgXn8ICZpDUFWon7t9HYY8JzcAV1G270S0qOjuxhLdMbtZs2Zs27atZhfOMAzDYX0sDMMwDMOxcOFC5qPwohwkKkA5DS2QsJiPRIHvC9EceS5K3LoJaNbOi4ozkKgojjpOxL32IgqVuhZ5KFqikKe3UPft7u640Sxzy092xx7plv3djfNb1GH7S8r2nYgnKqDmHbMNwzAqwjwWhmEYhgEsWrSI3r17kwEchXIiDkEeApDR/g4SFiXIm5BE6KkI3PKGqH/FROSJGI5EyiokVDqj/IzxKOn7MsKwJ+8JSUXCosgt8/svQuJgOuqMPRSViW2LBMRu1PF7HfJA+KZ2O1BORTxR4ftQLF68mJ49e+7BFTQMo75jHgvDMAyj3hMtKn6CyrqegrpY/zdqPe+5WIhKte5AoVDeUPfdrychQXEG8gQsQV6NHORNGI/K0V7hXl+G+lREkHD5AAmMFkiseHGRiErLjkPJ5Peg6lJXIQ/GHCRebkVhXA2RuHgM9cswUWEYxt7EhIVhGIZR74lXFepWlGz9Cmok5/HiYinyCuSjClAJ7nkiyos4A/W0WI08BR1RWdiHkYi4DImT1e7/FOTVmIISuBehErW7CcXFG8iTMhAJhN+5MY0DPgZ6Iy/IzcBtSFxE3L6eBZ5w65uoMAxjb2DCwjAMw6j3fPfdd6xDRvtjbtmDyJtwEdAqZv3TgKbASmS4FyBR0QxVd1qMjP1VKJm7Fwpjeh31xdiKvBYrga6oetS7wIeoUlNnFGY1Dnk/drvn54AhSCDcgMTESDfWjsDfCL0SNyBx0ceNeb5b705MVBiGsXewHAvDMAzDAObMmUNGRgYPol4Q24BRhDkW0XyNQqB6oYTr7Uhc+OZ1O5FwiAAnuOeuyEvxMDATeTyuAo5FAuE1VCL2PPf/q267F5BQmY76YFyDPBij3XEeRGFRzxOKCl/S9noUtpWLRNI8KO2YbaLCMIzaxjwWhmEYRr1n7ty5ZGRkkIEM/ulIIFQkKlYjUdENGfAtkbeiAIVGHQtc4tZ9w21XjITKzSgvIkJYuWk86mlxPsrbOAV5IhqivIrPUFL22W7/uOfTULnY1ii8KVpUADwDvI+ExLRp0wiCgPymTU1UGIaxVzCPhWEYhlGviRYVhagi02VINPjSrp6vgbWE/S0CZPjjnpORMGiKhEFjlHNRhDp0N0BehESUIF6AQpSOB05F4mQXSvJujQTGR6h3xgnuta1IpGx3z5e6Mb2JksG9XHgG+DcKeZo2bRpHHnkkgPWpMAxjr2HCwjAMw6i3xIqKZsCPkRdgHqrmBBIXX6Myrn1QPkMeqtzUAIVFNUCei+bIc1CCPAolSFzsRh21C1DFqN0oofpolMdRgATIGvc4BImXbkA2Ejl90Q93vttvCfAS6kMxlFAEPUN8UWEYhrE3sVAowzAMo14SLSoGI6N+GGGn6nTkeVgGfIFERW+UWP028FvU1wJUxWk38ig0cPtKQB6N4UAGMv43oG7dIJFxJRIsf0OiZiVK+G6HelNkI09EAurm3Q9VnDoMiZovUM7GciRCEjBRYRhG3WHCwjAMw6h3RIuKn6Cu1Yeiykxj3TrzULhROxRq1A6FGb0JTEaei9eBqUhMNEShSQtRXsMSt/x9t6/BqNJTQyQ4GiFxcazb5zcoFyKCRMRnwAQUFjUdhUz5H+1Wbgz3AwtQQvZDwF2YqDAMo+6wUCjDMAyj3pGRkUELwr4Vu5Ax/zkSGbnIy9ATeQK2Ie9BFjL4T0TVmz5A4gLgTJT4Pd/9v8Dt7wskKk5EYqIJquZUhATFe0i0fOe2S0VC4iVUDSrXPR5CouNGyvahWLFiBV26dCESiZRWfzJRYRhGXWDCwjAMw6hXLFiwAFCY00OEXoh1qJTr88BTaPb/NLfNcuBJ1PV6JErEzkOCIUA5FGtRiFKi2/c0JA6ORaKisVteQpjU/QgSGeuRYDkHiYmnkZgZgkKl5rnHg6jZ3XuUFRUAQRAQiURMVBiGUWeYsDAMwzDqDQsWLKBv375kIG/F2yhZ24dEdUThSlOBF4EfoCpPz6P8hkLUGbsPaoS3HeVl/A+JhEtQOdmJqKFdMfApClu6Bv3o7kLlZZ9Gid6tUfnaD1BS9hLkzTjf7evnKNRpU4MGzNu9u7QPRbSo8ARBgGEYRl1hwsIwDMOoF0SLip+g5nTtgP+g8rHtkRfiCtQz4v+A3yBxMRgZ+segUKn7kNfhHCQ6vkQiYSaq9DQbGIS8IOvdMZq4ff8XGIM6bPcDXnbLC5EISQAuRKLiKSR+1gPBrl0AHHrooayYMaOcqDAMw6hrTFgYhmEYBz3xRMVyJBRaoSZ2bwEXI29EH+SFeAeJjHOQMOjmth8D3IvyKN5HYUvXoYpO/0O9Jc4CNgIt3LpjUBjTp26fR6FQqcGoOtQW4Ah3nNORqHgA5WxEeyI2b95ci1fGMAyj9jBhYRiGYRzUVCQqVqG+DycjcfEcCmFqg/IjNqAwqfYoPKkpkIaSuZNRmdeXkCcD4GPgpygXoh1KtG6HwpruBv6EvA9DUXjTj4Eu7jlAIVg93HEfQAnasaLCMAxjf8aEhWEYhnHQEisqrkY5E6uQgOjs1rvYPT+HBMAu1LRuMErYXo9EQlNUvelYFAr1AqrYVACMQqFNu9z+l6DE8ERUdvYvKOSpG/rxvRDlcTwNXI6Sv79Flaeex0SFYRgHHnXSx2Ljxo385z//YdSoUaSmptK4cWOaNm1K7969ue2228jJyfle+01JSSESiVT4GDRoUO2eiGEYhrFfk5mZSXvCsrKgUKMuSBCsiFr3ImA0Eh6noXClHUgcnAp0RcnaSajD9lHI4zEdlZo9CzW42+b2v8HtvwT92EZQg70Gbtn5KGTqXZT4vRzlaExCSeR9gUgkUnsXwzAMYy9TJx6LO++8kxdeeIGEhAT69evHiBEj2LZtG9988w0PPvggTz/9NBMmTGDYsGHfa/8XXHABycnJ5Zb36NFjT4duGIZhHEAUFRURiUR4B3icsKt2N/ec4559GvRFSITkIVHRBnXELkaeh1ZIXASoed37SFRchkTFaiQ2OqME7jWoBG3EbV/itvVC4wLUsXscqgj1hTueH+8DSFyY58IwjAOBOhEWbdq04Q9/+APXXnstHTt2LF1eWFjI9ddfz/jx47nkkktYsmQJrVu3rvH+77vvPlJSUmpxxIZhGMaBiu/v8KD7vzJxUYSM/4Yo7CkBhT6VEDa3a4DCl8YhT8avkWdiDfJqdHL7zEcio5nbb+C2TSAMFyhCwgRUHSoF5WjcgTpog4kLwzAOHOpEWDzwwANxlycnJ/PUU08xZcoUcnNzmTJlCpdffvk+Hp1hGIZxsFGRuPCeihyUJ9EUGf9tCPMpipGoSHTrvohExcnA75CgWIWESlckMlYjkdAV2Or2VeS2b4hERpHbd0OU49HA7Tsd+IpQXCxCYVsNGzZklys5axiGsT9SJzkWldG0aVPS0tIAWLVqVR2PxjAMwzhYCIKgtHv14ygECSQuSoBslDydiISE75Cd5P4uQj0onkLlaP+MEq7XoNCntkhgeFHRxa1/E2qQ18Ad04dDFaNwqMZuHBegvI3NSGh8BZyBumyvB7KysmrzchiGYdQ6+11VqOLi4tLk7fbt23+vfYwdO5bc3Fx27dpFhw4dOPnkkznppJNqcZSGYRjGgYj3XHi/+XUoF6IIeSm2AIegH8cWSGA0QALgDfc4AgmQqW6blijZej0SGSlIVLyGOmx3RB6OYuA8t6+I23cxyuVogsTHdJQ4/iPk5fB9LObOnUt6enrtXxDDMPYq8+bNIzMzs0bexoYNG5KVlXVAfub3O2Hx1FNPsXnzZpo0acIZZ5zxvfbxxz/+sdyygQMHMm7cOHr27Fnl9jt37mTnzp2l/+fn53+vcRiGYRj7H15cPIf6UuwEWqOwpzQkKAopKwB2oGpNnVAPihdQ2NIZSEjMdftJoayoGAwMQ523X3H7GokERZIbT7Fb/0WUs/EjFJZ1mhvTE0C/fv0sx8IwDjDmzZtHv3791NfG5UllZ2eTmZnJ7NmzyczMZPv27WW2iUQitEefeT+h0KRJE7Kysujfvz9ZWVmkpqbWyflUh/0qFGrOnDn87Gc/A+C3v/0tbdu2rdH2Z511FuPGjWPJkiXs2LGD5cuX85///IcuXbrwzTffcMopp7Bx48Yq93PvvffSsmXL0kfnzp2r3MYwDMPY/5g5c2a5kq2RSIS+KGk6WlSkoJyIFqgB3g6UgF2MPArnoDCn91COxonAFGAZKi0bQSFRXlSc7h7LgB8iofAiCqX6AuV1JLn9PYd6YHhRkeuOeZvbRzs0i2kYxoGBFxUZqKy0Lx+dlpZGi+++o1evXrTcsaPM91MkEiEduATIQOIiEonQ+rvvSEtLo+WOHfTq1YvFixfXyTlVh0hQwymQn//850yaNKnGBxozZgwnnHBCha+vXr2aE044gRUrVjBixAgmTpxYa/W7c3NzOfLII8nJyeH222/n3//+d6Xrx/NYdO7cmby8PFq0aFErYzIMwzD2LjNnzuSII46gHQpT8p6Kvqg87JGUFRV+Cilwj3zkuWjsHgmoDOxkJBJORCLiE+SVOAxYikTD6chbsdQtPxTlbvwe+AwZDoOQuHgThVdluvUORaKiLfAM8C9gDhYOZRgHCtGiwjfmfBy4H1gHDAA2AScA/0PhjqDCDbeiiYuxwD9R3pZf/0TgU+Qhzc7O3meei/z8fFq2bFktO7jG0x9r165l0aJFNR5UYWFhha+tX7+eIUOGsGLFCk4//XRefvnlWm0K1KZNG+644w7uuOMOJk+eXKWwSEpKIikpqdJ1DMMwjP0XLyoyUF+Ktwk9FTcBPVBYUjxR4X99WrjXfVhUExT6VAK8joTGLUgwvIuqQk1FJWZPQaKivdtPA/faOnesKe7v/6Eu3j9HDfIWIy/K8ZioMIwDkXiiIkCTCd8hcbENVX3rgb4vHkOeUS8qQA00dwJ/R99Bt6JQzTTk9ezVq9c+FRfVpcahUM8//zxBENT4MXz48Lj727hxI4MHDyY7O5uhQ4cyceLEvWLU9+nTB5BnxDAMwzh4iRYVP0GzfodBqai4GYmErW65LzkbLSpACdq+mV0emolLRN6I85CYmAxcDwxBs4hHAxtRxahWQHNULvZVt+7hwD+Q8PgMCY9NqNleDzeefNSJ20SFYRxYVCQqvPfzB+h7aDiQiiYjOqEqcOnIWwn6vtmGvkP8+mlIhPQHrkWhk/tjWFSdBmxu2rSJwYMHs2DBAoYMGcKkSZNo3Lhx1Rt+D7799lsAmjdvvlf2bxiGYdQ9saLialT5aTvKVzgTGf4RVK2pGfoRb04oKvJRF+016Ic+QNWiGiFvQyIKfUpAVaJAYUutUYhUW+TRmAQcg3IsfKL4EJSnMRq4AomJscBLSMh0BbJQFam5mKgwjAOFqkTFx8CH6HuoCwp/ykXfJ6OQ5/J15NU4FXkz30d5V73Q98EyJC5A4mJ/9FzUmbDYvHkzgwcPZt68eQwZMoTJkyfTpEmTvXa88ePHA3D00UfvtWMYhmEYdUc8UfENynk4G7gBJUbnoTKxuehH39MSiYGngYFolnAtClHoQdih24dNDUHhTo8jUTEQCYQ84CTgS9STYiVqprcY5WMMRHHTg1CI1HVu3QeQcfElJioM40AjMzOT9ij0siJRcRGafPAsBnojsdAdhVm+hsIiV6AKcme5dXPdsn7o+wj2T3FRJ1WhcnNzGTJkCHPnzmXo0KE1EhVDhgyhd+/eTJgwoczy119/nenTp5dbv6CggDvuuKM04fzOO+/c8xMwDMMw9iviiYoAzQwegX7YX0D5Di1RiEEL5JXId4+XUazzKtScLhvogLwaLdy+ctzrjZBI+ADNMF6MjIDjUaWn9u7/FchbciRwJfC5G4vvXQHyWHwNzEPhUiYqDOPAY9euXaxDBR4eB3ahcKbtSFRkUFZU9EYipDdhU85hwIVoMmIkoahYiL6r2iEvamfUb6c/KhLRDgmb/YE68Vhcd911zJ49m0gkQps2bbjpppvirjdy5EhGjhxZZtnSpUtZsWIFeXl5ZZZ/+OGH3H///XTp0oWMjAxatWrF2rVrmTVrFlu2bKFhw4bcd999DB06dG+dlmEYhlFHHHHEEeVmCzejxncPIrHxkFv3cvccQSJgM/AR6jOxG/g1EhCvoDKRA5FA6EIoLj5xr29HORsZbvlwJEamIC/Jk0ikTEQeikEokXw3cBzykPhcihkzZjBw4EDmHqCNsQyjvuMrzz3o/r/EPQ9BYU1TCMUChJMLIHHREBiBcrB87aWFKGzqUOQJBX0ffYs8su/hqt7F9MOoK+pEWOTm5gK6AS+//HKF66WkpJQTFhUxcuRICgsLmTFjBtOmTSM3N5fExES6dOnCD3/4Q26++WYyMjJqY/iGYRjGfob/QfezhecjT0IrlD/xJEqyjhYXzVCexb/cNknAz1BCZRPk0n8TlZrNIBQXK1BllwbAr1CZ2CVo1rAVmnVsCfwXiZeRSOC86Pabg/peFCGjwIuKAQMG1Kg7r2EY+x/xxIUXBBPd81nlthI+jKgqUbEciYpn3Ov7U/PMGvexqI/UpH6vYRiGUXf4krI3oITIH0S9tguJiwXAj5G4eBr4KwpZuA55PAqAPigPYwJKojwPiYv/Am+hZO4RKAyhEFV2aYNETBLKy/jYrX8KyrG4H4Vb+dqEvr+GFxWGYRw8+GZ3tyJxsY0wIXskFYsLz/4kKvZqHwvDMAzD2F/xs4VPuP+vQt4D0A9etOdiDoqH3kiYVJmHEigbIpFwgdt2AkrU/hSFMd2ESsPmutcTUQJ3dGjDyUjEvEUYblUQM04TFYZxYJGTk0P//v3ZunVrpetFey52o6ac1fVc7E+ioqaYsDAMwzAOKqorLt5AORJNgdnIw3A+CnFa5tbvRCguPgZ+iLwZq1HYQneUdJmDwp66IpGRiOKpZ6KwhvEo7OoolFfRuHHj/dIoMAyjYnJycujWrRvtkEci9jO8cOFCMjMz2blzJxB+Fz2PvJvtKS8uznTPvtz1DlQtKoEDT1RAHVWFMgzDMIy9SRAEzAeeQD/E0eU+vLg4FHklhqEf9feRZyIBJWAvQwLCey7uQlVYVrt9dEKhTKDu3auQAVCExMMEJCpmuNeHoqTv9UBWVlbtnrBhGHsVLyoyUFGHvkhceBYuXEifPn1oVVRUutyHQ12OcrW+QxMRJwE9kbjIQ8UmvExogprnlaDQqSI0cRGgPhf7s6gA81gYhmEYByl+tvBlNFvYhdBzcR7KkxiAvBb/D/3Av+Rev5hQXOD+jiDPRgtUIrIzKgG5HImMFGQAfIQMgrYodKoZEi/3o9CohQsXkpaWVvsnbBjGXiFaVPhy1o+j3jORSIQFCxbQp08fuqDPfRJlcyxuRN8vm5FH9EtU8OFcNHERLRMi6PsFJCK+RN8ty936W4jvLdlfMGFhGIZhHJT4H/YrUAiCb4aXhfIreiNRMRolYntixcV89KO+FkhGoVCdkQHgn5chcbEMGIMqS21EDe9mA3cjEWKiwjAOLOKJCpBYAIkLLyoykGfyN8ijcWvUehEUIvmRe5wHnOGWR3ss/LrR4gL0neJ5hv1XXJiwMAzDMA46YmcLQcKgADWrK3T//4SyomK0e/biYjCaadyEREVfQjHhgyA6uecXUDhVRxQnfRZwAvAsYSUoExWGceBQkajwXI++S55CleBuQRXnUtB3z3UocXube3yBvJnnAqcTfoccTOLChIVhGIZxUBFPVGxGP/DJqFdFe5SI3QGFL6VEbT8aWISMgE5olvEQoBcKc9jllkXTAiVWrkVCJQ2JkMVoZrI9YdjE/mYIGIZRnqpERQn6fI9AeRPPoepyXVHVuOuRUChEouJT1LfmPCQqvOBojPK6DhZxYcLCMAzDOGioSFTsQMb/BtQJ+xQUppSARASE4uI5FP88EomDXCQqOiFRUezWixYXTVBC58Oos/ZxyMvRHoVHZLr1TFwYxoFB//79aY9CmyoSFQkon+JqNBGxDomG85CggLKiYiQKfwpQ+eolKIm7J2WrKeUTNsmLoO+fPFQcoi9lxcVY9q/vFKsKZRiGYRwUVCUqWqIf6OGoDOwsZCAkIXGRg0TFFNTTYphb/2SU+J2AxEQjJC6K3DGKUbWXAW4bXwmqG/KI+FnIG4HbKF9NxjCM/Y+tW7eyDvW6eTzmNd+/phHqXzPJ/X0M8LXbpgDYioTFe0A/JCpK0PfNgqi/l6Lvk+3o++dnwFR3jN1IxKxFvXPaIK/IIcgzOghVuNtfvlNMWBiGYRgHPJFIhPZIEFQkKjx/QcbAWGAaobh4EhkIw9EsZVMUOtUo5liNCMXFd2gmcbF7XILCIGYAryBDIdcdw4uL01HX7eTk5No4dcMw9hJBEDAPeJCy4qKBe26ESsa+C4wCXkae0DFuWYC+W04lbMiZjRrgNUdlZ9ujMKclyNv5KhIOE5HIWIJESDNUOCKCSluvROGXi90xAJYt83Xs6g4LhTIMwzAOeGbMmMERRxzBe0gwnEN8UQHwNKrYtAV5KEAiZCFqkHeeW+YTLpvFOZ4XG/koPGGt+z9C2FBvHDJARrn/26BE7vdQL4sl1svCMPZ7ojtogyYHWqNQx9dROdhL0YQEwF+BXyJxARIVA5EH437gaDS5kI6+P5qjEMvxyJN6AQrV/BB4FDgCeTr6Ig/IXPSd8xUKv7oUCYv7gR49erB06VK6d+9euxehBpiwMAzDMA54BgwYUCou/olExUXEFxWPow7YX6Hch3+jBMkfoRjoRORhgLBErRcXEeShaAHsRDOHG932JYR9Ly52677g/h8F/BflYMwFlixZQo8ePfbwrA3D2BfEiovr0ATBF8hLOQx9/hPQZEK0uPjOvb4TmIcmMY5HgmItmtBYikrQbkKTGQEKgZqPJiGORt9lG1Do1DfAdDQR8iN33ATgX9S9uDBhYRiGYRwURIuLR5Bhfzn6AYeyouJht+x4ZPD3RLOKEcKwJR+o5MVFMgqVeg04E+VfrAd6oByMXW775W79i1DYwyfoR/95JCqmTJliosIwDjCixcVW1A/nSOR9KIlaz5eivguVlX0EhSt9ivKutgEPIRHRAX1fvI48E18CTyCPxOeooEQ+8BjwLfqeWYZETV/gBsKcBp9gXtfiwnIsDMMwjIMGLy7mIfHwPApBiCcqHkd9Jzai2vLjkTjwtVUiyDPRDP24v4Lin1uhEKrXUTJle5R42QiJjS7IWHgK5XAcCkxw2/UCzjrrLBYt8rWoDMM4UPA5F5PRd8kslGSdgMSFf0xAIZWbUQjke6gnzli3PAe4zy2fj74z3kbiwa9/PPoOuQT1wfkX8CZquHka8sqOoayouRqVxs1A4qIuci7MY2EYhmEcVER7Lh5GoQafoKTKaFHxAPIogMKb3kPJkedQtgFeC+ANlJh5HPrR/hr4mFB8tI86fg9keDxF2CfjJOAqd/x8IDMzk++++64Wz9owjL2Nrzw3GuVaPIYmHEBezF3os/8oqgKVg75PTgO6o++LS936LyGvxLHI8zkMhUSdB5yIPBINkOczQJMkX6EGfCOAfyBRczZhk06QuJiDPCKZmZkUFBTU3gWoBiYsDMMwjIOOaHGxGf3g3+pe86LCN5xKRx1z81ByNcgYABkCryLjIRNVaylAyZRN0CwjKIfCi4txbj/foWTM5sij8TqKo14PBCYqDOOAIl456x+551dQTkQREhunou+ELOQJbQakunU6Ia9CL+BvaNLjdOSZWIS8He3Q9xKEXbx7o8mKTW6d6UB/lLsREE6EPI6+l9YDwT4WFWDCwjAMwzhIiRYXoFCCOehHN1pUeENhN/B3yoqLV4EXgRPcYz0SEOvQj/oWJBZAhsTHqDRlW+Sl+Br1wXgPeU58NalFixaRlpZWuydsGMZeIZ6o8PwITVzcj0pUn4SEQg/3AIkLkNdyN/oeSAQ6osTsH6JwqBRkmL/q1h+Jwqwao++j+cjT0QxVjzodeTlAHpEnCCdN6qphngkLwzAM46BlwIABioueN49+/fqxCYkDoJyh0AA1pvLiYh6qvjIIhUBtcNt0RzOL81HIAkhcrEEJmvloVvIb4HpkEHREP/oN0axm7969WbhwoYkLw9jPqUxUgCYMIsjYPwVNOKxHYZa9UOUmKCsuXkf5Ejcg74bvi5GAQqKgrLiIoKIR7yPP6hWoMpVnKSpC8Sx1KyrAhIVhGIZRD0hPT2fu3LlkZmbC7t2kAddS3lCIFhf/Q6IiBZWFzECiYhcKiYog42Go2/ZjlEjZGCVb/hjFP28BzkKGxxMoORNCcXH44YeTlZVF7969a/28DcP4/lRHVLyPch9uQd2wdyCP5UK3Tqy4WI4KRpxL+YpSvmRtrLgoQSFOG91YokVFZyRS/oNExfz586lLrCqUYRiGUS9IT08nMTGRdig8qS9hadhoEtCP9/+hH/T7CUtD7kTCIhElevdG5SOHovKSRyKPxMVoprEAiZXmKKnyx279du74vXv3pk1REX369GHhQm+KGIZR17Rq1Yr2KPE6nqhYhLyTnyMvRQvU1+YwNCHRAYkLXyDifJRHsRQJijPc8mhD3FeVAomLfiiv4kHiiwpQUYkpbt0MoG/fvixYsKCmp1trmLAwDMMw6g1ZWVmsR8ZAFvrRjxYXAUq6LkGhB6+inIrPUG7GbuTqb+jWTSMUF9PdPn0iZq5brw3qjLsNuIxQXOxG4mQkMgpMXBjG3mXBggUkJiZWa91Zs2axDlV5ejLmtW/d82HIKzkBVWjqivIstqK+OPHExb2oEpQXEL65ncf30XkT+AB9j/jJi1hRsQV9RzVH3pChaNIiMzOzWue4NzBhYRiGYdQbUlNTyc7OZi7qRREtLryo2I3qzb+Aki/HIg/HJOBDwpr1uG16IY/G2yjB+59o9rKBezRFwqKEUFwch0ImLkWJm9di4sIw9iYLFiygb9++HFJcTCQSqXL9lJQUQF7HX1FWXPitE1Fe1UpUqGEDobDYSnlxUeC2SSYUFBH0PZFA2EPnLRQ2uQl9N/zKHWNMzBhbAxe6/V5MWA2qqKioyvPbW1iOhWEYhlGv8OKiV69ePBfz2qFIUDyFQp5WIQFyLjIYJiMD4HS3fgKaqfwEVYe5jrADdwOgECVbtkQ/uMWoxv17qMHWb1Glqv7AbSjkoU+fPixYsMByLgyjlvCiIgPN6r+N8icqS3L2+RXDgHeRcQ8qyNAKNb/7LcqvuAF5Hye6dY5GwgIUHrkZhU41RmKgGeW9FKDvjKkoETsXhVRdG7WeTwCP9lxcjDwXdV0NymPCwjAMw6h3VCQuJqJZwQRkPOQgj0USCnloiCq6gMTFWyjG+XTkeYAwVKoZMhTykJHRGDW5ehzVtP8vsAKFVXRGFWUaorK4Ji4Mo3aIFhU/QblOvpdNReIiNmn7aeSJjBYXf0Wi4gIkLFq41ya654HI4J8DbEdFICoSFb4PxVvIM3qWW/YKmuS4lvLVpby4WIFCMq8CnqnknPYVJiwMwzCMekmsuFiKKjstQs3vegC/RvkYPgThbPf8Ogpt8DkVPyQMj/AEhMZGHjISnqCsqFiORIWvd3+1ezZxYRh7TjxRAWEydqy4yMnJoVu3buUqQV3jnr24+ApVgxrm9unb0J3lniegkMrmyPPQDX3Gm1JWVASUFRUTCTt170YTE15IxBMXw9B3SBc0MdEizjnta0xYGIZhGPWWaHGxCcVIg8rK/hjNCp6BQh6ixUUJClkYRigqooWFNxhA8dTjUYx2T9SZO56o8FyNZjk3oSTMnTt31sq5GkZ9oiJR4YkVF8uXL6dbt26koT4RsZWgosXFOJQj9Qn6XhiKErcBhgOzgYeB0aj6W3ckKgLCkrLRoqIQiYoe6PumCBno0UJiN/KM+GXjUN7FVW7/8c6pLsSFJW8bhmEY9RovLryo6IoqNV2Dfsx7AX9CImAM8AaaHfyley6M2V+0yAhQffn/IBHiRcUyt78uhALE8zhhEqaJCsOoORWJit3II+m5EeU29YVST8WPUP7TYrfOIrcd6DvhTsLJgEKUvP0R+lwXoNDID4BjUN5FF+S5aERY+CE6/CniXj8dNdV8ComKRPfaBSiP4k3kyQQ4DzgCmOHWi+ZGt692QPPmzatzuWoV81gYhmEYhiMdiYUNqALUqZQVF6OBF1GoQjL64c932zanrLEQQfkTL6DY6ntQzsYqFG99KKpNn4xCGCKEsd/7QxKmYRyoZGZm0h55EqJFxf9DidhnAH9xy29AoYoT3Lo3oM/pMuDfwJduP/eg0KRrkADIQ0Z8e8JGdg1QYYaTUcW3Q1CluR3o+8F32I72WhShog6D3LoTkHHucyiKgDPdNi+6ZanATCDTbRPNWDeG9cDSrKzqXK5axTwWhmEYRr1m8eLF9OrViwzU5O4BNOP3ChIXIKPkBTTreAlKxN6JfuybIXFRgMRBdBhUG1ReNhcZNatRUvihaLazsXvOR6LifsK+GsuXx2vfZxhGVRQVFbEOeAd9rryo+Bx9tj9G+VMB8kwMRKGKN7jtU5CBPw2Jis/d9rtR48z/oZLRv0ahSBciT+Z7wPFICHRCYiIZiZACd7wG6HuiBAmYD1G+VqLbzyUo32IMoeho6F47GomYvyMP6PXoO2WZG/dY5NWYAyxdupTu3X2Q1L7DPBaGYRhGvSbe7KaPY34JGROfImPkR8AIVD4ykbD53S4kDiLIkIj2NVzhnp9EYmQkShRPRYZHIao68yiaZfwRMlC6d+/OsmXL6NatW62er2HUB4IgIBKJ8G/U7HI7cBMwClVPGgPcjMKMeqCQJc9vkEfgRpRDNQEJlKHoM3o48nisQx6LRkBb5HXoSziB0Ivw+2Er+l7wnotlyDO5FX13NEYTDhe4MYxHouIaZKxPQSFXq9F3TCskgCJoMmIS+h6pS1EBJiwMwzCMes727duJRCKls5s3Inf+cDRj+P+QiLgZCY5cZAS0QkZDMQp18j0roLy4GO2en0RC5UIkTpJRTPZY9//FwH3As2jm0cSFYXx/Fi1aRFpaGg1RrsUot/xK5DF4HImCB6K2+Q0y4K936xWhnIbvkEcRFBaVjMTKGOTB+BHKqViIQiDnIwHRBVWZg9BzsRNY4P733osF6HunN5q8KEbeiQYoX+IhNCFxMfKSjnPbXowmIp4C5lK3ogJMWBiGYRhG6ezmg+7/y9GP/RxkeIxG1aByCXtceFGRiH5MWyEjJJ+wrKQXFwmEnosxyFi4ENW5f869/lcUcvEKMmjAxIVhfF+ys7NJS0sjA4U4nYw+b63RZ9v3nXkaiYk/U15UgD7/24DByPPwHGpk+XckEj5EDS5HoM97R1QVKht9f+xCHpFmyAP6HGqa1xt5NFJRA813gHluH13RJMYKt/4ulI91HvA39P1xNwrPXIxyPtL8sXr0sD4WhmEYhlHXeHHxAPIqrELlJG9BP+hbiC8qGrntl6Mf1ZaECYw+58J31o0WF6Cwh7ORmJiHZh9fdq+ZuDCM70e0qPBVoZahiQGQuGhIWXFxDvqMR4uKAE0UFCIPxTWo0MKjwC+Ae1HOxrsox+Js9L2xHoVJbkNVpRq55/8gEZDt9nUpYQW501E1uPnuf//9sxx5OK4kFBUgYXG3W2eg+/tnKLQqMTGRoqKiml20WsKEhWEYhmFEsQpVg0lAguIKZFg0QvHRzVCVlwaEP6KPo67ao5Fh4qtDeaKrRV2JjIx33P6PdPvxTa8ykIFxKmUb5pm4MIzqES9vqjv63OUhkZBAWXHxIqrEFC0qdiGvwSEo/DEBhVNloZyHM5A4AHkj1qGJhyZo8qEPCnH8DxIA69DnfQUSI51RcQf/3XA68AVh4YjlwBrCBnteVHjuRhMSvd0xfDWo+XVQDcpjVaEMwzAMAzWUao+Mi75odvM9FMtciAyF5uiHsxHyahQjUfEIEhv/QfkSO9xrIIMhgVBojEfJ4EOAE5HRMgB11v0f6s59JppVBRlGQ1GcdWZmZoXjb968OcuWLavwdcOoL+zYsaNMVSiQx8J7FHei/AhfcemHKOToCuRd3ISExUTUEG8KMtjXIe/GR6gqVCMkEo5CYuI+VElqKxIyPdBExbtuvQuRgLkVeUeeRuFMPnAp4o49EYVTrUTV4Zajhnv+XKJJR987vhrU/Pnz6dOnTw2vWO1hHgvDMAzDAGbNmkX//v15D4VPzEPGxKPIADmdMCSiETIGxgCPoVjpl4DbCX/8/UyoD5UCiYpHUElKX1VmC5q5HIASRJ8GJqNykhFkiPiGeUF+PvHwoqhHjx51nrxpGPsD0XlT21EORBv0eSxxz7tQXlQimjQoRtWV3kQVmnJR/sTnaHKgAH0+T0bJ1KtQcYev3d+HAF+hSYjDUGWp8ciTOQx9T0BYde4hlHTdCH1fvI4Sydej74Xly5eTkpJSLgcsuit4dInZuhYVYMLCMAzDMAB5A7y4uB2VcvwpCkd4yq1zuntuikpWPgb0RF6NNShnYjcyPqCsuHgRiZRoUeE7cPu+Fle6fT+KRMrRaKaysoZ5kUiEdNS07z1MXBiGxxvkT6HP1WXo87kNlYktQt6LYvQZfQ19hjoB76PP1K2ESdfzUc7FWiRKHkNVoE5A4uJYJCCeRd8H05EH9JaoMW1BHk1f6GEL8lIuQR275+MmEWI+7/HExf4mKgAigbX2rJL8/HxatmxJXl4eLVq0qOvhGIZhGHuRSCRCX2QMXItm4H4NfOb+Pw15FB5GguAtFOawCoU//ADFPn+BEjR/iAyTB1Gs9CvEFxXJ7gFKDH3N/b0R11wrzs+1FxW3Ut7QMHFh1Hf856MzKgH7Q1T+9V2UaH0OMvC/Q+FO7wMnoUmFFYQiIwcJicVu/R2EfWyGoQ7YW1Ao4zDksXgSiYV0wm7aW1CY1CQkJnyidgfk4VhPfFER75z8RMK+EBU1sYMtx8IwDMMwHF5U3IbirXcig+IvqPnVGBRz/ShK5jwEeStWoaTNEjQzejeavXwa+B1KBi90xxhLKCpK0KxltKh4ARk4uSiR++8o5yMSiU4HLy8qQPkYP3Hb9ejRw3IujHqL/3z8GHn/GqA+FH8H+qEqTpORp+JtZKSfiERFd5SLsQz4L/JuJCBjvgPycESAU1CoVB/UgfuHSLTsRB6O36GGduehJO6tlBUVQRAwf/581iIPR1WiAvf6POQB3Z88FR4LhTIMwzAMyoqKG9yybchI2I28Fn9E4Qo9UXJ1PjJWrkM/qB8DZ6HKMXcTlo9NQuES21Fc9VVIjExFM6TXueO9gDwOy1Ec94So8T3gxuhDImJFhSe6kpSFRRn1kcTERNoj70EKEuqtkfcgB4mDnihJ+gtgAwpRTEGiYgYKdTwSeR2ao4IKn6Jys4egggoDUN5ELzRJcJo7/stIeFzu/r8fhWGdjsIqo0Mb+/Tpw/z588nMzCSoZonYIAhITExkflbWfiUqwISFYRiGYZQx1G+IWt4MiQtfPeYeZPS3QaFRbyLPg/cqDEGznxEUMrEJGTP3o664U1ADrQdRY6xhbj8JSHw8jOK3D6WsqPDiwYuLikSFx8SFUZ/Jysqib9++vI1CmgqRgD8DeSHeQaFQxyIv5EkoTLE1yot41r12CeqI/Rz6TB/h/h6IvBs9UWgV6DPsxUUCYfnoy5Fn4RXUvyKH8l6JPn361LjvRF31qagKC4UyDMMw6jVNmzalPTII4hnqzVDYk2+G9z+UpL0RdetdhUTBQOBcVFLyP8CfUH35/0M/tqe79Tu4/R2FjJtLkIj4LRIbv0fhVBfGjONG5E05HomKq1BIVgkKs4glXpnaSCTC7NmzadWqFTk5OdW5PIZxwNGnTx/++Mc/sgB5B45FoqIH+lyNRCFJ65B3cTPyTExGuRFeVHRG3wujUX7FBuRdLEHexy7oO8GXk05A1eKGAYejSYb7UZjVNuKLioMNExaGYRhGvSYrK4t16Md/LGUb24FyLCIoRnsM8jYsRAbD0ShM6hcoVGIe0BaJhiS3XTLydjRFoVSzgbtQ+cuFKKxiIBIbs1CC+C3I0IknLl5DoqKz298hqOTl6zHrPk5YpragoKC0JG1mZiYN8/Lo1q2biQvjoOTVV1/ld7/7Hb3RZ+hs5JHwacdnIHExHzXMOx7lUryP+lNciD5fHi8uVqDP6UXo++JNNOHgG2A2QIb1ZPRZboY8HJ3QZzRertTBRp0Ii/z8fH77299y1lln0aNHD1q2bEliYiIdOnTg3HPPZcqUKd9730VFRfztb38jMzOTZs2a0bp1a0455RReffXVWjwDwzAM42AhNTWV7Oxs5qDQoWhxsQsZDdGioilqXLUMGSp3oOpPA5F3YDWqNvNTFEbxJ6ArcDOqa38j8iYMREmhq5Eo+QX6UT4SCYeKxEVLZPS0Q8miHYBvUJiVFxePo7ApH8vtw6cuRomrP0DeFBMXxsHGq6++ykUXXUQ68iwOQ56KZsjTELjn05FXYiEqFduIsJN2W8IGl55h6PP2HkrC7uf+/pbw+yJAn8PXkOD4HOVfPI2+E3zzzYNZXNRJudklS5aQmppKcnIy/fr1o3379iQkJLBkyRKyXBvym2++mYcffrhG+92+fTvDhg3j888/p1WrVgwePJjCwkI++OADdu3axV133cV9991X4/FauVnDMIyDn8WLF9OrVy8yUGWlKwm9FU8TioqPkHHQAlWbuQIJggTkvchDFaJAsdqPIWFSgkTFNe41/+Ob5/YVQQmj/3LLp7v/H0ax3K+iRHIvKm5FM7GTkOhJcuNq5MYYKypuQo253kTCowTFni8kbMRlGHvK6tWrOeqoo1i/fn21t0lOTmbbtm01ChNq1qwZWVlZ9OzZs3RZVlYW/fv3pwMSCaehxOt0JOJLCMOVvHfhTZRsPQQVXmiN8qN2oc+Sb4bppcADKEejORIhI6LGNBl9ThOBL1G1qCdQmVncPmKrQh0I1MgODuqAgoKC4IsvvgiKi4vLvfbBBx8ETZs2DYDgrbfeqtF+b7/99gAIMjIygk2bNpUunzZtWpCcnBwAweTJk2s83ry8vAAI8vLyarytYRiGceCQnZ2t3xEInoKgCIJHIUiHYKD7/xkI2kPwEwgCCIrdcv/Y5R67ISiB4DEIjnfPgXuURL1eErP8JxB0gOC/btkTEGRCcC4EP3BjewKCtRAUuP3cD8FREAyCIFV2UBAEQYAb+0Nu/Xy3z4fd+RwFQTe3/vLly+v24hsHHHPmzAkSEhJK/1+1alUABO2i3oOxJCQkBHPmzCn9HwgOg6B5JdvEgvsMAsHixYuDIAiCWbNmlX520yFIg+AfECyH4GMIVrjP53fusSvq87cSgqVunXy37DsICiHYGbXeFAjOg2Cw+wx+F/Xa6xCMhuBad/wLIciN2m/g/l8Gwb8h6FuD861ramIH10koVHJyMoMGDaJhw/JFqU499VQuueQSAN55551q73PLli08+uijADz66KMceuihpa8deeSR/OIXvwDgL3/5y54M3TAMwziIiQ6L+jfwc+SpWAb8CM1ejkbegXfQzL//IS2J2VcEeQRGoPCqEbhGd27d3e45OijiCbffQ1F4FMD1wKkoX6ItoaeiOfKgTEQVp45Fs6ytUFJprKci2W3zOor/Ps4d+xC3fm2FRS1fvrzG3v0WLVqwfPnyPT62se+YO3cuGRkZHFZSQiQSYfXq1XTu3JkMNJNfUe+VtiUlZGRkMHfuXCKRCL3QZyMTeRWqChPy7+tLUL+W1NRUlixZQv/+/WmPChZkoc/CWNRXogtKnF6HPsOgz1+APIa7UB5UMfIuFCKvQ4JbFiDPxhhUYvpSFNrk9+U9FUciD2AJoaeiIWHlqNbo8zkCVZ87GMOi9svkbS84kpKSqr3N1KlTKSoqokuXLhx//PHlXh81ahQAX375JWvXrq2dgRqGYRgHHdHiYjwKWdiBhMZY9MO5FFV5eRBVkfFhFYF7JCDjxHfUTnXPhUhceBHiBQZIpDyIDJGPkEFTgsKwnkXhULcRioomqPrUWyje+zskLvzx+6BSl9Gi4jkUA94PVbXx4qItobh48803v/e1W758Od27d6eZSxavDpFIhOSCArp3727i4gDBiwovInpDqaj4CWoieRtlDWffJ+Z8FJqUkZFBL7f9r4GfufVjxUUkEiESidCsWbMypZb/SdgMMjU1lcWLF7MOCfObUYnXHSh0KVpcrCBMtM5H+RJNURno5ijccKt7LkHi4S1CUXEJ+gweQihMXkMJ2pehKlMN0WcvWlR4WrvjnY3yNtpB3In2A5X9Tlh88803vPTSS0QiEc4555xqbzdz5kwAjjrqqLivd+/enTZt2gAwa9asPR6nYRiGcfDixUVekyYEQcCKFSvKJHcnoGTrbEJx0ZBQXBQgEdGMsKN2Mqook4eMel+msoSyouJjtzwRCYe/Ig/JUGSMJCJR8Utk7JyIDK2RhOIiBTXYmw98ggymX6F8j0Fu/YvcODoig6k9cBhw5plnsmDBghpfMy8qKpuxjiV29tnExf5PtKj4Cerh0gWJVV+YAMLyyP590Be9R71nrRe671eh9+uIqPW9uIhEIjRGwrdo+3Z6oU7a8TrNe3ExD3kQelJeXByG+tCsRZ/RrcjIPwyJjYbukYiEfSOUoB0tKoajAgr+s5vszms18mpcjrybK1EOViz+8/82qkK1Hkrziw8G6lwi/e53v2PlypXs2LGD5cuX880335CYmMgDDzzAscceW+39+C+iLl1itWFIp06dyM3NtS8twzAMo0pSU1PZvn07oN+WFStW0LVrV/6FDJa3kfExD4kCUHhDvnt4L4FPzyxChkozZFgkuNfHIMOnEJWiBQmQF93yvigR9F1kLI1E5WqnoQRzPxN7htt2Agor2Y7K3r6HklPXoxnVHii0ayjqwXE/oWdlOBIiffv2Zf78+dXu6hstKn6CDD5fmcp3C48lttHfWCTcunfvzrJly+jWrVu1jm3sO2JFxRUoXLDQ/X8Kek/5Uq03IsP+STRT3x94BL33L3EP/xmJECZCP4A8eQXI2zcINZLsjTxxu5HHAco2g0xNTaUvStp+D31eliDhcb/bbiCaEOiOiib8wB17K/rMtCAUGO+gz1MBcAwKf2qFPvdeeJQgwZ+AwqFAXgtQn5nzCJtd5gG5SIA8ikIY586dS3p6egVX/MCjzoXFpEmTyii15ORk/vnPf3LNNddUslV5CgoKAFUJqIjkZM0b5efnV7qvnTt3snPnztL/q1rfMAzDOPiJFhebkKEeBAHz588nPT2dB5Hhch4SD40IjY+iqL+TkfAoRKFJDyPPQh/gBdTHwouKLshYaYhmcR9GRsoOVI72UjQzmuPGOBz4AoVS3YiExL9RKNUpKN9iNRIW65CR1A8JmhHIi9HFjaO64iKeqIDy3cKjxUW87uHRBqKJi/2PikTF5+geXow8ZlsJxcUq9L5KRMLxD0hEeE/FTpQP0RF5MLy42AX8DX3GLkbC4ghUJe2XqIzsTygrLj5CRv7lyJvohWpPt+0HbllryooKUC7ENuQJTHSP7Sh/qSfKd5oMfIXEBYTlaBPd89nu2YuLy9yzFxfPcPCLCvgewuLnP/85kyZNqvGBxowZwwknnFBuuQ9Lys/PZ9GiRfz73//mhhtuYPz48UycOJHmzZvX+Fh7yr333ssf/vCHfX5cwzAMY//Gi4sjjjiCYLP6Xfft25d58+aRnp7OC8hz0A4ZTUVIbOxGosJnDrZA9e9fQKICZPA/hvI3PqSsqADFhjdBxtQdSASUEMZwL0czrBuQgOiEjK3uSIR8g4yjc5ARNdctK3Kvt0MdiP1sa3XERUWiwhNPXMQTFR4TF3XPvHnzyMzMZNeuXSxdupTMzEy+/PLLMqLiavS+fQeJ2XORod0QeSDykacgAQnpy93fLyBP2XnoM7EWFSpY447dC4mTHsjblo08dj2R1+Aw9L6e6tb34uLvSKhfi/I3thGWdf4HCi8cgErKdkN9Zbyo2Ep5UeEfI5GQT0UCZ6Lb5hzCkrQB8jBGqFxcjELlbA9mUQHfQ1isXbuWRYsW1fhAhYWFlb7eokULBg4cyAsvvECrVq145JFH+MMf/lDtvhNegGzbtq3KMVRVreJXv/oVd955Z+n/+fn5dO7cuZItDMMwjPpCly5d2OxEhSc9PZ2+qGKUN4O9iPD18GPLkXRz629FlWT+jsKdXkOhTNGiYjcy5rogQ24wYfgSaHbYx2xfhgy9RcBiFD4yAs0KT0RG1CAkZBai2d/RqEnYCmTInYkMp9epWFxUJSo8seKiIlHhMXFRd8ybN49+/frRjjA/ph1KtPYVl65GgnYr8rK9h8TExSgXYpfbl+8239gtG4FEdwkKH0x023cgTKoOkCB/A70/b0TG+FtITNyA3s+vEIqLEuRJOA2J6E6En4tLUBjh58hjd407H+9l2Io+D80JE7obEoZmneWeX0ei4Ww3tjXoM9cNfSZ9dbeA+OJisft/IfIWHqyiAr6HsHj++ed5/vnn98ZYSrn66qt55JFHmDBhQrWFhW/ss3LlygrXWb16dZl1KyIpKalGFakMwzCM+ktFxnIEiYlGhCEbsdzo1nsAVcbZjEKZBlJWVPwShTwNR+EhnwI+BqAZMnamoZnho9x+mqOqT63demcgA+9VNNu8Chk9R6OO4GuQkbfBvZaBjLh/EV9cdO/enXaExmZl+OvyHBIxFYkKz9Uoj2UTkJmZaSHJ+wAvKjLQzP5b6H10KRKt3kPxGHA4sBG9TxchcQF6T25A7+kOSCDvQl6I5ui9ugJ9JtoiAfAtMvTbIm/dlyg0bzSqIPWGe4xEgrcEeetAItyL7u5IPPeOOqf/IvFwIxIB2whFxS4kzpsgD+I2t8yLCygrLsYjIXSkuza93Xnhlnsx4sO5lrjrttldn41IrB/MogL2gxyLePg8iY0bN1Z7myOOOAKAadOmxX192bJl5ObmAjBgwIA9HKFhGIZhVD0D72dBK+MG9/wACgvZHPP6YhQffioKcXobGXjFyADcgeK2Mwh7ZRQgQy45aj+B20cxMtTOQ56N2e4YPwDaoDCNIiRSoKyBX1RUVHre7ZDIeRslalclFm50xzysivVw+3vb7T8wUbHXiRYVd6CKYl2B55HhfRt6f85DoUU/RJWRvgJ+i94fryBDfSAy8Neh90435LVoiozt9ShcqRESFY3ca4F7fQryPoxAwuAd9L7xfV0S3D4vQp+vj90+ByER7d/7z7t9neMey5A474IETkP0+diGxEcjQm8LlDWQT0T5ERNRWNaphNXbEtBn3OeIRNDnMQsJlrFITNUHUQH7YblZgPfffx+AXr16VXubM888k8TERFauXMlnn31W7vVx48YBMGjQIDp06FA7AzUMwzDqLdUJ66lyH+75BmS8tUXiIprebvmbwCRUo7+H+/srJAjOQoJgKjKq/Gxw9HF8CdthwD3I0zAPhWc0QiEisaIi2sCPFhV9kWHXD81kP+DWrYpYUbEbiC1s66tJzYe41aSSkpJYuHBhNY5mVId4ogKUXP1jZECvRO/PPui98Boy+C9BnrC+6H33LvJY9AHS3N9L0cz+5+het3frFqL73xSJlG9QblGaW+8+5Bk4Bzg9arwlSISkoNCmy1Euxgz0Pt6GjPm30efiTCQKNrnj7CL83LVyf69Exv9cN+adhPkTvvfMULe/Bci719M9l6D3ajahqBiPxMvnyJNyFfp8HX744RXdhoOGOhEW48aNY/r06eWWB0HAa6+9xm9+8xsAbrjhhnLrDBkyhN69ezNhwoQyy1u3bs1NN90EwM0338y3335b+tqMGTP429/+BsCvf/3rWjsPwzAMo35SU1FRmf+9KnHxODJ65gNPoRniw5FAeAcZUOei2O6JSFxE/7hHd5JIco9maAZ3HjKMehOKiouQYRXPwPfnfQvKq7iJmosLz25Uvecy1GPDn2tloiISiXBIURF9+vQxcVELVCQqDkFG91XAdUhc5KD3elP0njoJvQ+nIZG7Ec3yv4k8BR2RSNgETEeCpB3yXmxEnrnVqATrFPc4C+Vq9EdC4TwkXHwBBC8qQMb+d0h0HIHCA0Hv3ffdPs5Fn50FKPeiExIZWwlLzL6Nqlu9gLx5a5G4KCIsHR1xxxrq9vsRKoTQEHkNWxF+PqNFRYo7p484+PpVVESdhEK98847XHbZZXTq1InDDz+cVq1a8e2337Jw4UJWrFgBwC233BJXWCxdupQVK1aQl5dX7rV77rmHr7/+mi+++ILU1FQGDx7Mtm3beP/99ykuLubOO+/k7LPPLredYRiGYVSXJk2a0B6Fa1RHVDxO1bkFPvHzBuRBGI96YyRS1tD2CbUdkdHXHhl1CYRlMCe65zMoKyo8SWgWeQEyntqgHIsAVa4pQb01HiS+qPBiKo+wd4avdPOA+7+q6+JFxZfIMPyf29dKKhcV6YQ9Cvr06cOCBQvo3bt3uXWNqqlKVIDeCyPd32OQwdwJvZcHIYN5IcoLao8SpF9AIUxHo/dpMQpxSiYMOSpGnoDD3GvPIYF6JXoPBMgL0pewEd1u93eEMD+iIXrvLHDjbOPGPhx5T152/yegsKSOyMuQiwTIFFSKeQH6TCQgIbDOve4rRX3nXvvIHW+1O/404CX02XvaHS8FhUGluH09iELC5syZQ79+/eLciYOLOvFYXH/99dx22220a9eOGTNm8Morr/DJJ5+QlJTElVdeySeffMJDDz1UZcfOWJo2bcpHH33EvffeS8eOHZk6dSpffPEFxx57LC+//DL/+Mc/9tIZGYZhGPWFrKws1iHjdmwV6/oZ+M+QgVHZjH7E7c8nei6gvKhIRwnZn6NZ2fNRbPdENIN7DqHn4s0KjpOLvBVJaPZ5pzv2Ce75KeAhVL62dGxxPDQtkSF3BjXzXESLipuBe1FPhOqIiluBfxJ2W+7Tp0+5CIb9gdmzZ9fYholEIsyePbvqFWuJzMzM0kpPFxJ2kW7lXi9B4UnvIwP/OrfsVhR+9CZ6r0xFosKXOG6PREcRek/+AOVUfIf6RcxDuRXdCcXD0cAs5AVoj0rDNkbvhxWEgsILDF/W9l1UjOBc9N4P3LqXIVFwP8ohykTv+TVIXDRyx3oWFSr4M/KuvILExndIMH3rziNAXpunUT7Syehz2hJ9rv/PjWMTEhEp1E9RARAJ4n2CjTLk5+fTsmVL8vLyqixVaxiGYRz8ZGdnk5aWVmmp1diwnqrCp3xDrznuf58cHb3tTUhMfIQMvg5unZGE3gNQecyphJV0PAVRjw0o6dRX8dmJZmKfRUbd5cATbvyVjTteN+HjkZG2HeWDeGJFxSg0+7wazXKPQQZdVc30QNf3IXe81157jfPOOy/O6PY9s2fPJjMzs8z9i2bFihUMGDCgtKAM6Bzbo5nyrKyscrH4bdq0YebMmXTt2rVWxxp9bYchA7oNYc6DD19ajwz+HihB+gXkVWuPvF3nImHp33O9kQegDcq32ISqRzVBIVBtUPhSE/TeW+f29ynyxI1En4Mc9F5MRcnku924E9D77WV3rEuQUAnc41nU4dtXgboGCe7Vbr0v3OvZyEPzO5R0fgX6PJyHSjonIrHkvXGLUAjYc+7hP6/t0Gfq/5AomsvBJSpqYgfvl8nbhmEYhrE/06tXLxYtWsQcZFzEei7i5QoEQcA84nsuokXFokWLWLp0KYXJyaWioj0y/M5Hxs6lKJzkA2QwRYuKPGTcdEaeCx84vBsZP4nIy5HiliWjWdkk1PF7PcrdGIFmqY+n8lySaM/FGjQDfRnwH5Qz4ovGxxMVIMNyI/KYXOTG5mf7KxIVX6Fwl9uQ5+L888/fLzwXXlRkoHvUF8p4LlasWEFKSgpJW7aUO8dL0LlkZmaW8VxEIhEab9lCSkpKabh4bRH9nnwX3Ytc4BPC6k3HION9HQpbmojeH6cjQXE1eh8+iwTBaShPqCt6vy1AXoFuSCAcTlgBars7Zid0vU5A75unkLegHwphWobe7w3c+lNRCOBF6L220q3jx/+8O8ZcVAXqCSTGO6HP2r+RgE1DYuJRd9wn3blOQaKmMxIv97nz6EJ5UTF37lw2RCJsQ2LlFQ4uUVFT9stys4ZhGIaxv+PFRVpaGv9yy66m8gRkLxQedP/fSHlR4SsiFhQUAJrBzszM5D1kqJ4N/BTNCF9HmFsBEhFbUJhGNgpxSUYhJAkofMR7LDqjGd15hD03BiPD6BwUKnI+Mharak/XEhlp7VEloXwkII5Ds9AlSDzEigrfo+MdZFTfjgxDqFhU3IGMxFNQ6dMNbj/nn39+nXou3nrrLc4444xSL9aV6BqPQeeSk5NDSkoKGSj86G3CClv+HB9GfSIyMzNL73t0XklKSgo5OTm16rmIfk/6aks7kKjo6dZJI/QELEYJy2lIPGxB76tByDtR7Lbpit4Ha5CgaIHei+3Rey0bvYdTUe5GR/S+2I3yFi5w6xYiT0mSG8PbSFScg8Svx4fudUfetkfReyQViaSH0Ht9CkrQ9jke65DIWIsE0evIY/GIW/4WEkaFKEzs5+heeFGRnp5OSUkJoPv5rTtefRQVYKFQ1cJCoQzDMIyKiA6L8gZjRbkCnthE5FhREYufCe+HZl3zUKL3lVHreFHxIUowPQd5OZLQLKKvse/LZyYgA/JbZJQlI2PwORSW0hEZ8NWZgWyPvBbRouIqNKP9CpoJXo6Ezr1uGy8qPkMz1R8jo3QnCrcpRknCP446zh2oQ/hJSLBsR96WRGQgroe4XcKjiUQizJw5k/79+1fjzMSCBQvK9PGIxYuKPoTVvd5E92QemuWfD2VC59LQ/bgKeXLeRWFCi1BY2Dw0e34H5QVoPHFR2XnNnTuXjIwMEhMT2blzZ9xziEQi9EQ9KkYjgdHAPQJkcP8XiYoO7rEAGd7nIlGxEonQzuh+ryMsL9vc7acZEprrkEhpjbwYDdA9n4OE5kzksbgMiQXc9fwpEgu/iBl/jjtmF7f+E0jgb0DviyNRSNY6oGHDhsyaNau0y/h6t49oIXsUEhvr3Gvz5s0jPT29dP2KelI0aNCArKysg0pUWCiUYRiGYewjosOiXqRqUQFhCMqLVC0qQPXvs7KymItmT69EBleJez1WVJyPZnxLKFuTP0ACogkyykuQgZiGElZzkVF5CZppPqUa51+ZqACFq1zvjvkm8AxhWNSHyIj7I4p134XEVpEb10BCD8YdKETneuCvbn3fJLDY7bMP6hK+YEFsdwzhw8oGDBjArFmzqnF2EhV9+/blkOLiuAnZ0Z6KW1AY2kvonrQhLHvqez1cjWb3k4AfIYP3v+jadUPi40x0T3zyMG47n7QeGxYVfV6RSISlS5eWvuZFRTsgUlRU7hwaNGjA2LEK5itCZV7nu7+L0D3xOTuXIw9WaySAJiBRcAbKM+hMWN51HRKqPVEC9/+QKJiKBFU7JF62ovd0ERIHecgT4YsB7EBiuASJkpGEIVnRpKD3xAok0EagnKS2SCTvdGMKgoDi4mLS09OZO3cumxo0IAiCMmFhd7rxrANeeeUVgiCgb9++BEHApgYNKm10t3v37oNKVNQU81hUA/NYGIZhGFWRnZ1NZmYmO3bsqPY2TZo0ISsrq1oNYSORCF2QoX4LCstIRkbXVsqKihGEM7gdkRHnPRe7kJG1kzDnoiPyBKxAhno2MnYnI6PQ9wiIpSpREc0rwB/QjGZHNFudggzmWSih9lrU1diXAU0B7kIx82+h2ehrUOz8J0gEDUOC5Uk0670DGbaxnot4XqKqPBdeVPRDIusDygrHeOFPU5Fo60pYcWs9KkH6NjJ6m7trdgPy1GxEuSWHoypG2SgM5350zxLQ7DmU91ykpKSQ7q7Ds+g+rweWLFnCjh07yMjIoB/ypr2H7nEBlMnfWYfeI5cSen/+jqqGbQH+hITRj914troxvoWM/ItRGF0EiYQ1KHypIxIEbyNxkoYEyVluvI3c/9+i99kGJASy0HtgCHov70Tv9WTCJnQT3X5Guuuyyo3lMCR8mrv1H6d86eTKiO4qP2vWLDIzM6vc5mCnJnawCYtqYMLCMAzDqEsikQh9gGPRTOoZ7u+mSFh8iIx/LyqWIQOyOTL4OyHD0QuLQpQX8R0yMrciIXIIas431722DcX9t6S8uLgBeAMZle2pXFTg9v8sMopboBnpIcA4ZCzeiWLst7qHFxeFyLDshYzR8e4cbkNem0I0y/0Wyk8odOe8tVGjMt3Co8Ncoo3zisRFtKi4GBnF85FAmg+sWbOGjh070sWN+y8odGwrCmHqggTDKiQMNqC+B58gAXIDMqDXu3VbEvZoKADuRoby8agCWD6qakTM+Pu6a/ExElQDkUib69bth3JXRqME5QcIxUW6uxcrkHDp6JbdgnJ5it3jHWTMj0DelC1uvKtRhaiFyMs1FHmOiqLOZwrybJyF3p8vIfF1ASoDu4KwY3Z7QlEx2K3T1J37TsI8jcCNx5eaPZLyoqK5O/+AMCyqJuLCREVITexgS942DMMwjP2Ypk2b0h5VQboP1dL3PSqOQUbX/1AIzghkPM1HQiIDJch6g7QtmrV+AyWonoMMymzkeYgWFaOQByAbVcZ5B832gzwhWWhm+0233ZlULiqWEMbU90FJ3KuRmBmMhBKEfRTOQkb2o2gW/iZkqLcGTkQGd8T9n+CuT4CSbpcCS1w4VLwkcF8e+F8ofChWXMSKin4oNMsbTa8AHTt2JAMJnnkod6QPMvRT3dgPc+uvdtfoCJQfciahqOiJcgK2ukcTdI+XEybnpyLR1Mnt62pkKHsvwsfoPh2FDPTjkKdggzvv0e4aXu/G48XFfEIBgDv2QMJk7UbutdPd65OQZ2sDErfnInHny8/uRveyGaGomOjuzelIkJzkXnsGhYSdikROa5RX8QYSkBe4MRcjQeFFMEhc9HHX5Qn0XrjILU8iFBW4Y92IPCO5QFJSUoV5Jh6bc//+mLAwDMMwjP0YHyr1HprBv8YtfxMZcGeg2eX3UJnO5sio3YiMqa7ox3418jp8gQzHN5CB6A3zjpQXFWPRbHknZADi9p2AZqi3IIO2EIUJ9UIGXjQ5yINShGb0e6IZ8ZVum1tRCFCOWz8FGbsgAznPjftjt80NqKGa75TcndD7cpob94NAz56qaVRRD46KxEU8UdEPGbEL3TY+xCkD3ZNjkPi6xa3nq2xFkLgI3DVojwz/j914vagoQkZ8K+A3qIfEdWiGvxglVEMoLnq4bW5CHpBsJCx3IvEyCxnbM1FoVbSxd517/gW65z1QbsfnSBSchTwfoPdJovv7dDeuDwnDz0Di4ofIGzLeHWs4oajwgnMrClNriQTANuRlKkHvmbnIA5GKxM1uwmZ4iVHjKEDv5VXuXLchYZ2B3qO+OEG0uBiLPh/rgQVZWRh7DxMWhmEYhrEfk5qaSnZ2Nr169Sota+vFxX+RQToSGen/RsboD5ERuBAZuGnIqB+LjMe7UOjR/cjou5z4ouLfaBb7E3cckOHQwh0zAYVKLXavPeaevbjIQQZ1OzSz3AZ1LW6JPCDHIEM2ErU+SFxsQUbjlUgcvIrETHQx2WXuuQ3Ka2jq1l+AchkaU3kPjlhxMW7cOEaNGhVXVIBCnKIrV32O8hJw5/INMtbPcsu8MdwWXdOVSDwUUFZUFKPr+kcULnWN228LlH+wk7AC2GMoBOqP7vjRouISJAAmA68hj8B0lP/xL8KKPVlu/L1QXkt3JEC/dGPsSiiivLh4A1VV8uuvRuJiF7o//d05TkDvhzmEORA7Ce/PJ0hwXIM8M++5c5+PPFHtUbjUAhRaN4Sw8lcyeo9kI+EQQWFQG1A52eMI845w60SHjS1YsIDevXtj7D1MWBiGYRjGfk5l4sJ3LP4fmsX/GM30+jj/BUhUfIpmeFei+P2PkEfjEzSj3oeqRYWntXv2fQTiiYuBlBUV36Dysj1RCNUgZERPQGKoi9suB81u+ypJXd1rQwg9GQGauQcZmZvdOu1REvd7yFi/m4pFhedqZHRuAkaNGkU75BGJFRWeDDe2Rki0fYbCgfqia/s6Mnh9x/NEdE07I0Gxxe2/JbpPxW7936F7dzUShi3c+j5xuQWhEHrGPaCsqBju9nk2EhGvIsM7WlzcjkRdHyQSjnJjucqd01jkCelAKC4WIe/C2Ugs+NC6E935znJ/X+2ONRUJHJ+nUeLOYYo79oUoxKs5ErfvIzF6vhvzRPQe2OWOMwTd8+XoPd7AnWdXFGI3G4U5ve7OCSQuxhM2qzNRsW8wYWEYhmEYBwAViYsvUIz7SrcsC4WJ/AYZ739EM8DHIQHwI1RlaR2a0Z/j1v8F1RMVnsrExb+RYXkxMg4/Qkavb4z3CZotBxl/UF5cpCDDEWR4e1Hh/w9QmNEGFPaVjkTFw2iW/X3CHIfKeBxVLVoPrFy5ki5duvAV8kDEigrP8UhQgHJFuiJD2HeGnuhe8+KiAcpp+NadY4uo5Y2Q+HsDhXL9EF1b38jQiwpfKvgW9/wcEmleVPhjBciQ916TV1HFpY9RrsU0dO1vRSLJ5yQ0RwIJlNdyk3vtBZSHcwJhwnZXZMgf5sY8Cd2v05C4O96tuxMJnEboffCOG/sAd5wz3HhfQp6cT1FX9TOQkJmHGgyCvCSL3DgPR2GAH7jrcD7y3jzmrv81KA/mAeSJM1Gx7zBhYRiGYRgHCLHiYg4SFitRwqnvWbCVMPzDN+zzlZc+QUbwP1Hi71hkXD+MZv/fp6yo2IwSj+NRkbgocNu1RR6DIcij8gwKvermxpPgnmPFRXvCMCKPFxOeYjSDvRkZtS+64/dExnR1iO2Sfthhh9Heje9T5G0ZUcG2Xlysd+c5wO0HN66JyBhvge7PCndundx5+PCvJBTKVYIEWH+UuBwrKjwNkHdhO0qkjhYV/vVocbEOzdpvJRQVt6MKTvko3GwbMtS7EoqL+914d7l1u7jxLEdCrhAl3vdGouAN9D45B4kK34G7EaGxOcyt9zwKv+voxh5BAmIbEk7D3D5eQO/Jf7nr3Q+951oj4fEc8rj8zJ33uUgULURCqrE73z59+lhC9j7CGuQZhmEYxgGEFxe+Id8Cwio2/fr1IwgC5s+fX65h3zxkqK1A4SK+o/XVKLa+JUpAjhYVTyPD9ZlKxtMaGZYjkFfiEGQUZyPDbyPwD1T16UFkVLZARrdvzjcQiYvX3D5jRYXHG+O+mVoOMso/YM9FBcDGjRtZh4znJm5fkyrZx/Fo9nwTEg990XkvQh4bLypyUChUp6hz8GZuAbrWv0XJzf9GCeHxRIXfdj0SlJmEs/6eEsIu69lI9FyKxFIK8ioMc6+3QILCCwyQuDgD9e0oIhQVHZGHw/e5+AHyWPgCAv1QzsVmwvCnRCQqCtwxfHL4LPQ+WOOOmY68a02RkJmKEs+vRiFWOUgoFCAP1Z/ReylaVIBCoToQ9l/53B2vHcRtbmjUPuaxMAzDMIwDDC8uMjMzCbZvL/d6nz59mD9/vl53vRwaNmzIrl27SEPhJuMIQ5NAM/TvoMo6jZDh/RBhHwDQTHk8WiMj/AMU7gMyFkcThiP1RDH2/0HhLFchQfEmMshbUNZzAaEhHU1A2PyvK/KEPIpyF2ZVML5Y4omK0v27xnEg4eCFSkWei1R0vXLQ9ZuFwpmOQTPnG5Go6OjOx4dLRZChXIhExGXuHP7gxtcUhfREe2q8p6MDEnHvIiP6HMLO6n6dJe74vhnfjcjQfgMZ7t5T4MOy8t2zDy0bge7DRjf2ruiaFyGR4sOnmrp9ZqGciuaEoiLB7XcLEp+JhKVrp7jnM90xzkHvgzFuDJejcKxthKWKJyHh+hbywFxAKCo8r6P31FAkXnw1qJkzZ2LsfcxjYRiGYRgHIKmpqWyPIyo8ffr0KW0QBypbm4sqP7VAM/zjYrY5jVBU+Ph03PpPULHnYiyaQZ7n/o8t8ZqDQrKucscY4/b1NTJUU1HuRxdkyOcRGsmxrEJx/r5M7kvIOE9CnpGqqExUeLyH5zOq57lIQWFGb6NwnIuRwFqAZsvjiYrdbtyJyBjfgc7/cuQRuB95jKCswPIj9l3HX0XiIlpULHbHbotCq3yy+TnI+J+IxIXft/dcLCLsgdKK8qJinTvXQ936ie6cJyMRdhJhUnuJO7+lbixb3DbJSFychbwSr7vjtUClbzu47c9D97c5CjM7mjB/JduN7WF0P2M5A4kKHw5YVYd1o/YwYWEYhmEY9YC+ffsyb9485qPwnIrERazhPQ8Z8hWJi+hynhC/b0RnlGcxHc3kn4xEwCQUo5+GxMZKVDHIz6LHiosVSKR0d9skoXKmzVH4zRxCr0c8NqLwrOp0YI4WF/koTCuvgnUfQ96AS5Dh7kOjWiJDPFZU4P5PRsKqAAmYVGSgX0GYG1ETcREtKg5DCfuHumMVIDETKy78vlYjz8AOt/4yJEw6EYqK7shbkYyu91RU8ngIYQ+JnW4fO93+Vrtzy3b79OLiVOR1WeDGvAm9F6cjT0S7qPPNQWF80911nTJlSun9eZDy4iL6PWmiYt9ioVCGYRiGUU/w4iI9PR3QDPnD7rVRxJ/Njw4N6kTZsKjqiAqQoXqz+/s5ZABvcdu1Q6LibcI+FfGM6GJkYLZAxq0XHWe757Fodvwdt/4lcc7/MGSMb0Ex99URF/7cb6VsZSrPYyjc5lJkEG9BRnUqCsFZTSguIkikeOHkm7j5vgt93LYlbqz/RvkWI5BAiBcWNdr9/190jRoj4+44t3wzEhQtCMWND+ua6J67IW9TEsqjWIXE4GFubGvc3ylu/RJ0v3yX7FPccXeh8sG+R0lnN5YfuP0scdv/wK17PBKLL7gxTHTr5brr1oVQVLyARMjy5ctJSdFI/P150O33RkxU1DUmLAzDMAyjHlGRuJiBYtOryjvw4mIuMiCrEhUeLy4mo5CbfiiJ/BXKi4roBGdvTDdChm0OmkH3VYpA4UcNkECZ6dY/CYXVxOLH9wBVi4tIJEI6Ei8d0Ex8k6jXZ7ixH0coKrYhI749MrZXovCQFJRX8Lobry8HGy0uCpGASEQGenvU36FV9JioWFxMQsZ6W+RB6eXG3MI9ogWbFxePoFCj/u71EiQKmiCRAxJ/G925NEcCYBLKm+mKBE0Ht2wKyn94BoW39UW5HqluDPPdefVE77cZ6N6+joQhqOhABHmjJhNfVHiixcUiJPJMVNQdJiwMwzAMo54RKy6aImNuLZXnHXhxEb0+yFA8jaqb0f0/FPbSGxmdG6lYVEDZsqwBYZ+L5cgATkFGdwQZ/yWol4XPE6iI6ogLLyouRNWXUikrKnJQeNFwwkZ/AwmN+BI0Yx9Bs/IfImM/ldBTEC0udiGDvClKcF+CvEIXUT5BOVZcgHIzFqFqUbciEbA9akzxEuGPQyWI84CfIgHgSxP3RB4M3HmAxGQzJCiPQpW91rptPkYFALqjkKc0d01y3Bg2IE/FDnT/ZqHEey+ClgBLly6le/fuRCIRxrlz+JyKRYXHvze/JUzUNlFRN1iOhWEYhmHUQ2JzLioTFR4f177KrT9lyhRWrVrFOjRTPLaSbeehsJ7OSFRsoHJRsRPN/hcRhhB5cdHNjWGle60VMvrPQl6RnSiBujJuBG5DM+qxpUhjRUUfJIY8Oe7Y3YDrUfiQzwHwYU7+fDohQ/spFE70cxQ6NJGwMtJONGvfCrgH9RK5EoVX7UYCJpp4d+l5N6bz3Zh7Iw/IIuKLCpDR3tuNZw4SAO3QvfJhS0nI81KCQpTWofyIZUgYdkD9Nx5wY12DRN4At5+XkbHfHt1z78H4N0pw/xIJBy8qQO+zRe76VCUqSq9JEJio2A8wj4VhGIZh1FO8uMjMzCQoLq56A2TANWrUiHlZWfTt2xeAVatW0blz59KO4FfH2e4naGb+RDRL/R8UclORqNjplu1EYTITkKF/HmU7dIN6WWxHs/0/Qkbxa2j2/thKziWe56IqUbEDGfCt3TjWopCjXcgQbkrYJwKUh/AhClHahDqcLyfMKdiFkp8TgXuRsX8FCm9qgYz5Qrffhsh4j50Vfh6Vnr0QiasSN6YEZMR/jaoqRTPJje0mVI1pgTv2ccA3hBW+eqLrvMQd+zt37oNQ2dckd+wtbrzXuHP6F2E3+Ifc9RiFEs1fRt6PTUhsRIsKTxAEtGrViuWzZlUpKqK3MeoWExaGYRiGUY/p27cvxdUUFZ7Y9Tt16lSpuBiOZrJ/jEJoNiBRMR0Z4z4B2wuJQjRL3hgZouPQTHpsh26QwbvR/d8OeUXeQx6OmVQuLEDiYhEy4CORCO3d2OKJCtyYOiOPybOomeCRKGckGeWMgMTFZJRUPRR5BR5FIinDHTPVvR4gY/5dJCouR0b6Nnc9mhFWdvLJ3ztRT4doUXE2Mv4jSFT0cmOJFRdeVJyNKnQ1csvjiQsfXtQZCYalKHSqjTvnpwhzI9aia+7zHL7++msGDhwYNwdi0aJFHHnkkSzNyionKjxbt26Nu9zYfzFhYRiGYRjGHlORuLgbGZIXALej2fQvorb7j3s+GxnLOSgnoQeKw38ZGfkDUMnSaHHRDs16b0QhNs+imfLlyAh/BBnkleV+jCVsojZu3DhGjRrF18i4jhUVIKM9BYUrPY2M92nutR7u+RU0Iz8LNYC7GCW8T0Zem3HuumS5bZ5BeQpnIlHRComKfCQqWiCxlY88G2ORIOmDPDUXomTsAAmKEvfw4mIrEkK9UF6DFxWD3XiLkQcFyosLX52pBbqPRSj8KQFd/5Fuu/no/uS7a+lFBZTPgVi0aBG9evWioCA2yMs40DFhYRiGYRhGrRArLuagyj9rURz+WCQ2jqW8uNiFxMFyFPbzHPIGnA/cgoxdz3g0M98flVPtigztaFERrxRpLLGlSU877TTaEnb07oCarcUy2b2ejMTB8ZQVF8Wou/lZqAv3WFSWdjMST3932/iu0oe58/nUXa8RxBcVye7cH0Oz/9+iRG1f4cl7KgIkLEC5EOvdfj4m9BANdusmuvHGExcZKAl7g7tG+ShXwnfP3oTCm7oQlimOFRWeIAho0qQJi7Ky6NWrF8bBiQkLwzAMwzBqjWhxsYlwhjotLa2MJyNaXHyH4vBPQUbyl6jqVCGhoXIcobjYiYzrk1DVpG8oLyqgcnERr9/B9OnT6dKlCzko9Md7R6LFxSQUntUEzeYvQKLoZCQuCpFH40YkHsYioxsUujQPeTquRE0CL3LXYaQb/7+QAX8p8UXFA4QdvQehfJVFSABA2UpaS9y6c1GifBN3LtGioqF77CAUF3ko/+Nxd41bU1ZU+ByPtlHXtTScrH37cqLCs2PHjrjLjYMHqwplGIZhGEat4sVF0LYtQRDQq1cvFi1axBxkOI9FBsixaLZ+BzK4x6GwoAdQWM1aynZWPg4ZsztR3sB4ZKTHExWeeB2aK2qi1rlzZ1auXMk85C1p6o7xptsuWlR8ioz2NWvWMBd5A/JQ0vgCNJv/IKGoKHbn+Mknn5DtzvVOZJDPQ4nQh7rzeNwdK56omI88AuuRV2SeO94i5KXwwmK5Wz7DjQnCBoIfEYoKT2MUNrYEeWQ+QWFPD6Du47GiIprocLJvvvkmzhpGfcE8FoZhGIZh1DqdOnVi/fr1pf97cRHruZiDjOgN7rEVGaheIMR6HBah2fR1yPB+GRn08USFpyZN1Ly46NJF6eEnIqN+AcqZ8KIiupHgmjVr6NixI/ko72EJmr3/ABnrXlSsXLmSzp07l8s58NvnovCm3cA/3fmNoqyo8Mf8+uuvOfrooxlP2GU8QHkUS93xZqF8j40oKfxZ5L14BFVzGkrZnhgrUTL4K+48Frp9bUcekYpEhRdpq1evpmPHjnHvgVE/MI+FYRiGYRj7hFjPxZ1oVn8hMpiXLFlCQbNmZQRCtMfhTrfdfOCll16iABm9lYmK2P28SNWdmeN5LmYSX1QAdOjQgTVr1rAShUc1QfkUfSkvKqLH07BTJ9asWUOHDh0IgoCNCQk899xzbHXX5BHgD5QXFQADBw7k66+/Zi4SHvPcNr6a0ywkEBYikTPeXavV7vz/jQSW32M2Eh7jkZiY5rbdgfI0HiL0+HhMVBixRAIr+lsl+fn5tGzZkry8PFq0aFH1BoZhGIZhVEh2djZpaWm0o6x3ojIikUjp+r6q0KxZsxgwYECN+hdEIpFqN1FbtWoVXbp0IR11mf6E8gZ+vP379T8lvqioCn9eQJXX6JtvvuHoo4+mH/JcpCGvxCtRY507dy4ZGRkkJiayc+dOIpEInYDDgTtQ8vWzSHTluG0aNGhASUkJCxYsoHfv3qXndSvyHJmoqD/UxA42YVENTFgYhmEYRu2SnZ1NZmZmjRJ6mzRpQtY+rirkxcX3FUE1FRXRLF26lMzMTAoLCytdL1pcnIJCsKojgDqhKlbdURO/nGpskw6cRhhOZqLi4MeERS1jwsIwDMMw6i+rVq3iyCOPZOPGjdXe5rDDDmP69OnfW1TUFC8uaiqAWqE8iy012MYfw0RF/aAmdrDlWBiGYRiGYVRC586dayQqADZu3LjPRAWEORfVFRW49bYC25OSarRNpH17ExVGXKwqlGEYhmEYxkHAwIEDa5RvAtUXIdGsXbu2xtsY9QPzWBiGYRiGYRiGsceYsDAMwzAMwzAMY48xYWEYhmEYhmEYxh5jwsIwDMMwDMMwjD3GhIVhGIZhGIZhGHuMCQvDMAzDMAzDMPYYExaGYRiGYRiGYewx1seiGvgaz/n5+XU8EsMwDMMwDMPYd3j7tzo9T0xYVIOCggKAfdpB0zAMwzAMwzD2FwoKCmjZsmWl60SC79NysZ5RUlLC2rVrad68OZFIpK6Hs9fJz8+nc+fOrFq1ihYtWtT1cIw9xO7nwYXdz4MPu6cHF3Y/Dy7sfspTUVBQQIcOHUhIqDyLwjwW1SAhIYFOnTrV9TD2OS1atKi3H6KDEbufBxd2Pw8+7J4eXNj9PLio7/ezKk+Fx5K3DcMwDMMwDMPYY0xYGIZhGIZhGIaxx5iwMMqRlJTE73//e5KSkup6KEYtYPfz4MLu58GH3dODC7ufBxd2P2uGJW8bhmEYhmEYhrHHmMfCMAzDMAzDMIw9xoSFYRiGYRiGYRh7jAkLwzAMwzAMwzD2GBMW9ZipU6dy9913c84559ChQwcikQiRSITVq1fv0X6Lior429/+RmZmJs2aNaN169accsopvPrqq7U0cqMiCgoK+H//7/+RlpZGkyZNOPTQQznrrLP44IMPvtf+TjnllNL3RbxHu3btavkM6h+vvPIKp5xyCq1bt6ZZs2ZkZmbyf//3fxQXF3+v/U2fPp2LLrqItm3b0rhxY7p168att97Kxo0ba3nkRjxq634+88wzlX72IpEIb7311l46C2PRokU8+OCDXHXVVWRkZNCwYUMikQh//vOf92i/7733HmeeeSaHHnooTZo0oXfv3vz617+msLCwlkZuVERt39O77767ys/owoULa/ks9n+sQV49ZtSoUeTl5dXqPrdv386wYcP4/PPPadWqFcOHD6ewsJAPPviAjz/+mLvuuov77ruvVo9piI0bN3LiiSeSnZ1N+/btOeecc9iwYQNvvvkmb775Jvfffz+33nrr99r36aefHldEVLdhjhGfO+64g/vvv5+GDRsyePBgkpOT+eCDD/jFL37B5MmTeeedd2jSpEm19/fqq69y6aWXsmvXLgYOHEi3bt2YNm0aDz30EK+88gqffvopPXv23ItnVL+p7fsJ0KNHD0444YS4r3Xs2LE2hm3E4dFHH+X++++v1X3+61//4s477yQSiXDiiSfStm1bPvnkE+655x7++9//8umnn3LooYfW6jGNkL1xTwEyMzPp379/3Nfq5W9kYNRbrr766uCee+4J3nrrrWDjxo0BEADBqlWrvvc+b7/99gAIMjIygk2bNpUunzZtWpCcnBwAweTJk2tj+EYM5557bgAEQ4YMCbZt21a6fMqUKUGDBg2ChISEICsrq0b7PPnkkwMg+PDDD2t5tMaECRMCIEhOTg6mT59eunzTpk1BRkZGAAR33XVXtfe3Zs2aoGnTpgEQPP7446XLd+3aFVx++eUBEAwcODAoKSmp1fMwRG3fz7FjxwZAcOWVV+6F0RpV8eSTTwY//elPgxdeeCFYsGBBMHr06AAI/vSnP32v/c2YMSOIRCJBgwYNgqlTp5Yu37ZtWzBkyJAACC644ILaGr4Rh9q+p7///e8DIPj9739fuwM9wDFhYZSyp8IiNzc3SExMDIDg008/Lff6n/70pwAIBg0atKdDNWKYN29eAAQNGjQIcnJyyr1+7bXXBkBwySWX1Gi/Jiz2HgMHDgyA4M9//nO51z755JMACJKSkoKtW7dWa38/+9nPAiAYOnRoudcKCgqCli1bBkDw1ltv7fHYjfLU9v00YbF/ceWVV+6REXrRRRcFQHDdddeVey0nJydISEgIgGDBggV7OlSjmuzpPTVhER/LsTBqjalTp1JUVESXLl04/vjjy70+atQoAL788kvWrl27r4d3UDNhwgQAjj/+eLp27VrudX/tJ0+e/L1j943aY82aNXzzzTdAeG+iOeGEE+jcuTM7d+5k6tSp1dqnfw/E219ycjIjRowA4LXXXvu+wzYqYG/cT+PgoaioiClTpgDx3x9du3Yt/c30n2PDOFCxHAuj1pg5cyYARx11VNzXu3fvTps2bcjNzWXWrFl06NBhXw7voKaqa++Xb9u2jcWLF9O3b98a7X/ChAlMnDiRHTt20LZtW4477jhOO+00EhJsbuL74O9XmzZt6NatW9x1jjrqKFatWsXMmTO59NJLK91fQUEBS5YsKd2uov0999xzpcc2ao/avp/RLFmyhN/85jds3LiR5ORk+vXrx4gRIywW/wAiOzub7du3A5V/Pj/55BP7fB6AzJgxg1/+8pfk5ubSsmVLBgwYwDnnnEPz5s3remh1ggkLo9ZYvnw5AF26dKlwnU6dOpGbm1u6rlE7VHXtW7RoQYsWLcjPz2f58uU1FhYPPPBAuWW9evXi+eefZ+DAgTUfcD2nOp+Vzp07l1m3MnJyckr/rmifNdmfUTNq+35G89lnn/HZZ5+VWda4cWPuvvtufvGLX9RwpEZd4O95q1atKjQ27fN54DJ58mQmT55cZlnLli154IEHuOKKK+poVHWHTTcatUZBQQEAzZo1q3Cd5ORkAPLz8/fJmOoLe+van3jiiTz55JMsWrSIbdu2sXr1aiZMmEB6ejrZ2dkMHTqUBQsW7Nng6yG1fb/8/irbp3329h574/PXrl07fv3rX/PVV1+xadMm8vPz+eabb7jiiivYuXMnv/zlL7nnnnv2fPDGXsd+Gw9OevTowT333MPMmTPJzc0lNzeXTz/9lLPPPpu8vDyuvPJKXnjhhboe5j7HPBYHID//+c+ZNGlSjbcbM2ZMhWULjbpjf76ff/rTn8r837RpUzp27MgZZ5zBiSeeyDfffMOvfvUrJk6cuFfHYRj1jeHDhzN8+PAyy4466iieffZZMjMzueuuu/jjH//ItddeS9u2betolIZRfxk9enS5ZccffzyTJ0/mtttu48EHH+QnP/kJF110EYmJiXUwwrrBhMUByNq1a1m0aFGNt9vbDXi8i3fbtm1VjqFFixZ7dSwHErVxP/f1tU9KSuLXv/41I0eO5K233qK4uJhGjRrt8X7rC7V9v6LDK7Zt2xa3drp99vYe+/rzd/vtt3PvvfeyefNm3nnnnbgGjrH/YL+N9Y+7776bRx55hE2bNvHVV19x4okn1vWQ9hkWCnUA8vzzzxOoVHCNHrGzX7VNSkoKACtXrqxwHd/V269r1M79rOra5+fnl7rYa+va9+nTB4CdO3eyefPmWtlnfcHfg1WrVlW4jn+tOvcruhJYRe+BmuzPqBm1fT+rokGDBqSmpgLhd6qx/+Lv+datW8uELUZjn8+DizZt2nDYYYcB9e8zasLCqDWOOOIIAKZNmxb39WXLlpGbmwvAgAED9tm46gNVXXu/vFmzZvTq1atWjvntt9+W/l1fq198X/z7/9tvv60wWdPfM39vK6NFixalHbWreg9UZ39Gzajt+1kd/OfPPnv7P2lpaTRt2hSwz2d9Yffu3eTl5QH17zNqwsKoNc4880wSExNZuXJluSomAOPGjQNg0KBBVmq2lhk5ciSgCjLxZqz9tT/nnHNqLWRp/PjxgDwXPvHQqB6dOnUqrabl7000n376KatWrSIpKYkzzzyzWvs877zzKtxfYWFhadWS888///sO26iAvXE/K2PGjBlkZ2cDcPTRR+/x/oy9S2JiImeddRYQ//2xYsUKPv/8cyD8HBsHNpMmTWL79u1EIpEKSwwftOyrTnzG/g/V7Lw9ePDgIC0tLXjttdfKvXb77bcHQHD44YcHmzdvLl0+ffr0IDk5OQCCyZMn1/rYjSA499xzSzsvb9++vXT51KlTgwYNGgQJCQlBVlZWue1Gjx4dpKWlBQ8++GCZ5R988EHw4YcfBiUlJWWW79y5M7j33nuDSCQSAMGYMWP2zgkd5EyYMCEAguTk5GD69Omlyzdv3hxkZGQEQHDXXXeV2ea1114L0tLSgsGDB5fb35o1a4KmTZsGQPDEE0+ULt+1a1cwevToAAgGDhxY7n4atUNt3s9t27YFDz30UJCfn1/uOB9//HGQkpISAMEJJ5ywd07GKEd1ujQ/+OCDQVpaWjB69Ohyr02fPj2IRCJBgwYNgjfffLN0+bZt24IhQ4YEQHDBBRfslbEb8dmTe7pixYrgueeeC3bs2FFumwkTJgRt2rQJgODyyy+v9XHv75iwqMf88Y9/DI455pjShxcWAwYMKF120003lduua9euARCMHTu23Gvbtm0Ljj322AAIWrduHVxwwQXB8OHDg0aNGgVAcOedd+6DM6ufbNiwIUhNTQ2AoH379sHFF18cnHLKKaUC4P7774+73cknnxwAwe9///syy//1r38FQNC2bdvg9NNPD0aNGhUMGzYsaNu2bel75ac//ek+OLODl9tuuy0AgkaNGgXDhw8PLrjggqBVq1YBEBx//PFlBGIQBMHYsWMDIOjatWvc/b388stBgwYNAiA45phjgh/+8IdB9+7dS+/j4sWL98FZ1V9q635u2bIlAIKkpKRg0KBBwcUXXxycf/75Qb9+/Uo/exkZGcHatWv34dnVL6ZPn17m9/HQQw8NgKBTp05llkffg9///vcBEJx88slx9/nPf/4zAIJIJBKccsopwcUXXxy0b98+AIK0tLRg06ZN++js6ie1eU9nzpxZOpFw4oknBpdccklw7rnnlv4GA8Gpp54aFBQU7OOzrHtMWNRjvFqv7BHvC7IyYREE4Yx2v379giZNmgQtW7YMTjrppODll1/euydkBHl5ecEvf/nLIDU1NUhKSgratGkTDB8+PHjvvfcq3KYiYTFjxozgpptuCo4++uigffv2QVJSUtCkSZOgR48ewRVXXBF89tlne/ls6gcvvfRScNJJJwUtWrQImjRpEvTr1y/461//GuzcubPculUJiyAIgmnTpgXnn39+8IMf/CBITEwMunbtGtxyyy3B+vXr9+JZGJ7auJ87d+4Mfvvb3wZnnHFG0K1bt6B58+ZBw4YNgx/84AfB0KFDg8cffzzu/oza48MPP6zy9xEIli9fXrpNVcIiCILg3XffDYYPHx60adMmSEpKClJTU4Nf/epXcb1TRu1Sm/d08+bNwS9+8Ytg8ODBQZcuXYJmzZoFjRo1Ctq3bx+cffbZwbhx44Ldu3fv2xPcT4gEQRDUPIDKMAzDMAzDMAwjxJK3DcMwDMMwDMPYY0xYGIZhGIZhGIaxx5iwMAzDMAzDMAxjjzFhYRiGYRiGYRjGHmPCwjAMwzAMwzCMPcaEhWEYhmEYhmEYe4wJC8MwDMMwDMMw9hgTFoZhGIZhGIZh7DEmLAzDMAzDMAzD2GNMWBiGYRiGYRiGsceYsDAMwzAMwzAMY48xYWEYhmEYhmEYxh5jwsIwDMMwDMMwjD3m/wOpFIo/RhllQgAAAABJRU5ErkJggg==", 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", 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" ] @@ -335,7 +336,7 @@ "$$\n", "\\min_{T:\\mathbb{R}^d \\rightarrow \\mathbb{R}^d} \\Delta(T\\sharp \\mu, \\nu) + \\lambda_\\mathrm{MG} \\mathcal{M}_\\mu^c(T)\n", "$$\n", - "For all fittings, we use $\\Delta = S_{\\varepsilon, \\ell_2^2}$, the {func}`~ott.tools.sinkhorn_divergence.sinkhorn_divergence` with the {class}`squared-Euclidean cost `\n", + "For all fittings, we use $\\Delta = S_{\\varepsilon, \\ell_2^2}$, the {func}`~ott.tools.sinkhorn_divergence.sinkhorn_divergence` with the {class}`squared Euclidean cost `\n", "The function considers a ground cost function `cost_fn` (corresponding to $c$), as well as the `epsilon` regularization parameters to compute approximated Wasserstein distances, both for fitting and regularizer." ] }, @@ -354,7 +355,7 @@ "):\n", " dim_data = 2\n", " # define the neural map\n", - " model = models.MLP(\n", + " model = potentials.PotentialMLP(\n", " dim_hidden=[32, 64, 32], is_potential=False, act_fn=nn.gelu\n", " )\n", "\n", @@ -387,7 +388,7 @@ " print(\"Selected `epsilon_regularizer`:\", epsilon_regularizer)\n", "\n", " def regularizer(x, y):\n", - " gap, out = losses.monge_gap_from_samples(\n", + " gap, out = monge_gap.monge_gap_from_samples(\n", " x,\n", " y,\n", " cost_fn=cost_fn,\n", @@ -397,7 +398,7 @@ " return gap, out.n_iters\n", "\n", " # define solver\n", - " solver = map_estimator.MapEstimator(\n", + " solver = monge_gap.MongeGapEstimator(\n", " dim_data=dim_data,\n", " fitting_loss=fitting_loss,\n", " regularizer=regularizer,\n", diff --git a/docs/tutorials/icnn_inits.ipynb b/docs/tutorials/icnn_inits.ipynb index 41a6fb931..1f9d01b3c 100644 --- a/docs/tutorials/icnn_inits.ipynb +++ b/docs/tutorials/icnn_inits.ipynb @@ -8,7 +8,7 @@ "\n", "As input convex neural networks (ICNN) are notoriously difficult to train {cite}`richter-powell:21`, {cite}`bunne:22` propose to use closed-form solutions between Gaussian approximations to derive relevant parameter initializations for ICNNs: given two measures $\\mu$ and $\\nu$, one can initialize ICNN parameters so that its gradient can map approximately $\\mu$ into $\\nu$. These initializations rely on closed-form solutions available for Gaussian measures {cite}`gelbrich:90`.\n", "\n", - "In this notebook, we introduce the *identity* and *Gaussian approximation*-based initialization schemes, and illustrate how they can be used within the `OTT` library when using {class}`~ott.neural.models.ICNN`-based potentials with the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` solver." + "In this notebook, we introduce the *identity* and *Gaussian approximation*-based initialization schemes, and illustrate how they can be used within the `OTT` library when using {class}`~ott.neural.networks.icnn.ICNN`-based potentials with the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` solver." ] }, { @@ -20,7 +20,7 @@ "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", - " !pip install -q git+https://github.com/ott-jax/ott@main" + " %pip install -q git+https://github.com/ott-jax/ott@main" ] }, { @@ -32,14 +32,15 @@ "import jax\n", "import jax.numpy as jnp\n", "import numpy as np\n", + "\n", "import optax\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from ott import datasets\n", "from ott.geometry import pointcloud\n", - "from ott.neural import models\n", - "from ott.neural.solvers import neuraldual\n", + "from ott.neural.methods import neuraldual\n", + "from ott.neural.networks import icnn\n", "from ott.tools import plot" ] }, @@ -49,9 +50,9 @@ "source": [ "## Setup training and validation datasets\n", "\n", - "To test the ICNN initialization methods, we choose the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` of the `OTT` library as an example. Here, we aim at computing the map between two toy datasets representing both, source and target distribution using the\n", + "To test the ICNN initialization methods, we choose the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` of the `OTT` library as an example. Here, we aim at computing the map between two toy datasets representing both, source and target distribution using the\n", "datasets `simple` (data clustered in one center) and `circle` (two-dimensional Gaussians arranged on a circle) from {class}`~ott.datasets.create_gaussian_mixture_samplers`.\n", - "For more details on the execution of the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual`, we refer the reader to {doc}`neural_dual` notebook.\n", + "For more details on the execution of the {class}`~ott.neural.methods.neuraldual.W2NeuralDual`, we refer the reader to {doc}`neural_dual` notebook.\n", "\n", "## Experimental setup \n", "\n", @@ -113,8 +114,8 @@ "### Identity initialization method\n", "\n", "Next, we define the architectures parameterizing the dual potentials $f$ and $g$. These need to be parameterized by ICNNs. You can adapt the size of the ICNNs by passing a sequence containing hidden layer sizes. While ICNNs are by default containing partially positive weights, we can solve the problem using approximations to this positivity constraint (via weight clipping and a weight penalization).\n", - "For this, set `pos_weights` to `True` in {class}`~ott.neural.models.ICNN` and {class}`~ott.neural.solvers.neuraldual.W2NeuralDual`.\n", - "For more details on how to customize {class}`~ott.neural.models.ICNN`,\n", + "For this, set `pos_weights` to `True` in {class}`~ott.neural.networks.icnn.ICNN` and {class}`~ott.neural.methods.neuraldual.W2NeuralDual`.\n", + "For more details on how to customize {class}`~ott.neural.networks.icnn.ICNN`,\n", "we refer you to the documentation.\n", "\n", "We first explore the `identity` initialization method. This initialization method is the default choice of the current ICNN and data independent, thus no further arguments need to be passed to the ICNN architecture." @@ -127,8 +128,8 @@ "outputs": [], "source": [ "# initialize models using identity initialization (default)\n", - "neural_f = models.ICNN(dim_hidden=[64, 64, 64, 64], dim_data=2)\n", - "neural_g = models.ICNN(dim_hidden=[64, 64, 64, 64], dim_data=2)" + "neural_f = icnn.ICNN(dim_hidden=[64, 64, 64, 64], dim_data=2)\n", + "neural_g = icnn.ICNN(dim_hidden=[64, 64, 64, 64], dim_data=2)" ] }, { @@ -140,14 +141,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/michal/projects/nott/src/ott/neural/solvers/neuraldual.py:276: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n", + "/Users/michal/Projects/dott/src/ott/neural/methods/neuraldual.py:154: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n", " self.setup(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "243d6aa24b1d45cba5ba10522373dc3a", + "model_id": "62abc21c2f8b47c09c328cb9ef44efd1", "version_major": 2, "version_minor": 0 }, @@ -190,7 +191,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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", 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", 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" ] @@ -263,7 +264,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To use the Gaussian initialization, the samples of source and target (`samples_source` and `samples_target`) need to be passed to the {class}`~ott.neural.models.ICNN` definition via the `gaussian_map_samples` argument. Note that ICNN $f$ maps source to target (`gaussian_map_samples=(samples_source, samples_target)`), and $g$ maps target to source cells (`gaussian_map_samples=(samples_target, samples_source)`)." + "To use the Gaussian initialization, the samples of source and target (`samples_source` and `samples_target`) need to be passed to the {class}`~ott.neural.networks.icnn.ICNN` definition via the `gaussian_map_samples` argument. Note that ICNN $f$ maps source to target (`gaussian_map_samples=(samples_source, samples_target)`), and $g$ maps target to source cells (`gaussian_map_samples=(samples_target, samples_source)`)." ] }, { @@ -273,12 +274,12 @@ "outputs": [], "source": [ "# initialize models using Gaussian initialization\n", - "neural_f = models.ICNN(\n", + "neural_f = icnn.ICNN(\n", " dim_hidden=[64, 64, 64, 64],\n", " dim_data=2,\n", " gaussian_map_samples=(samples_source, samples_target),\n", ")\n", - "neural_g = models.ICNN(\n", + "neural_g = icnn.ICNN(\n", " dim_hidden=[64, 64, 64, 64],\n", " dim_data=2,\n", " gaussian_map_samples=(samples_target, samples_source),\n", @@ -294,14 +295,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/michal/projects/nott/src/ott/neural/solvers/neuraldual.py:276: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n", + "/Users/michal/Projects/dott/src/ott/neural/methods/neuraldual.py:154: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n", " self.setup(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4e2a1cdac674c588497d0803d003ec2", + "model_id": "fdf9e1aeda2b473c93d15d4815247286", "version_major": 2, "version_minor": 0 }, @@ -344,7 +345,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -413,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.6" }, "vscode": { "interpreter": { diff --git a/docs/tutorials/neural_dual.ipynb b/docs/tutorials/neural_dual.ipynb index 9095f0583..2021eebfb 100644 --- a/docs/tutorials/neural_dual.ipynb +++ b/docs/tutorials/neural_dual.ipynb @@ -7,12 +7,12 @@ "# Neural Dual Solver \n", "\n", "This tutorial shows how to use `OTT` to compute the Wasserstein-2 optimal transport map between continuous measures in Euclidean space that are accessible via sampling.\n", - "{class}`~ott.neural.solvers.neuraldual.W2NeuralDual` solves this\n", + "{class}`~ott.neural.methods.neuraldual.W2NeuralDual` solves this\n", "problem by optimizing parameterized Kantorovich dual potential functions\n", "and returning a {class}`~ott.problems.linear.potentials.DualPotentials`\n", "object that can be used to transport unseen source data samples to its target distribution (or vice-versa) or compute the corresponding distance between new source and target distribution.\n", "\n", - "The dual potentials can be specified as non-convex neural networks ({class}`~ott.neural.models.MLP`) or an input-convex neural network ({class}`~ott.neural.models.ICNN`) {cite}`amos:17`. {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` implements the method developed by {cite}`makkuva:20` along with the improvements and fine-tuning of the conjugate computation from {cite}`amos:23`. For more insights on the approach itself, we refer the user to the original sources." + "The dual potentials can be specified as non-convex neural networks {class}`~ott.neural.networks.potentials.PotentialMLP` or an input-convex neural network {class}`~ott.neural.networks.icnn.ICNN` {cite}`amos:17`. {class}`~ott.neural.methods.neuraldual.W2NeuralDual` implements the method developed by {cite}`makkuva:20` along with the improvements and fine-tuning of the conjugate computation from {cite}`amos:23`. For more insights on the approach itself, we refer the user to the original sources." ] }, { @@ -24,7 +24,7 @@ "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", - " !pip install -q git+https://github.com/ott-jax/ott@main" + " %pip install -q git+https://github.com/ott-jax/ott@main" ] }, { @@ -37,16 +37,18 @@ "source": [ "import jax\n", "import jax.numpy as jnp\n", + "import numpy as np\n", + "from torch.utils.data import DataLoader, IterableDataset\n", + "\n", "import optax\n", - "from flax import linen as nn\n", "\n", "import matplotlib.pyplot as plt\n", "from IPython.display import clear_output, display\n", "\n", "from ott import datasets\n", "from ott.geometry import pointcloud\n", - "from ott.neural import models\n", - "from ott.neural.solvers import neuraldual\n", + "from ott.neural.methods import neuraldual\n", + "from ott.neural.networks import potentials\n", "from ott.tools import sinkhorn_divergence" ] }, @@ -56,7 +58,7 @@ "source": [ "## Setup training and validation datasets\n", "\n", - "We apply the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` to compute the transport between toy datasets.\n", + "We apply the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` to compute the transport between toy datasets.\n", "Here, we aim at computing the map between two toy datasets representing both, source and target distribution using the\n", "datasets `simple` (data clustered in one center) and `circle` (two-dimensional Gaussians arranged on a circle) from {class}`~ott.datasets.create_gaussian_mixture_samplers`.\n", "\n", @@ -93,18 +95,7 @@ "outputs": [ { "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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" ] @@ -147,16 +138,16 @@ "eval_data_source = next(valid_dataloaders.source_iter)\n", "eval_data_target = next(valid_dataloaders.target_iter)\n", "\n", - "plot_samples(eval_data_source, eval_data_target)" + "_ = plot_samples(eval_data_source, eval_data_target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we define the architectures parameterizing the dual potentials $f$ and $g$. We first parameterize $f$ with an {class}`~ott.neural.models.ICNN` and $\\nabla g$ as a non-convex {class}`~ott.neural.models.MLP`. You can adapt the size of the ICNNs by passing a sequence containing hidden layer sizes. While ICNNs are by default containing partially positive weights, we can run the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` using approximations to this positivity constraint (via weight clipping and a weight penalization).\n", - "For this, set `pos_weights` to `True` in {class}`~ott.neural.models.ICNN` and {class}`~ott.neural.solvers.neuraldual.W2NeuralDual`.\n", - "For more details on how to customize {class}`~ott.neural.models.ICNN`,\n", + "Next, we define the architectures parameterizing the dual potentials $f$ and $g$. We first parameterize $f$ with an {class}`~ott.neural.networks.icnn.ICNN` and $\\nabla g$ as a non-convex {class}`~ott.neural.networks.potentials.PotentialMLP`. You can adapt the size of the ICNNs by passing a sequence containing hidden layer sizes. While ICNNs are by default containing partially positive weights, we can run the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` using approximations to this positivity constraint (via weight clipping and a weight penalization).\n", + "For this, set `pos_weights` to `True` in {class}`~ott.neural.networks.icnn.ICNN` and {class}`~ott.neural.methods.neuraldual.W2NeuralDual`.\n", + "For more details on how to customize {class}`~ott.neural.networks.icnn.ICNN`,\n", "we refer you to the documentation." ] }, @@ -169,7 +160,7 @@ "# initialize models and optimizers\n", "num_train_iters = 5001\n", "\n", - "neural_f = models.ICNN(\n", + "neural_f = icnn.ICNN(\n", " dim_data=2,\n", " dim_hidden=[64, 64, 64, 64],\n", " pos_weights=True,\n", @@ -179,7 +170,7 @@ " ), # initialize the ICNN with source and target samples\n", ")\n", "\n", - "neural_g = models.MLP(\n", + "neural_g = potentials.PotentialMLP(\n", " dim_hidden=[64, 64, 64, 64],\n", " is_potential=False, # returns the gradient of the potential.\n", ")\n", @@ -196,7 +187,7 @@ "source": [ "## Train Neural Dual\n", "\n", - "We then initialize the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` by passing two {class}`~ott.neural.models.ICNN` models parameterizing $f$ and $g$, as well as by specifying the input dimensions of the data and the number of training iterations to execute. Once the {class}`~ott.neural.solvers.neuraldual.W2NeuralDual` is initialized, we can obtain the neural {class}`~ott.problems.linear.potentials.DualPotentials` by passing the corresponding dataloaders to it.\n", + "We then initialize the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` by passing two {class}`~ott.neural.networks.icnn.ICNN` models parameterizing $f$ and $g$, as well as by specifying the input dimensions of the data and the number of training iterations to execute. Once the {class}`~ott.neural.methods.neuraldual.W2NeuralDual` is initialized, we can obtain the neural {class}`~ott.problems.linear.potentials.DualPotentials` by passing the corresponding dataloaders to it.\n", "\n", "Execution of the following cell will probably take a few minutes, depending on your system and the number of training iterations." ] @@ -257,7 +248,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The output of the solver, `learned_potentials`, is an instance of {class}`~ott.problems.linear.potentials.DualPotentials`. This gives us access to the learned potentials and provides functions to compute and plot the forward and inverse OT maps between the measures." + "The output of the solver, `learned_potentials`, is an instance of {class}`~ott.problems.linear.potentials.DualPotentials`. This gives us access to the learned potentials and provides functions to compute and plot the forward and inverse OT maps between the measures." ] }, { @@ -518,7 +509,7 @@ "source": [ "## Solving a harder problem\n", "\n", - "We next set up a harder OT problem to transport from a mixture of five Gaussians to a mixture of four Gaussians and solve it by using the non-convex {class}`~ott.neural.models.MLP` potentials to model $f$ and $g$." + "We next set up a harder OT problem to transport from a mixture of five Gaussians to a mixture of four Gaussians and solve it by using the non-convex {class}`~ott.neural.networks.potentials.PotentialMLP` potentials to model $f$ and $g$." ] }, { @@ -576,8 +567,8 @@ "source": [ "num_train_iters = 20001\n", "\n", - "neural_f = models.MLP(dim_hidden=[64, 64, 64, 64])\n", - "neural_g = models.MLP(dim_hidden=[64, 64, 64, 64])\n", + "neural_f = potentials.PotentialMLP(dim_hidden=[64, 64, 64, 64])\n", + "neural_g = potentials.PotentialMLP(dim_hidden=[64, 64, 64, 64])\n", "\n", "lr_schedule = optax.cosine_decay_schedule(\n", " init_value=5e-4, decay_steps=num_train_iters, alpha=1e-2\n", @@ -719,8 +710,8 @@ "\n", " input_dim = 2\n", "\n", - " neural_f = models.MLP(dim_hidden=[64, 64, 64, 64])\n", - " neural_g = models.MLP(dim_hidden=[64, 64, 64, 64])\n", + " neural_f = potentials.PotentialMLP(dim_hidden=[64, 64, 64, 64])\n", + " neural_g = potentials.PotentialMLP(dim_hidden=[64, 64, 64, 64])\n", "\n", " lr_schedule = optax.cosine_decay_schedule(\n", " init_value=5e-4, decay_steps=num_train_iters, alpha=1e-2\n", @@ -802,7 +793,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.6" }, "vscode": { "interpreter": { diff --git a/docs/tutorials/point_clouds.ipynb b/docs/tutorials/point_clouds.ipynb index fd20ffc9a..156bafaa9 100644 --- a/docs/tutorials/point_clouds.ipynb +++ b/docs/tutorials/point_clouds.ipynb @@ -64,7 +64,7 @@ }, "outputs": [], "source": [ - "def create_points(rng: jax.random.PRNGKeyArray, n: int, m: int, d: int):\n", + "def create_points(rng: jax.Array, n: int, m: int, d: int):\n", " rngs = jax.random.split(rng, 3)\n", " x = jax.random.normal(rngs[0], (n, d)) + 1\n", " y = jax.random.uniform(rngs[1], (m, d))\n", @@ -279,6 +279,8 @@ "outputs": [], "source": [ "# Helper function to plot successively the optimal transports\n", + "\n", + "\n", "def plot_ots(ots):\n", " fig = plt.figure(figsize=(8, 5))\n", " plott = ott.tools.plot.Plot(fig=fig)\n", @@ -366973,6 +366975,8 @@ "outputs": [], "source": [ "# Plotting utility\n", + "\n", + "\n", "def plot_map(x, y, z, forward: bool = True):\n", " plt.figure(figsize=(10, 8))\n", " marker_t = \"o\" if forward else \"X\"\n", diff --git a/docs/tutorials/soft_sort.ipynb b/docs/tutorials/soft_sort.ipynb index cf0f751ac..880506731 100644 --- a/docs/tutorials/soft_sort.ipynb +++ b/docs/tutorials/soft_sort.ipynb @@ -37,16 +37,17 @@ "\n", "from tqdm.notebook import tqdm\n", "\n", - "import flax.linen as nn\n", "import jax\n", "import jax.numpy as jnp\n", "import numpy as np\n", - "import optax\n", "import torchvision\n", - "from flax import struct\n", "from scipy import ndimage\n", "from torch.utils import data\n", "\n", + "import flax.linen as nn\n", + "import optax\n", + "from flax import struct\n", + "\n", "import matplotlib.pyplot as plt\n", "\n", "from ott.tools import soft_sort" diff --git a/docs/tutorials/sparse_monge_displacements.ipynb b/docs/tutorials/sparse_monge_displacements.ipynb index a21213703..8b735d9f7 100644 --- a/docs/tutorials/sparse_monge_displacements.ipynb +++ b/docs/tutorials/sparse_monge_displacements.ipynb @@ -114,6 +114,8 @@ "outputs": [], "source": [ "# Plotting utility\n", + "\n", + "\n", "def plot_map(x, y, x_new=None, z=None, ax=None, title=None):\n", " if ax is None:\n", " f, ax = plt.subplots(figsize=(10, 8))\n", diff --git a/pyproject.toml b/pyproject.toml index e28ddd80c..3bb8351be 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,6 +59,7 @@ Changelog = "https://github.com/ott-jax/ott/releases" neural = [ "flax>=0.6.6", "optax>=0.1.1", + "diffrax>=0.4.1", ] dev = [ "pre-commit>=2.16.0", @@ -102,11 +103,14 @@ include = '\.ipynb$' [tool.isort] profile = "black" +line_length = 80 include_trailing_comma = true multi_line_output = 3 -sections = ["FUTURE", "STDLIB", "THIRDPARTY", "NUMERIC", "PLOTTING", "FIRSTPARTY", "LOCALFOLDER"] -# also contains what we import in notebooks -known_numeric = ["numpy", "scipy", "jax", "flax", "optax", "jaxopt", "torch", "ot", "torchvision", "pandas", "sklearn"] +sections = ["FUTURE", "STDLIB", "THIRDPARTY", "TEST", "NUMERIC", "NEURAL", "PLOTTING", "FIRSTPARTY", "LOCALFOLDER"] +# also contains what we import in notebooks/tests +known_neural = ["flax", "optax", "diffrax", "orbax"] +known_numeric = ["numpy", "scipy", "jax", "flax", "optax", "jaxopt", "ot", "torch", "torchvision", "pandas", "sklearn", "tslearn"] +known_test = ["_pytest", "pytest"] known_plotting = ["IPython", "matplotlib", "mpl_toolkits", "seaborn"] [tool.pytest.ini_options] @@ -182,85 +186,85 @@ ignore_path = ["docs/**/_autosummary", "docs/contributing.rst"] [tool.tox] legacy_tox_ini = """ - [tox] - min_version = 4.0 - env_list = lint-code,py{3.8,3.9,3.10,3.11,3.12},py3.9-jax-default - skip_missing_interpreters = true +[tox] +min_version = 4.0 +env_list = lint-code,py{3.8,3.9,3.10,3.11,3.12},py3.9-jax-default +skip_missing_interpreters = true - [testenv] - extras = - test - # https://github.com/google/flax/issues/3329 - py{3.9,3.10,3.11,3.12},py3.9-jax-default: neural - pass_env = CUDA_*,PYTEST_*,CI - commands_pre = - gpu: python -I -m pip install "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html - jax-latest: python -I -m pip install 'git+https://github.com/google/jax@main' - commands = - python -m pytest {tty:--color=yes} {posargs: \ - --cov={env_site_packages_dir}{/}ott --cov-config={tox_root}{/}pyproject.toml \ - --no-cov-on-fail --cov-report=xml --cov-report=term-missing:skip-covered} +[testenv] +extras = + test + # https://github.com/google/flax/issues/3329 + py{3.9,3.10,3.11,3.12},py3.9-jax-default: neural +pass_env = CUDA_*,PYTEST_*,CI +commands_pre = + gpu: python -I -m pip install "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html + jax-latest: python -I -m pip install 'git+https://github.com/google/jax@main' +commands = + python -m pytest {tty:--color=yes} {posargs: \ + --cov={env_site_packages_dir}{/}ott --cov-config={tox_root}{/}pyproject.toml \ + --no-cov-on-fail --cov-report=xml --cov-report=term-missing:skip-covered} - [testenv:lint-code] - description = Lint the code. - deps = pre-commit>=2.16.0 - skip_install = true - commands = - pre-commit run --all-files --show-diff-on-failure +[testenv:lint-code] +description = Lint the code. +deps = pre-commit>=2.16.0 +skip_install = true +commands = + pre-commit run --all-files --show-diff-on-failure - [testenv:lint-docs] - description = Lint the documentation. - deps = - extras = docs,neural - ignore_errors = true - allowlist_externals = make - pass_env = PYENCHANT_LIBRARY_PATH - set_env = SPHINXOPTS = -W -q --keep-going - changedir = {tox_root}{/}docs - commands = - make linkcheck {posargs} - make spelling {posargs} +[testenv:lint-docs] +description = Lint the documentation. +deps = +extras = docs,neural +ignore_errors = true +allowlist_externals = make +pass_env = PYENCHANT_LIBRARY_PATH +set_env = SPHINXOPTS = -W -q --keep-going +changedir = {tox_root}{/}docs +commands = + make linkcheck {posargs} + make spelling {posargs} - [testenv:build-docs] - description = Build the documentation. - use_develop = true - deps = - extras = docs,neural - allowlist_externals = make - changedir = {tox_root}{/}docs - commands = - make html {posargs} - commands_post = - python -c 'import pathlib; print("Documentation is under:", pathlib.Path("{tox_root}") / "docs" / "_build" / "html" / "index.html")' +[testenv:build-docs] +description = Build the documentation. +use_develop = true +deps = +extras = docs,neural +allowlist_externals = make +changedir = {tox_root}{/}docs +commands = + make html {posargs} +commands_post = + python -c 'import pathlib; print("Documentation is under:", pathlib.Path("{tox_root}") / "docs" / "_build" / "html" / "index.html")' - [testenv:clean-docs] - description = Remove the documentation. - deps = - skip_install = true - changedir = {tox_root}{/}docs - allowlist_externals = make - commands = - make clean +[testenv:clean-docs] +description = Remove the documentation. +deps = +skip_install = true +changedir = {tox_root}{/}docs +allowlist_externals = make +commands = + make clean - [testenv:build-package] - description = Build the package. - deps = - build - twine - commands = - python -m build --sdist --wheel --outdir {tox_root}{/}dist{/} {posargs:} - twine check {tox_root}{/}dist{/}* - commands_post = - python -c 'import pathlib; print(f"Package is under:", pathlib.Path("{tox_root}") / "dist")' +[testenv:build-package] +description = Build the package. +deps = + build + twine +commands = + python -m build --sdist --wheel --outdir {tox_root}{/}dist{/} {posargs:} + twine check {tox_root}{/}dist{/}* +commands_post = + python -c 'import pathlib; print(f"Package is under:", pathlib.Path("{tox_root}") / "dist")' - [testenv:format-references] - description = Format references.bib. - skip_install = true - allowlist_externals = biber - commands = biber --tool --output_file={tox_root}{/}docs{/}references.bib --nolog \ - --output_align --output_indent=2 --output_fieldcase=lower \ - --output_legacy_dates --output-field-replace=journaltitle:journal,thesis:phdthesis,institution:school \ - {tox_root}{/}docs{/}references.bib +[testenv:format-references] +description = Format references.bib. +skip_install = true +allowlist_externals = biber +commands = biber --tool --output_file={tox_root}{/}docs{/}references.bib --nolog \ + --output_align --output_indent=2 --output_fieldcase=lower \ + --output_legacy_dates --output-field-replace=journaltitle:journal,thesis:phdthesis,institution:school \ + {tox_root}{/}docs{/}references.bib """ [tool.ruff] @@ -271,6 +275,10 @@ exclude = [ "docs/_build", "dist" ] +line-length = 80 +target-version = "py38" + +[tool.ruff.lint] ignore = [ # Do not assign a lambda expression, use a def -> lambda expression assignments are convenient "E731", @@ -285,10 +293,8 @@ ignore = [ # Missing docstring in magic method "D105", ] -line-length = 80 select = [ "D", # flake8-docstrings - "I", # isort "E", # pycodestyle "F", # pyflakes "W", # pycodestyle @@ -305,20 +311,20 @@ select = [ "RET", # flake8-raise ] unfixable = ["B", "UP", "C4", "BLE", "T20", "RET"] -target-version = "py38" -[tool.ruff.per-file-ignores] + +[tool.ruff.lint.per-file-ignores] # TODO(michalk8): PO004 - remove `self.initialize` "tests/*" = ["D", "PT004", "E402"] "*/__init__.py" = ["F401"] "docs/*" = ["D"] "src/ott/types.py" = ["D102"] -[tool.ruff.pydocstyle] +[tool.ruff.lint.pydocstyle] convention = "google" -[tool.ruff.pyupgrade] +[tool.ruff.lint.pyupgrade] # Preserve types, even if a file imports `from __future__ import annotations`. keep-runtime-typing = true -[tool.ruff.flake8-tidy-imports] +[tool.ruff.lint.flake8-tidy-imports] # Disallow all relative imports. ban-relative-imports = "parents" -[tool.ruff.flake8-quotes] +[tool.ruff.lint.flake8-quotes] inline-quotes = "double" diff --git a/src/ott/__init__.py b/src/ott/__init__.py index dac0eb854..c40402511 100644 --- a/src/ott/__init__.py +++ b/src/ott/__init__.py @@ -25,7 +25,6 @@ ) with contextlib.suppress(ImportError): - # TODO(michalk8): add warning that neural module is not imported from . import neural from ._version import __version__ diff --git a/src/ott/datasets.py b/src/ott/datasets.py index 2a8d353e6..36ac6b561 100644 --- a/src/ott/datasets.py +++ b/src/ott/datasets.py @@ -51,17 +51,17 @@ class GaussianMixture: rectangle batch_size: batch size of the samples - init_rng: initial PRNG key + rng: initial PRNG key scale: scale of the Gaussian means std: the standard deviation of the individual Gaussian samples """ name: Name_t batch_size: int - init_rng: jax.Array + rng: jax.Array scale: float = 5.0 std: float = 0.5 - def __post_init__(self): + def __post_init__(self) -> None: gaussian_centers = { "simple": np.array([[0, 0]]), @@ -96,7 +96,7 @@ def __iter__(self) -> Iterator[jnp.array]: return self._create_sample_generators() def _create_sample_generators(self) -> Iterator[jnp.array]: - rng = self.init_rng + rng = self.rng while True: rng1, rng2, rng = jax.random.split(rng, 3) means = jax.random.choice(rng1, self.centers, (self.batch_size,)) @@ -128,26 +128,18 @@ def create_gaussian_mixture_samplers( rng1, rng2, rng3, rng4 = jax.random.split(rng, 4) train_dataset = Dataset( source_iter=iter( - GaussianMixture( - name_source, batch_size=train_batch_size, init_rng=rng1 - ) + GaussianMixture(name_source, batch_size=train_batch_size, rng=rng1) ), target_iter=iter( - GaussianMixture( - name_target, batch_size=train_batch_size, init_rng=rng2 - ) + GaussianMixture(name_target, batch_size=train_batch_size, rng=rng2) ) ) valid_dataset = Dataset( source_iter=iter( - GaussianMixture( - name_source, batch_size=valid_batch_size, init_rng=rng3 - ) + GaussianMixture(name_source, batch_size=valid_batch_size, rng=rng3) ), target_iter=iter( - GaussianMixture( - name_target, batch_size=valid_batch_size, init_rng=rng4 - ) + GaussianMixture(name_target, batch_size=valid_batch_size, rng=rng4) ) ) dim_data = 2 diff --git a/src/ott/initializers/__init__.py b/src/ott/initializers/__init__.py index 5406247dc..0fad8c3ff 100644 --- a/src/ott/initializers/__init__.py +++ b/src/ott/initializers/__init__.py @@ -11,4 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import contextlib + from . import linear, quadratic + +with contextlib.suppress(ImportError): + from . import neural + +del contextlib diff --git a/src/ott/neural/solvers/__init__.py b/src/ott/initializers/neural/__init__.py similarity index 91% rename from src/ott/neural/solvers/__init__.py rename to src/ott/initializers/neural/__init__.py index b09d8c60b..77e74d166 100644 --- a/src/ott/neural/solvers/__init__.py +++ b/src/ott/initializers/neural/__init__.py @@ -11,4 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import conjugate, map_estimator, neuraldual +from . import meta_initializer diff --git a/src/ott/neural/models.py b/src/ott/initializers/neural/meta_initializer.py similarity index 51% rename from src/ott/neural/models.py rename to src/ott/initializers/neural/meta_initializer.py index 0ee4a39f4..be1f87909 100644 --- a/src/ott/neural/models.py +++ b/src/ott/initializers/neural/meta_initializer.py @@ -12,10 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. import functools -from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Union +from typing import Any, Dict, Optional, Sequence, Tuple import jax import jax.numpy as jnp + import optax from flax import linen as nn from flax.core import frozen_dict @@ -23,195 +24,15 @@ from ott import utils from ott.geometry import geometry -from ott.initializers.linear import initializers as lin_init -from ott.neural import layers -from ott.neural.solvers import neuraldual +from ott.initializers.linear import initializers from ott.problems.linear import linear_problem +from ott.solvers.linear import sinkhorn -__all__ = ["ICNN", "MLP", "MetaInitializer"] - -# wrap to silence docs linter -DEFAULT_KERNEL_INIT = lambda *a, **k: nn.initializers.normal()(*a, **k) -DEFAULT_RECTIFIER = nn.activation.relu -DEFAULT_ACTIVATION = nn.activation.relu - - -class ICNN(neuraldual.BaseW2NeuralDual): - """Input convex neural network (ICNN). - - Implementation of input convex neural networks as introduced in - :cite:`amos:17` with initialization schemes proposed by :cite:`bunne:22`. - - Args: - dim_data: data dimensionality. - dim_hidden: sequence specifying size of hidden dimensions. The - output dimension of the last layer is 1 by default. - ranks: ranks of the matrices :math:`A_i` used as low-rank factors - for the quadratic potentials. If a sequence is passed, it must contain - ``len(dim_hidden) + 2`` elements, where the last 2 elements correspond - to the ranks of the final layer with dimension 1 and the potentials, - respectively. - init_fn: Initializer for the kernel weight matrices. - The default is :func:`~flax.linen.initializers.normal`. - act_fn: choice of activation function used in network architecture, - needs to be convex. The default is :func:`~flax.linen.activation.relu`. - pos_weights: Enforce positive weights with a projection. - If :obj:`False`, the positive weights should be enforced with clipping - or regularization in the loss. - rectifier_fn: function to ensure the non negativity of the weights. - The default is :func:`~flax.linen.activation.relu`. - gaussian_map_samples: Tuple of source and target points, used to initialize - the ICNN to mimic the linear Bures map that morphs the (Gaussian - approximation) of the input measure to that of the target measure. If - :obj:`None`, the identity initialization is used, and ICNN mimics half the - squared Euclidean norm. - """ - - dim_data: int - dim_hidden: Sequence[int] - ranks: Union[int, Tuple[int, ...]] = 1 - init_fn: Callable[[jax.Array, Tuple[int, ...], Any], - jnp.ndarray] = DEFAULT_KERNEL_INIT - act_fn: Callable[[jnp.ndarray], jnp.ndarray] = DEFAULT_ACTIVATION - pos_weights: bool = False - rectifier_fn: Callable[[jnp.ndarray], jnp.ndarray] = DEFAULT_RECTIFIER - gaussian_map_samples: Optional[Tuple[jnp.ndarray, jnp.ndarray]] = None - - def setup(self) -> None: # noqa: D102 - dim_hidden = list(self.dim_hidden) + [1] - *ranks, pos_def_rank = self._normalize_ranks() - - # final layer computes average, still with normalized rescaling - self.w_zs = [self._get_wz(dim) for dim in dim_hidden[1:]] - # subsequent layers re-injected into convex functions - self.w_xs = [ - self._get_wx(dim, rank) for dim, rank in zip(dim_hidden, ranks) - ] - self.pos_def_potentials = self._get_pos_def_potentials(pos_def_rank) - - @nn.compact - def __call__(self, x: jnp.ndarray) -> float: # noqa: D102 - w_x, *w_xs = self.w_xs - assert len(self.w_zs) == len(w_xs), (len(self.w_zs), len(w_xs)) - - z = self.act_fn(w_x(x)) - for w_z, w_x in zip(self.w_zs, w_xs): - z = self.act_fn(w_z(z) + w_x(x)) - z = z + self.pos_def_potentials(x) - - return z.squeeze() - - def _get_wz(self, dim: int) -> nn.Module: - if self.pos_weights: - return layers.PositiveDense( - dim, - kernel_init=self.init_fn, - use_bias=False, - rectifier_fn=self.rectifier_fn, - ) - - return nn.Dense( - dim, - kernel_init=self.init_fn, - use_bias=False, - ) - - def _get_wx(self, dim: int, rank: int) -> nn.Module: - return layers.PosDefPotentials( - rank=rank, - num_potentials=dim, - use_linear=True, - use_bias=True, - kernel_diag_init=nn.initializers.zeros, - kernel_lr_init=self.init_fn, - kernel_linear_init=self.init_fn, - bias_init=nn.initializers.zeros, - ) - - def _get_pos_def_potentials(self, rank: int) -> layers.PosDefPotentials: - kwargs = { - "num_potentials": 1, - "use_linear": True, - "use_bias": True, - "bias_init": nn.initializers.zeros - } - - if self.gaussian_map_samples is None: - return layers.PosDefPotentials( - rank=rank, - kernel_diag_init=nn.initializers.ones, - kernel_lr_init=nn.initializers.zeros, - kernel_linear_init=nn.initializers.zeros, - **kwargs, - ) - - source, target = self.gaussian_map_samples - return layers.PosDefPotentials.init_from_samples( - source, - target, - rank=self.dim_data, - kernel_diag_init=nn.initializers.zeros, - **kwargs, - ) - - def _normalize_ranks(self) -> Tuple[int, ...]: - # +2 for the newly added layer with 1 + the final potentials - n_ranks = len(self.dim_hidden) + 2 - if isinstance(self.ranks, int): - return (self.ranks,) * n_ranks - - assert len(self.ranks) == n_ranks, (len(self.ranks), n_ranks) - return tuple(self.ranks) - - @property - def is_potential(self) -> bool: # noqa: D102 - return True - - -class MLP(neuraldual.BaseW2NeuralDual): - """A generic, not-convex MLP. - - Args: - dim_hidden: sequence specifying size of hidden dimensions. The output - dimension of the last layer is automatically set to 1 if - :attr:`is_potential` is ``True``, or the dimension of the input otherwise - is_potential: Model the potential if ``True``, otherwise - model the gradient of the potential - act_fn: Activation function - """ - - dim_hidden: Sequence[int] - is_potential: bool = True - act_fn: Callable[[jnp.ndarray], jnp.ndarray] = nn.leaky_relu - - @nn.compact - def __call__(self, x: jnp.ndarray) -> jnp.ndarray: # noqa: D102 - squeeze = x.ndim == 1 - if squeeze: - x = jnp.expand_dims(x, 0) - assert x.ndim == 2, x.ndim - n_input = x.shape[-1] - - z = x - for n_hidden in self.dim_hidden: - Wx = nn.Dense(n_hidden, use_bias=True) - z = self.act_fn(Wx(z)) - - if self.is_potential: - Wx = nn.Dense(1, use_bias=True) - z = Wx(z).squeeze(-1) - - quad_term = 0.5 * jax.vmap(jnp.dot)(x, x) - z += quad_term - else: - Wx = nn.Dense(n_input, use_bias=True) - z = x + Wx(z) - - return z.squeeze(0) if squeeze else z +__all__ = ["MetaInitializer"] @jax.tree_util.register_pytree_node_class -class MetaInitializer(lin_init.DefaultInitializer): +class MetaInitializer(initializers.DefaultInitializer): """Meta OT Initializer with a fixed geometry :cite:`amos:22`. This initializer consists of a predictive model that outputs the @@ -314,7 +135,7 @@ def update( def init_dual_a( # noqa: D102 self, - ot_prob: "linear_problem.LinearProblem", + ot_prob: linear_problem.LinearProblem, lse_mode: bool, rng: Optional[jax.Array] = None, ) -> jnp.ndarray: @@ -337,8 +158,6 @@ def init_dual_a( # noqa: D102 def _get_update_fn(self): """Return the implementation (and jitted) update function.""" - from ott.problems.linear import linear_problem - from ott.solvers.linear import sinkhorn def dual_obj_loss_single(params, a, b): f_pred = self._compute_f(a, b, params) diff --git a/src/ott/math/__init__.py b/src/ott/math/__init__.py index 64bc1c07b..ce2a09a73 100644 --- a/src/ott/math/__init__.py +++ b/src/ott/math/__init__.py @@ -11,9 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import ( - fixed_point_loop, - matrix_square_root, - unbalanced_functions, - utils, -) +from . import fixed_point_loop, matrix_square_root, unbalanced_functions, utils diff --git a/src/ott/neural/__init__.py b/src/ott/neural/__init__.py index aa1ca23fa..3af88e56b 100644 --- a/src/ott/neural/__init__.py +++ b/src/ott/neural/__init__.py @@ -11,4 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import layers, losses, models, solvers +from . import datasets, methods, networks diff --git a/src/ott/neural/datasets.py b/src/ott/neural/datasets.py new file mode 100644 index 000000000..89453b2ce --- /dev/null +++ b/src/ott/neural/datasets.py @@ -0,0 +1,120 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import collections +import dataclasses +from typing import Any, Dict, Optional, Sequence + +import numpy as np + +__all__ = ["OTData", "OTDataset"] + +Item_t = Dict[str, np.ndarray] + + +@dataclasses.dataclass(repr=False, frozen=True) +class OTData: + """Distribution data for (conditional) optimal transport problems. + + Args: + lin: Linear term of the samples. + quad: Quadratic term of the samples. + condition: Condition corresponding to the data distribution. + """ + lin: Optional[np.ndarray] = None + quad: Optional[np.ndarray] = None + condition: Optional[np.ndarray] = None + + def __getitem__(self, ix: int) -> Item_t: + return {k: v[ix] for k, v in self.__dict__.items() if v is not None} + + def __len__(self) -> int: + if self.lin is not None: + return len(self.lin) + if self.quad is not None: + return len(self.quad) + return 0 + + +class OTDataset: + """Dataset for optimal transport problems. + + Args: + src_data: Samples from the source distribution. + tgt_data: Samples from the target distribution. + src_conditions: Conditions for the source data. + tgt_conditions: Conditions for the target data. + is_aligned: Whether the samples from the source and the target data + are paired. If yes, the source and the target conditions must match. + seed: Random seed used to match source and target when not aligned. + """ + SRC_PREFIX = "src" + TGT_PREFIX = "tgt" + + def __init__( + self, + src_data: OTData, + tgt_data: OTData, + src_conditions: Optional[Sequence[Any]] = None, + tgt_conditions: Optional[Sequence[Any]] = None, + is_aligned: bool = False, + seed: Optional[int] = None, + ): + self.src_data = src_data + self.tgt_data = tgt_data + + if src_conditions is None: + src_conditions = [None] * len(src_data) + self.src_conditions = list(src_conditions) + if tgt_conditions is None: + tgt_conditions = [None] * len(tgt_data) + self.tgt_conditions = list(tgt_conditions) + + self._tgt_cond_to_ix = collections.defaultdict(list) + for ix, cond in enumerate(tgt_conditions): + self._tgt_cond_to_ix[cond].append(ix) + + self.is_aligned = is_aligned + self._rng = np.random.default_rng(seed) + + self._verify_integrity() + + def _verify_integrity(self) -> None: + assert len(self.src_data) == len(self.src_conditions) + assert len(self.tgt_data) == len(self.tgt_conditions) + + if self.is_aligned: + assert len(self.src_data) == len(self.tgt_data) + assert self.src_conditions == self.tgt_conditions + else: + sym_diff = set(self.src_conditions + ).symmetric_difference(self.tgt_conditions) + assert not sym_diff, sym_diff + + def _sample_from_target(self, src_ix: int) -> Item_t: + src_cond = self.src_conditions[src_ix] + tgt_ixs = self._tgt_cond_to_ix[src_cond] + ix = self._rng.choice(tgt_ixs) + return self.tgt_data[ix] + + def __getitem__(self, ix: int) -> Item_t: + src = self.src_data[ix] + src = {f"{self.SRC_PREFIX}_{k}": v for k, v in src.items()} + + tgt = self.tgt_data[ix] if self.is_aligned else self._sample_from_target(ix) + tgt = {f"{self.TGT_PREFIX}_{k}": v for k, v in tgt.items()} + + return {**src, **tgt} + + def __len__(self) -> int: + return len(self.src_data) diff --git a/src/ott/neural/losses.py b/src/ott/neural/losses.py deleted file mode 100644 index f6136bf07..000000000 --- a/src/ott/neural/losses.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright OTT-JAX -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Any, Callable, Literal, Optional, Tuple, Union - -import jax -import jax.numpy as jnp - -from ott.geometry import costs, pointcloud -from ott.solvers import linear -from ott.solvers.linear import sinkhorn - -__all__ = ["monge_gap", "monge_gap_from_samples"] - - -def monge_gap( - map_fn: Callable[[jnp.ndarray], jnp.ndarray], - reference_points: jnp.ndarray, - cost_fn: Optional[costs.CostFn] = None, - epsilon: Optional[float] = None, - relative_epsilon: Optional[bool] = None, - scale_cost: Union[int, float, Literal["mean", "max_cost", "median"]] = 1.0, - return_output: bool = False, - **kwargs: Any -) -> Union[float, Tuple[float, sinkhorn.SinkhornOutput]]: - r"""Monge gap regularizer :cite:`uscidda:23`. - - For a cost function :math:`c` and empirical reference measure - :math:`\hat{\rho}_n=\frac{1}{n}\sum_{i=1}^n \delta_{x_i}`, the - (entropic) Monge gap of a map function - :math:`T:\mathbb{R}^d\rightarrow\mathbb{R}^d` is defined as: - - .. math:: - \mathcal{M}^c_{\hat{\rho}_n, \varepsilon} (T) - = \frac{1}{n} \sum_{i=1}^n c(x_i, T(x_i)) - - W_{c, \varepsilon}(\hat{\rho}_n, T \sharp \hat{\rho}_n) - - See :cite:`uscidda:23` Eq. (8). This function is a thin wrapper that calls - :func:`~ott.neural.losses.monge_gap_from_samples`. - - Args: - map_fn: Callable corresponding to map :math:`T` in definition above. The - callable should be vectorized (e.g. using :func:`jax.vmap`), i.e, - able to process a *batch* of vectors of size `d`, namely - ``map_fn`` applied to an array returns an array of the same shape. - reference_points: Array of `[n,d]` points, :math:`\hat\rho_n` in paper - cost_fn: An object of class :class:`~ott.geometry.costs.CostFn`. - epsilon: Regularization parameter. See - :class:`~ott.geometry.pointcloud.PointCloud` - relative_epsilon: when `False`, the parameter ``epsilon`` specifies the - value of the entropic regularization parameter. When `True`, ``epsilon`` - refers to a fraction of the - :attr:`~ott.geometry.pointcloud.PointCloud.mean_cost_matrix`, which is - computed adaptively using ``source`` and ``target`` points. - scale_cost: option to rescale the cost matrix. Implemented scalings are - 'median', 'mean' and 'max_cost'. Alternatively, a float factor can be - given to rescale the cost such that ``cost_matrix /= scale_cost``. - return_output: boolean to also return the - :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput`. - kwargs: holds the kwargs to instantiate the or - :class:`~ott.solvers.linear.sinkhorn.Sinkhorn` solver to - compute the regularized OT cost. - - Returns: - The Monge gap value and optionally the - :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput` - """ - target = map_fn(reference_points) - return monge_gap_from_samples( - source=reference_points, - target=target, - cost_fn=cost_fn, - epsilon=epsilon, - relative_epsilon=relative_epsilon, - scale_cost=scale_cost, - return_output=return_output, - **kwargs - ) - - -def monge_gap_from_samples( - source: jnp.ndarray, - target: jnp.ndarray, - cost_fn: Optional[costs.CostFn] = None, - epsilon: Optional[float] = None, - relative_epsilon: Optional[bool] = None, - scale_cost: Union[int, float, Literal["mean", "max_cost", "median"]] = 1.0, - return_output: bool = False, - **kwargs: Any -) -> Union[float, Tuple[float, sinkhorn.SinkhornOutput]]: - r"""Monge gap, instantiated in terms of samples before / after applying map. - - .. math:: - \frac{1}{n} \sum_{i=1}^n c(x_i, y_i)) - - W_{c, \varepsilon}(\frac{1}{n}\sum_i \delta_{x_i}, - \frac{1}{n}\sum_i \delta_{y_i}) - - where :math:`W_{c, \varepsilon}` is an entropy-regularized optimal transport - cost, the :attr:`~ott.solvers.linear.sinkhorn.SinkhornOutput.ent_reg_cost`. - - Args: - source: samples from first measure, array of shape ``[n, d]``. - target: samples from second measure, array of shape ``[n, d]``. - cost_fn: a cost function between two points in dimension :math:`d`. - If :obj:`None`, :class:`~ott.geometry.costs.SqEuclidean` is used. - epsilon: Regularization parameter. See - :class:`~ott.geometry.pointcloud.PointCloud` - relative_epsilon: when `False`, the parameter ``epsilon`` specifies the - value of the entropic regularization parameter. When `True`, ``epsilon`` - refers to a fraction of the - :attr:`~ott.geometry.pointcloud.PointCloud.mean_cost_matrix`, which is - computed adaptively using ``source`` and ``target`` points. - scale_cost: option to rescale the cost matrix. Implemented scalings are - 'median', 'mean' and 'max_cost'. Alternatively, a float factor can be - given to rescale the cost such that ``cost_matrix /= scale_cost``. - return_output: boolean to also return the - :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput`. - kwargs: holds the kwargs to instantiate the or - :class:`~ott.solvers.linear.sinkhorn.Sinkhorn` solver to - compute the regularized OT cost. - - Returns: - The Monge gap value and optionally the - :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput` - """ - cost_fn = costs.SqEuclidean() if cost_fn is None else cost_fn - geom = pointcloud.PointCloud( - x=source, - y=target, - cost_fn=cost_fn, - epsilon=epsilon, - relative_epsilon=relative_epsilon, - scale_cost=scale_cost, - ) - gt_displacement_cost = jnp.mean(jax.vmap(cost_fn)(source, target)) - out = linear.solve(geom=geom, **kwargs) - loss = gt_displacement_cost - out.ent_reg_cost - return (loss, out) if return_output else loss diff --git a/src/ott/neural/methods/__init__.py b/src/ott/neural/methods/__init__.py new file mode 100644 index 000000000..a5836f921 --- /dev/null +++ b/src/ott/neural/methods/__init__.py @@ -0,0 +1,14 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from . import monge_gap, neuraldual diff --git a/src/ott/neural/methods/flows/__init__.py b/src/ott/neural/methods/flows/__init__.py new file mode 100644 index 000000000..f5bba4cc5 --- /dev/null +++ b/src/ott/neural/methods/flows/__init__.py @@ -0,0 +1,14 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from . import dynamics, genot, otfm diff --git a/src/ott/neural/methods/flows/dynamics.py b/src/ott/neural/methods/flows/dynamics.py new file mode 100644 index 000000000..3ca60168c --- /dev/null +++ b/src/ott/neural/methods/flows/dynamics.py @@ -0,0 +1,164 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import abc + +import jax +import jax.numpy as jnp + +__all__ = [ + "BaseFlow", + "StraightFlow", + "ConstantNoiseFlow", + "BrownianBridge", +] + + +class BaseFlow(abc.ABC): + """Base class for all flows. + + Args: + sigma: Noise used for computing time-dependent noise schedule. + """ + + def __init__(self, sigma: float): + self.sigma = sigma + + @abc.abstractmethod + def compute_mu_t( + self, t: jnp.ndarray, src: jnp.ndarray, tgt: jnp.ndarray + ) -> jnp.ndarray: + """Compute the mean of the probability path. + + Compute the mean of the probability path between :math:`x_0` and :math:`x_1` + at time :math:`t`. + + Args: + t: Time :math:`t` of shape ``[batch, 1]``. + src: Sample from the source distribution of shape ``[batch, ...]``. + tgt: Sample from the target distribution of shape ``[batch, ...]``. + """ + + @abc.abstractmethod + def compute_sigma_t(self, t: jnp.ndarray) -> jnp.ndarray: + """Compute the standard deviation of the probability path at time :math:`t`. + + Args: + t: Time :math:`t` of shape ``[batch, 1]``. + + Returns: + Standard deviation of the probability path at time :math:`t`. + """ + + @abc.abstractmethod + def compute_ut( + self, t: jnp.ndarray, src: jnp.ndarray, tgt: jnp.ndarray + ) -> jnp.ndarray: + """Evaluate the conditional vector field. + + Evaluate the conditional vector field defined between :math:`x_0` and + :math:`x_1` at time :math:`t`. + + Args: + t: Time :math:`t` of shape ``[batch, 1]``. + src: Sample from the source distribution of shape ``[batch, ...]``. + tgt: Sample from the target distribution of shape ``[batch, ...]``. + + Returns: + Conditional vector field evaluated at time :math:`t`. + """ + + def compute_xt( + self, rng: jax.Array, t: jnp.ndarray, src: jnp.ndarray, tgt: jnp.ndarray + ) -> jnp.ndarray: + """Sample from the probability path. + + Sample from the probability path between :math:`x_0` and :math:`x_1` at + time :math:`t`. + + Args: + rng: Random number generator. + t: Time :math:`t` of shape ``[batch, 1]``. + src: Sample from the source distribution of shape ``[batch, ...]``. + tgt: Sample from the target distribution of shape ``[batch, ...]``. + + Returns: + Samples from the probability path between :math:`x_0` and :math:`x_1` + at time :math:`t`. + """ + noise = jax.random.normal(rng, shape=src.shape) + mu_t = self.compute_mu_t(t, src, tgt) + sigma_t = self.compute_sigma_t(t) + return mu_t + sigma_t * noise + + +class StraightFlow(BaseFlow, abc.ABC): + """Base class for flows with straight paths. + + Args: + sigma: Noise used for computing time-dependent noise schedule. + """ + + def compute_mu_t( # noqa: D102 + self, t: jnp.ndarray, src: jnp.ndarray, tgt: jnp.ndarray + ) -> jnp.ndarray: + return (1.0 - t) * src + t * tgt + + def compute_ut( # noqa: D102 + self, t: jnp.ndarray, src: jnp.ndarray, tgt: jnp.ndarray + ) -> jnp.ndarray: + del t + return tgt - src + + +class ConstantNoiseFlow(StraightFlow): + r"""Flow with straight paths and constant flow noise :math:`\sigma`. + + Args: + sigma: Constant noise used for computing time-independent noise schedule. + """ + + def compute_sigma_t(self, t: jnp.ndarray) -> jnp.ndarray: + r"""Compute noise of the flow at time :math:`t`. + + Args: + t: Time :math:`t` of shape ``[batch, 1]``. + + Returns: + Constant, time-independent standard deviation :math:`\sigma`. + """ + return jnp.full_like(t, fill_value=self.sigma) + + +class BrownianBridge(StraightFlow): + r"""Brownian Bridge. + + Sampler for sampling noise implicitly defined by a Schrödinger Bridge + problem with parameter :math:`\sigma` such that + :math:`\sigma_t = \sigma \cdot \sqrt{t \cdot (1 - t)}` :cite:`tong:23`. + + Args: + sigma: Noise used for computing time-dependent noise schedule. + """ + + def compute_sigma_t(self, t: jnp.ndarray) -> jnp.ndarray: + r"""Compute noise of the flow at time :math:`t`. + + Args: + t: Time :math:`t` of shape ``[batch, 1]``. + + Returns: + Samples from the probability path between :math:`x_0` and :math:`x_1` + at time :math:`t`. + """ + return self.sigma * jnp.sqrt(t * (1.0 - t)) diff --git a/src/ott/neural/methods/flows/genot.py b/src/ott/neural/methods/flows/genot.py new file mode 100644 index 000000000..ce200d376 --- /dev/null +++ b/src/ott/neural/methods/flows/genot.py @@ -0,0 +1,317 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import functools +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple + +import jax +import jax.numpy as jnp +import jax.tree_util as jtu +import numpy as np + +import diffrax +from flax.training import train_state + +from ott import utils +from ott.neural.methods.flows import dynamics +from ott.neural.networks import velocity_field +from ott.solvers import utils as solver_utils + +__all__ = ["GENOT"] + +# input: (src_lin, tgt_lin, src_quad, tgt_quad), output: (len(src), len(tgt)) +# all are optional because the problem can be linear/quadratic/fused +DataMatchFn_t = Callable[[ + Optional[jnp.ndarray], Optional[jnp.ndarray], Optional[jnp.ndarray], + Optional[jnp.ndarray] +], jnp.ndarray] + + +class GENOT: + """Generative Entropic Neural Optimal Transport :cite:`klein_uscidda:23`. + + GENOT is a framework for learning neural optimal transport plans between + two distributions. It allows for learning linear and quadratic + (Fused) Gromov-Wasserstein couplings, in both the balanced and + the unbalanced setting. + + Args: + vf: Vector field parameterized by a neural network. + flow: Flow between the latent and the target distributions. + data_match_fn: Function to match samples from the source and the target + distributions with a ``(src_lin, tgt_lin, src_quad, tgt_quad) -> matching`` + signature. + source_dim: Dimensionality of the source distribution. + target_dim: Dimensionality of the target distribution. + condition_dim: Dimension of the conditions. If :obj:`None`, the underlying + velocity field has no conditions. + time_sampler: Time sampler with a ``(rng, n_samples) -> time`` signature. + latent_noise_fn: Function to sample from the latent distribution in the + target space with a ``(rng, shape) -> noise`` signature. + If :obj:`None`, multivariate normal distribution is used. + latent_match_fn: Function to match samples from the latent distribution + and the samples from the conditional distribution with a + ``(latent, samples) -> matching`` signature. If :obj:`None`, no matching + is performed. + n_samples_per_src: Number of samples drawn from the conditional distribution + per one source sample. + kwargs: Keyword arguments for + :meth:`~ott.neural.networks.velocity_field.VelocityField.create_train_state`. + """ # noqa: E501 + + def __init__( + self, + vf: velocity_field.VelocityField, + flow: dynamics.BaseFlow, + data_match_fn: DataMatchFn_t, + *, + source_dim: int, + target_dim: int, + condition_dim: Optional[int] = None, + time_sampler: Callable[[jax.Array, int], + jnp.ndarray] = solver_utils.uniform_sampler, + latent_noise_fn: Optional[Callable[[jax.Array, Tuple[int, ...]], + jnp.ndarray]] = None, + latent_match_fn: Optional[Callable[[jnp.ndarray, jnp.ndarray], + jnp.ndarray]] = None, + n_samples_per_src: int = 1, + **kwargs: Any, + ): + self.vf = vf + self.flow = flow + self.data_match_fn = data_match_fn + self.time_sampler = time_sampler + if latent_noise_fn is None: + latent_noise_fn = functools.partial(_multivariate_normal, dim=target_dim) + self.latent_noise_fn = latent_noise_fn + self.latent_match_fn = latent_match_fn + self.n_samples_per_src = n_samples_per_src + + self.vf_state = self.vf.create_train_state( + input_dim=target_dim, + condition_dim=source_dim + (condition_dim or 0), + **kwargs + ) + self.step_fn = self._get_step_fn() + + def _get_step_fn(self) -> Callable: + + @jax.jit + def step_fn( + rng: jax.Array, + vf_state: train_state.TrainState, + time: jnp.ndarray, + source: jnp.ndarray, + target: jnp.ndarray, + latent: jnp.ndarray, + source_conditions: Optional[jnp.ndarray], + ): + + def loss_fn( + params: jnp.ndarray, time: jnp.ndarray, source: jnp.ndarray, + target: jnp.ndarray, latent: jnp.ndarray, + source_conditions: Optional[jnp.ndarray], rng: jax.Array + ) -> jnp.ndarray: + x_t = self.flow.compute_xt(rng, time, latent, target) + if source_conditions is None: + cond = source + else: + cond = jnp.concatenate([source, source_conditions], axis=-1) + + v_t = vf_state.apply_fn({"params": params}, time, x_t, cond) + u_t = self.flow.compute_ut(time, latent, target) + + return jnp.mean((v_t - u_t) ** 2) + + grad_fn = jax.value_and_grad(loss_fn) + loss, grads = grad_fn( + vf_state.params, time, source, target, latent, source_conditions, rng + ) + + return loss, vf_state.apply_gradients(grads=grads) + + return step_fn + + def __call__( + self, + loader: Iterable[Dict[str, np.ndarray]], + n_iters: int, + rng: Optional[jax.Array] = None + ) -> Dict[str, List[float]]: + """Train the GENOT model. + + Args: + loader: Data loader returning a dictionary with possible keys + `src_lin`, `tgt_lin`, `src_quad`, `tgt_quad`, `src_conditions`. + n_iters: Number of iterations to train the model. + rng: Random key for seeding. + + Returns: + Training logs. + """ + + def prepare_data( + batch: Dict[str, jnp.ndarray] + ) -> Tuple[Tuple[jnp.ndarray, Optional[jnp.ndarray], jnp.ndarray], Tuple[ + jnp.ndarray, jnp.ndarray, jnp.ndarray, jnp.ndarray]]: + src_lin, src_quad = batch.get("src_lin"), batch.get("src_quad") + tgt_lin, tgt_quad = batch.get("tgt_lin"), batch.get("tgt_quad") + arrs = src_lin, tgt_lin, src_quad, tgt_quad + + if src_quad is None and tgt_quad is None: # lin + src, tgt = src_lin, tgt_lin + elif src_lin is None and tgt_lin is None: # quad + src, tgt = src_quad, tgt_quad + elif all(arr is not None for arr in arrs): # fused quad + src = jnp.concatenate([src_lin, src_quad], axis=1) + tgt = jnp.concatenate([tgt_lin, tgt_quad], axis=1) + else: + raise RuntimeError("Cannot infer OT problem type from data.") + + return (src, batch.get("src_condition"), tgt), arrs + + rng = utils.default_prng_key(rng) + training_logs = {"loss": []} + for batch in loader: + rng = jax.random.split(rng, 5) + rng, rng_resample, rng_noise, rng_time, rng_step_fn = rng + + batch = jtu.tree_map(jnp.asarray, batch) + (src, src_cond, tgt), matching_data = prepare_data(batch) + + n = src.shape[0] + time = self.time_sampler(rng_time, n * self.n_samples_per_src) + latent = self.latent_noise_fn(rng_noise, (n, self.n_samples_per_src)) + + tmat = self.data_match_fn(*matching_data) # (n, m) + src_ixs, tgt_ixs = solver_utils.sample_conditional( # (n, k), (m, k) + rng_resample, + tmat, + k=self.n_samples_per_src, + ) + + src, tgt = src[src_ixs], tgt[tgt_ixs] # (n, k, ...), # (m, k, ...) + if src_cond is not None: + src_cond = src_cond[src_ixs] + + if self.latent_match_fn is not None: + src, src_cond, tgt = self._match_latent(rng, src, src_cond, latent, tgt) + + src = src.reshape(-1, *src.shape[2:]) # (n * k, ...) + tgt = tgt.reshape(-1, *tgt.shape[2:]) # (m * k, ...) + latent = latent.reshape(-1, *latent.shape[2:]) + if src_cond is not None: + src_cond = src_cond.reshape(-1, *src_cond.shape[2:]) + + loss, self.vf_state = self.step_fn( + rng_step_fn, self.vf_state, time, src, tgt, latent, src_cond + ) + + training_logs["loss"].append(float(loss)) + if len(training_logs["loss"]) >= n_iters: + break + + return training_logs + + def _match_latent( + self, rng: jax.Array, src: jnp.ndarray, src_cond: Optional[jnp.ndarray], + latent: jnp.ndarray, tgt: jnp.ndarray + ) -> Tuple[jnp.ndarray, Optional[jnp.ndarray], jnp.ndarray]: + + def resample( + rng: jax.Array, src: jnp.ndarray, src_cond: Optional[jnp.ndarray], + tgt: jnp.ndarray, latent: jnp.ndarray + ) -> Tuple[jnp.ndarray, Optional[jnp.ndarray], jnp.ndarray]: + tmat = self.latent_match_fn(latent, tgt) # (n, k) + + src_ixs, tgt_ixs = solver_utils.sample_joint(rng, tmat) # (n,), (m,) + src, tgt = src[src_ixs], tgt[tgt_ixs] + if src_cond is not None: + src_cond = src_cond[src_ixs] + + return src, src_cond, tgt + + cond_axis = None if src_cond is None else 1 + in_axes, out_axes = (0, 1, cond_axis, 1, 1), (1, cond_axis, 1) + resample_fn = jax.jit(jax.vmap(resample, in_axes, out_axes)) + + rngs = jax.random.split(rng, self.n_samples_per_src) + return resample_fn(rngs, src, src_cond, tgt, latent) + + def transport( + self, + source: jnp.ndarray, + condition: Optional[jnp.ndarray] = None, + t0: float = 0.0, + t1: float = 1.0, + rng: Optional[jax.Array] = None, + **kwargs: Any, + ) -> jnp.ndarray: + """Transport data with the learned plan. + + This function pushes forward the source distribution to its conditional + distribution by solving the neural ODE. + + Args: + source: Data to transport. + condition: Condition of the input data. + t0: Starting time of integration of neural ODE. + t1: End time of integration of neural ODE. + rng: Random generate used to sample from the latent distribution. + kwargs: Keyword arguments for :func:`~diffrax.odesolve`. + + Returns: + The push-forward defined by the learned transport plan. + """ + + def vf(t: jnp.ndarray, x: jnp.ndarray, cond: jnp.ndarray) -> jnp.ndarray: + params = self.vf_state.params + return self.vf_state.apply_fn({"params": params}, t, x, cond) + + def solve_ode(x: jnp.ndarray, cond: jnp.ndarray) -> jnp.ndarray: + ode_term = diffrax.ODETerm(vf) + sol = diffrax.diffeqsolve( + ode_term, + t0=t0, + t1=t1, + y0=x, + args=cond, + **kwargs, + ) + return sol.ys[0] + + kwargs.setdefault("dt0", None) + kwargs.setdefault("solver", diffrax.Tsit5()) + kwargs.setdefault( + "stepsize_controller", diffrax.PIDController(rtol=1e-5, atol=1e-5) + ) + + rng = utils.default_prng_key(rng) + latent = self.latent_noise_fn(rng, (len(source),)) + + if condition is not None: + source = jnp.concatenate([source, condition], axis=-1) + + return jax.jit(jax.vmap(solve_ode))(latent, source) + + +def _multivariate_normal( + rng: jax.Array, + shape: Tuple[int, ...], + dim: int, + mean: float = 0.0, + cov: float = 1.0 +) -> jnp.ndarray: + mean = jnp.full(dim, fill_value=mean) + cov = jnp.diag(jnp.full(dim, fill_value=cov)) + return jax.random.multivariate_normal(rng, mean=mean, cov=cov, shape=shape) diff --git a/src/ott/neural/methods/flows/otfm.py b/src/ott/neural/methods/flows/otfm.py new file mode 100644 index 000000000..65d6a149d --- /dev/null +++ b/src/ott/neural/methods/flows/otfm.py @@ -0,0 +1,199 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple + +import jax +import jax.numpy as jnp +import jax.tree_util as jtu +import numpy as np + +import diffrax +from flax.training import train_state + +from ott import utils +from ott.neural.methods.flows import dynamics +from ott.neural.networks import velocity_field +from ott.solvers import utils as solver_utils + +__all__ = ["OTFlowMatching"] + + +class OTFlowMatching: + """(Optimal transport) flow matching :cite:`lipman:22`. + + With an extension to OT-FM :cite:`tong:23,pooladian:23`. + + Args: + vf: Vector field parameterized by a neural network. + flow: Flow between the source and the target distributions. + match_fn: Function to match samples from the source and the target + distributions. It has a ``(src, tgt) -> matching`` signature. + time_sampler: Time sampler with a ``(rng, n_samples) -> time`` signature. + kwargs: Keyword arguments for + :meth:`~ott.neural.networks.velocity_field.VelocityField.create_train_state`. + """ + + def __init__( + self, + vf: velocity_field.VelocityField, + flow: dynamics.BaseFlow, + match_fn: Optional[Callable[[jnp.ndarray, jnp.ndarray], + jnp.ndarray]] = None, + time_sampler: Callable[[jax.Array, int], + jnp.ndarray] = solver_utils.uniform_sampler, + **kwargs: Any, + ): + self.vf = vf + self.flow = flow + self.time_sampler = time_sampler + self.match_fn = match_fn + + self.vf_state = self.vf.create_train_state( + input_dim=self.vf.output_dims[-1], **kwargs + ) + self.step_fn = self._get_step_fn() + + def _get_step_fn(self) -> Callable: + + @jax.jit + def step_fn( + rng: jax.Array, + vf_state: train_state.TrainState, + source: jnp.ndarray, + target: jnp.ndarray, + source_conditions: Optional[jnp.ndarray], + ) -> Tuple[Any, Any]: + + def loss_fn( + params: jnp.ndarray, t: jnp.ndarray, source: jnp.ndarray, + target: jnp.ndarray, source_conditions: Optional[jnp.ndarray], + rng: jax.Array + ) -> jnp.ndarray: + + x_t = self.flow.compute_xt(rng, t, source, target) + v_t = vf_state.apply_fn({"params": params}, t, x_t, source_conditions) + u_t = self.flow.compute_ut(t, source, target) + + return jnp.mean((v_t - u_t) ** 2) + + batch_size = len(source) + key_t, key_model = jax.random.split(rng, 2) + t = self.time_sampler(key_t, batch_size) + grad_fn = jax.value_and_grad(loss_fn) + loss, grads = grad_fn( + vf_state.params, t, source, target, source_conditions, key_model + ) + return vf_state.apply_gradients(grads=grads), loss + + return step_fn + + def __call__( # noqa: D102 + self, + loader: Iterable[Dict[str, np.ndarray]], + *, + n_iters: int, + rng: Optional[jax.Array] = None, + ) -> Dict[str, List[float]]: + """Train the OTFlowMatching model. + + Args: + loader: Data loader returning a dictionary with possible keys + `src_lin`, `tgt_lin`, `src_condition`. + n_iters: Number of iterations to train the model. + rng: Random number generator. + + Returns: + Training logs. + """ + rng = utils.default_prng_key(rng) + training_logs = {"loss": []} + for batch in loader: + rng, rng_resample, rng_step_fn = jax.random.split(rng, 3) + + batch = jtu.tree_map(jnp.asarray, batch) + + src, tgt = batch["src_lin"], batch["tgt_lin"] + src_cond = batch.get("src_condition") + + if self.match_fn is not None: + tmat = self.match_fn(src, tgt) + src_ixs, tgt_ixs = solver_utils.sample_joint(rng_resample, tmat) + src, tgt = src[src_ixs], tgt[tgt_ixs] + src_cond = None if src_cond is None else src_cond[src_ixs] + + self.vf_state, loss = self.step_fn( + rng_step_fn, + self.vf_state, + src, + tgt, + src_cond, + ) + + training_logs["loss"].append(float(loss)) + if len(training_logs["loss"]) >= n_iters: + break + + return training_logs + + def transport( + self, + x: jnp.ndarray, + condition: Optional[jnp.ndarray] = None, + t0: float = 0.0, + t1: float = 1.0, + **kwargs: Any, + ) -> jnp.ndarray: + """Transport data with the learned map. + + This method pushes-forward the data by solving the neural ODE + parameterized by the velocity field. + + Args: + x: Initial condition of the ODE of shape ``[batch_size, ...]``. + condition: Condition of the input data of shape ``[batch_size, ...]``. + t0: Starting point of integration. + t1: End point of integration. + kwargs: Keyword arguments for the ODE solver. + + Returns: + The push-forward or pull-back distribution defined by the learned + transport plan. + """ + + def vf( + t: jnp.ndarray, x: jnp.ndarray, cond: Optional[jnp.ndarray] + ) -> jnp.ndarray: + params = self.vf_state.params + return self.vf_state.apply_fn({"params": params}, t, x, cond) + + def solve_ode(x: jnp.ndarray, cond: Optional[jnp.ndarray]) -> jnp.ndarray: + ode_term = diffrax.ODETerm(vf) + result = diffrax.diffeqsolve( + ode_term, + t0=t0, + t1=t1, + y0=x, + args=cond, + **kwargs, + ) + return result.ys[0] + + kwargs.setdefault("dt0", None) + kwargs.setdefault("solver", diffrax.Tsit5()) + kwargs.setdefault( + "stepsize_controller", diffrax.PIDController(rtol=1e-5, atol=1e-5) + ) + + in_axes = [0, None if condition is None else 0] + return jax.jit(jax.vmap(solve_ode, in_axes))(x, condition) diff --git a/src/ott/neural/solvers/map_estimator.py b/src/ott/neural/methods/monge_gap.py similarity index 62% rename from src/ott/neural/solvers/map_estimator.py rename to src/ott/neural/methods/monge_gap.py index f5389f50d..140fad4a1 100644 --- a/src/ott/neural/solvers/map_estimator.py +++ b/src/ott/neural/methods/monge_gap.py @@ -18,6 +18,7 @@ Callable, Dict, Iterator, + Literal, Optional, Sequence, Tuple, @@ -26,17 +27,146 @@ import jax import jax.numpy as jnp + import optax from flax.core import frozen_dict from flax.training import train_state from ott import utils -from ott.neural.solvers import neuraldual +from ott.geometry import costs, pointcloud +from ott.neural.networks import potentials +from ott.solvers import linear +from ott.solvers.linear import sinkhorn + +__all__ = ["monge_gap", "monge_gap_from_samples", "MongeGapEstimator"] + + +def monge_gap( + map_fn: Callable[[jnp.ndarray], jnp.ndarray], + reference_points: jnp.ndarray, + cost_fn: Optional[costs.CostFn] = None, + epsilon: Optional[float] = None, + relative_epsilon: Optional[bool] = None, + scale_cost: Union[int, float, Literal["mean", "max_cost", "median"]] = 1.0, + return_output: bool = False, + **kwargs: Any +) -> Union[float, Tuple[float, sinkhorn.SinkhornOutput]]: + r"""Monge gap regularizer :cite:`uscidda:23`. + + For a cost function :math:`c` and empirical reference measure + :math:`\hat{\rho}_n=\frac{1}{n}\sum_{i=1}^n \delta_{x_i}`, the + (entropic) Monge gap of a map function + :math:`T:\mathbb{R}^d\rightarrow\mathbb{R}^d` is defined as: + + .. math:: + \mathcal{M}^c_{\hat{\rho}_n, \varepsilon} (T) + = \frac{1}{n} \sum_{i=1}^n c(x_i, T(x_i)) - + W_{c, \varepsilon}(\hat{\rho}_n, T \sharp \hat{\rho}_n) -__all__ = ["MapEstimator"] + See :cite:`uscidda:23` Eq. (8). This function is a thin wrapper that calls + :func:`~ott.neural.methods.monge_gap.monge_gap_from_samples`. + Args: + map_fn: Callable corresponding to map :math:`T` in definition above. The + callable should be vectorized (e.g. using :func:`~jax.vmap`), i.e, + able to process a *batch* of vectors of size `d`, namely + ``map_fn`` applied to an array returns an array of the same shape. + reference_points: Array of `[n,d]` points, :math:`\hat\rho_n`. + cost_fn: An object of class :class:`~ott.geometry.costs.CostFn`. + epsilon: Regularization parameter. See + :class:`~ott.geometry.pointcloud.PointCloud` + relative_epsilon: when `False`, the parameter ``epsilon`` specifies the + value of the entropic regularization parameter. When `True`, ``epsilon`` + refers to a fraction of the + :attr:`~ott.geometry.pointcloud.PointCloud.mean_cost_matrix`, which is + computed adaptively using ``source`` and ``target`` points. + scale_cost: option to rescale the cost matrix. Implemented scalings are + 'median', 'mean' and 'max_cost'. Alternatively, a float factor can be + given to rescale the cost such that ``cost_matrix /= scale_cost``. + return_output: boolean to also return the + :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput`. + kwargs: holds the kwargs to instantiate the or + :class:`~ott.solvers.linear.sinkhorn.Sinkhorn` solver to + compute the regularized OT cost. + + Returns: + The Monge gap value and optionally the + :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput` + """ + target = map_fn(reference_points) + return monge_gap_from_samples( + source=reference_points, + target=target, + cost_fn=cost_fn, + epsilon=epsilon, + relative_epsilon=relative_epsilon, + scale_cost=scale_cost, + return_output=return_output, + **kwargs + ) + + +def monge_gap_from_samples( + source: jnp.ndarray, + target: jnp.ndarray, + cost_fn: Optional[costs.CostFn] = None, + epsilon: Optional[float] = None, + relative_epsilon: Optional[bool] = None, + scale_cost: Union[int, float, Literal["mean", "max_cost", "median"]] = 1.0, + return_output: bool = False, + **kwargs: Any +) -> Union[float, Tuple[float, sinkhorn.SinkhornOutput]]: + r"""Monge gap, instantiated in terms of samples before / after applying map. -class MapEstimator: + .. math:: + \frac{1}{n} \sum_{i=1}^n c(x_i, y_i)) - + W_{c, \varepsilon}(\frac{1}{n}\sum_i \delta_{x_i}, + \frac{1}{n}\sum_i \delta_{y_i}) + + where :math:`W_{c, \varepsilon}` is an entropy-regularized optimal transport + cost, the :attr:`~ott.solvers.linear.sinkhorn.SinkhornOutput.ent_reg_cost`. + + Args: + source: samples from first measure, array of shape ``[n, d]``. + target: samples from second measure, array of shape ``[n, d]``. + cost_fn: a cost function between two points in dimension :math:`d`. + If :obj:`None`, :class:`~ott.geometry.costs.SqEuclidean` is used. + epsilon: Regularization parameter. See + :class:`~ott.geometry.pointcloud.PointCloud` + relative_epsilon: when `False`, the parameter ``epsilon`` specifies the + value of the entropic regularization parameter. When `True`, ``epsilon`` + refers to a fraction of the + :attr:`~ott.geometry.pointcloud.PointCloud.mean_cost_matrix`, which is + computed adaptively using ``source`` and ``target`` points. + scale_cost: option to rescale the cost matrix. Implemented scalings are + 'median', 'mean' and 'max_cost'. Alternatively, a float factor can be + given to rescale the cost such that ``cost_matrix /= scale_cost``. + return_output: boolean to also return the + :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput`. + kwargs: holds the kwargs to instantiate the or + :class:`~ott.solvers.linear.sinkhorn.Sinkhorn` solver to + compute the regularized OT cost. + + Returns: + The Monge gap value and optionally the + :class:`~ott.solvers.linear.sinkhorn.SinkhornOutput` + """ + cost_fn = costs.SqEuclidean() if cost_fn is None else cost_fn + geom = pointcloud.PointCloud( + x=source, + y=target, + cost_fn=cost_fn, + epsilon=epsilon, + relative_epsilon=relative_epsilon, + scale_cost=scale_cost, + ) + gt_displacement_cost = jnp.mean(jax.vmap(cost_fn)(source, target)) + out = linear.solve(geom=geom, **kwargs) + loss = gt_displacement_cost - out.ent_reg_cost + return (loss, out) if return_output else loss + + +class MongeGapEstimator: r"""Mapping estimator between probability measures. It estimates a map :math:`T` by minimizing the loss: @@ -54,7 +184,7 @@ class MapEstimator: For instance, :math:`\Delta` can be the :func:`~ott.tools.sinkhorn_divergence.sinkhorn_divergence` - and :math:`R` the :func:`~ott.neural.losses.monge_gap_from_samples` + and :math:`R` the :func:`~ott.neural.methods.monge_gap.monge_gap_from_samples` :cite:`uscidda:23` for a given cost function :math:`c`. In that case, it estimates a :math:`c`-OT map, i.e. a map :math:`T` optimal for the Monge problem induced by :math:`c`. @@ -77,7 +207,7 @@ class MapEstimator: def __init__( self, dim_data: int, - model: neuraldual.BaseW2NeuralDual, + model: potentials.BasePotential, optimizer: Optional[optax.OptState] = None, fitting_loss: Optional[Callable[[jnp.ndarray, jnp.ndarray], Tuple[float, Optional[Any]]]] = None, @@ -113,7 +243,7 @@ def __init__( def setup( self, dim_data: int, - neural_net: neuraldual.BaseW2NeuralDual, + neural_net: potentials.BasePotential, optimizer: optax.OptState, ): """Setup all components required to train the network.""" @@ -129,11 +259,11 @@ def setup( def regularizer(self) -> Callable[[jnp.ndarray, jnp.ndarray], float]: """Regularizer added to the fitting loss. - Can be, e.g. the :func:`~ott.neural.losses.monge_gap_from_samples`. + Can be, e.g. the :func:`~ott.neural.methods.monge_gap.monge_gap_from_samples`. If no regularizer is passed for solver instantiation, or regularization weight :attr:`regularizer_strength` is 0, return 0 by default along with an empty set of log values. - """ + """ # noqa: E501 if self._regularizer is not None: return self._regularizer return lambda *_, **__: (0.0, None) diff --git a/src/ott/neural/solvers/neuraldual.py b/src/ott/neural/methods/neuraldual.py similarity index 78% rename from src/ott/neural/solvers/neuraldual.py rename to src/ott/neural/methods/neuraldual.py index fffa92751..30fd08d4e 100644 --- a/src/ott/neural/solvers/neuraldual.py +++ b/src/ott/neural/methods/neuraldual.py @@ -11,10 +11,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import abc import warnings from typing import ( - Any, Callable, Dict, Iterator, @@ -27,139 +25,19 @@ import jax import jax.numpy as jnp + import optax -from flax import linen as nn -from flax import struct -from flax.core import frozen_dict -from flax.training import train_state from ott import utils from ott.geometry import costs -from ott.neural import models -from ott.neural.solvers import conjugate -from ott.problems.linear import potentials +from ott.neural.networks import icnn, potentials +from ott.neural.networks.layers import conjugate +from ott.problems.linear import potentials as dual_potentials -__all__ = ["W2NeuralTrainState", "BaseW2NeuralDual", "W2NeuralDual"] +__all__ = ["W2NeuralDual"] Train_t = Dict[Literal["train_logs", "valid_logs"], Dict[str, List[float]]] -Callback_t = Callable[[int, potentials.DualPotentials], None] - -PotentialValueFn_t = Callable[[jnp.ndarray], jnp.ndarray] -PotentialGradientFn_t = Callable[[jnp.ndarray], jnp.ndarray] - - -class W2NeuralTrainState(train_state.TrainState): - """Adds information about the model's value and gradient to the state. - - This extends :class:`~flax.training.train_state.TrainState` to include - the potential methods from the - :class:`~ott.neural.solvers.neuraldual.BaseW2NeuralDual` used during training. - - Args: - potential_value_fn: the potential's value function - potential_gradient_fn: the potential's gradient function - """ - potential_value_fn: Callable[ - [frozen_dict.FrozenDict[str, jnp.ndarray], Optional[PotentialValueFn_t]], - PotentialValueFn_t] = struct.field(pytree_node=False) - potential_gradient_fn: Callable[[frozen_dict.FrozenDict[str, jnp.ndarray]], - PotentialGradientFn_t] = struct.field( - pytree_node=False - ) - - -class BaseW2NeuralDual(abc.ABC, nn.Module): - """Base class for the neural solver models.""" - - @property - @abc.abstractmethod - def is_potential(self) -> bool: - """Indicates if the module implements a potential value or a vector field. - - Returns: - ``True`` if the module defines a potential, ``False`` if it defines a - vector field. - """ - - def potential_value_fn( - self, - params: frozen_dict.FrozenDict[str, jnp.ndarray], - other_potential_value_fn: Optional[PotentialValueFn_t] = None, - ) -> PotentialValueFn_t: - r"""Return a function giving the value of the potential. - - Applies the module if :attr:`is_potential` is ``True``, otherwise - constructs the value of the potential from the gradient with - - .. math:: - - g(y) = -f(\nabla_y g(y)) + y^T \nabla_y g(y) - - where :math:`\nabla_y g(y)` is detached for the envelope theorem - :cite:`danskin:67,bertsekas:71` - to give the appropriate first derivatives of this construction. - - Args: - params: parameters of the module - other_potential_value_fn: function giving the value of the other - potential. Only needed when :attr:`is_potential` is ``False``. - - Returns: - A function that can be evaluated to obtain a potential value, or a linear - interpolation of a potential. - """ - if self.is_potential: - return lambda x: self.apply({"params": params}, x) - - assert other_potential_value_fn is not None, \ - "The value of the gradient-based potential depends " \ - "on the value of the other potential." - - def value_fn(x: jnp.ndarray) -> jnp.ndarray: - squeeze = x.ndim == 1 - if squeeze: - x = jnp.expand_dims(x, 0) - grad_g_x = jax.lax.stop_gradient(self.apply({"params": params}, x)) - value = -other_potential_value_fn(grad_g_x) + \ - jax.vmap(jnp.dot)(grad_g_x, x) - return value.squeeze(0) if squeeze else value - - return value_fn - - def potential_gradient_fn( - self, - params: frozen_dict.FrozenDict[str, jnp.ndarray], - ) -> PotentialGradientFn_t: - """Return a function returning a vector or the gradient of the potential. - - Args: - params: parameters of the module - - Returns: - A function that can be evaluated to obtain the potential's gradient - """ - if self.is_potential: - return jax.vmap(jax.grad(self.potential_value_fn(params))) - return lambda x: self.apply({"params": params}, x) - - def create_train_state( - self, - rng: jax.Array, - optimizer: optax.OptState, - input: Union[int, Tuple[int, ...]], - **kwargs: Any, - ) -> W2NeuralTrainState: - """Create initial training state.""" - params = self.init(rng, jnp.ones(input))["params"] - - return W2NeuralTrainState.create( - apply_fn=self.apply, - params=params, - tx=optimizer, - potential_value_fn=self.potential_value_fn, - potential_gradient_fn=self.potential_gradient_fn, - **kwargs, - ) +Callback_t = Callable[[int, dual_potentials.DualPotentials], None] class W2NeuralDual: @@ -170,7 +48,8 @@ class W2NeuralDual: denoted source and target, respectively. This is achieved by parameterizing a Kantorovich potential :math:`f_\theta: \mathbb{R}^n\rightarrow\mathbb{R}` associated with the :math:`\alpha` measure with an - :class:`~ott.neural.models.ICNN` or :class:`~ott.neural.models.MLP`, where + :class:`~ott.neural.networks.icnn.ICNN` or a + :class:`~ott.neural.networks.potentials.PotentialMLP`, where :math:`\nabla f` transports source to target cells. This potential is learned by optimizing the dual form associated with the negative inner product cost @@ -186,10 +65,10 @@ class W2NeuralDual: transport map from :math:`\beta` to :math:`\alpha`. This solver estimates the conjugate :math:`f^\star` with a neural approximation :math:`g` that is fine-tuned - with :class:`~ott.neural.solvers.conjugate.FenchelConjugateSolver`, + with :class:`~ott.neural.networks.layers.conjugate.FenchelConjugateSolver`, which is a combination further described in :cite:`amos:23`. - The :class:`~ott.neural.solvers.neuraldual.BaseW2NeuralDual` potentials for + The :class:`~ott.neural.networks.potentials.BasePotential` potentials for ``neural_f`` and ``neural_g`` can 1. both provide the values of the potentials :math:`f` and :math:`g`, or @@ -198,7 +77,7 @@ class W2NeuralDual: via the Fenchel conjugate as discussed in :cite:`amos:23`. The potential's value or gradient mapping is specified via - :attr:`~ott.neural.solvers.neuraldual.BaseW2NeuralDual.is_potential`. + :attr:`~ott.neural.networks.potentials.BasePotential.is_potential`. Args: dim_data: input dimensionality of data required for network init @@ -228,8 +107,8 @@ class W2NeuralDual: def __init__( self, dim_data: int, - neural_f: Optional[BaseW2NeuralDual] = None, - neural_g: Optional[BaseW2NeuralDual] = None, + neural_f: Optional[potentials.BasePotential] = None, + neural_g: Optional[potentials.BasePotential] = None, optimizer_f: Optional[optax.OptState] = None, optimizer_g: Optional[optax.OptState] = None, num_train_iters: int = 20000, @@ -266,9 +145,9 @@ def __init__( # set default neural architectures if neural_f is None: - neural_f = models.ICNN(dim_data=dim_data, dim_hidden=[64, 64, 64, 64]) + neural_f = icnn.ICNN(dim_data=dim_data, dim_hidden=[64, 64, 64, 64]) if neural_g is None: - neural_g = models.ICNN(dim_data=dim_data, dim_hidden=[64, 64, 64, 64]) + neural_g = icnn.ICNN(dim_data=dim_data, dim_hidden=[64, 64, 64, 64]) self.neural_f = neural_f self.neural_g = neural_g @@ -285,8 +164,8 @@ def __init__( def setup( self, rng: jax.Array, - neural_f: BaseW2NeuralDual, - neural_g: BaseW2NeuralDual, + neural_f: potentials.BasePotential, + neural_g: potentials.BasePotential, dim_data: int, optimizer_f: optax.OptState, optimizer_g: optax.OptState, @@ -301,13 +180,13 @@ def setup( f"the `W2NeuralDual` setting, with positive weights " \ f"being {self.pos_weights}." if isinstance( - neural_f, models.ICNN + neural_f, icnn.ICNN ) and neural_f.pos_weights is not self.pos_weights: warnings.warn(warn_str, stacklevel=2) neural_f.pos_weights = self.pos_weights if isinstance( - neural_g, models.ICNN + neural_g, icnn.ICNN ) and neural_g.pos_weights is not self.pos_weights: warnings.warn(warn_str, stacklevel=2) neural_g.pos_weights = self.pos_weights @@ -325,7 +204,7 @@ def setup( # default to using back_and_forth with the non-convex models if self.back_and_forth is None: - self.back_and_forth = isinstance(neural_f, models.MLP) + self.back_and_forth = isinstance(neural_f, potentials.PotentialMLP) if self.num_inner_iters == 1 and self.parallel_updates: self.train_step_parallel = self.get_step_fn( @@ -359,8 +238,8 @@ def __call__( # noqa: D102 validloader_source: Iterator[jnp.ndarray], validloader_target: Iterator[jnp.ndarray], callback: Optional[Callback_t] = None, - ) -> Union[potentials.DualPotentials, Tuple[potentials.DualPotentials, - Train_t]]: + ) -> Union[dual_potentials.DualPotentials, + Tuple[dual_potentials.DualPotentials, Train_t]]: logs = self.train_fn( trainloader_source, trainloader_target, @@ -643,7 +522,7 @@ def step_fn(state_f, state_g, batch): def to_dual_potentials( self, finetune_g: bool = True - ) -> potentials.DualPotentials: + ) -> dual_potentials.DualPotentials: r"""Return the Kantorovich dual potentials from the trained potentials. Args: @@ -664,7 +543,7 @@ def g_value_finetuned(y: jnp.ndarray) -> jnp.ndarray: ) return -f_value(grad_g_y) + jnp.dot(grad_g_y, y) - return potentials.DualPotentials( + return dual_potentials.DualPotentials( f=f_value, g=g_value_prediction if not finetune_g or self.conjugate_solver is None else g_value_finetuned, diff --git a/src/ott/neural/networks/__init__.py b/src/ott/neural/networks/__init__.py new file mode 100644 index 000000000..5f2fd8636 --- /dev/null +++ b/src/ott/neural/networks/__init__.py @@ -0,0 +1,14 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from . import icnn, layers, potentials, velocity_field diff --git a/src/ott/neural/networks/icnn.py b/src/ott/neural/networks/icnn.py new file mode 100644 index 000000000..c6896dac4 --- /dev/null +++ b/src/ott/neural/networks/icnn.py @@ -0,0 +1,160 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Callable, Optional, Sequence, Tuple, Union + +import jax +import jax.numpy as jnp + +from flax import linen as nn + +from ott.neural.networks import potentials +from ott.neural.networks.layers import posdef + +__all__ = ["ICNN"] + +DEFAULT_KERNEL_INIT = lambda *a, **k: nn.initializers.normal()(*a, **k) +DEFAULT_RECTIFIER = nn.activation.relu +DEFAULT_ACTIVATION = nn.activation.relu + + +class ICNN(potentials.BasePotential): + """Input convex neural network (ICNN). + + Implementation of input convex neural networks as introduced in + :cite:`amos:17` with initialization schemes proposed by :cite:`bunne:22`. + + Args: + dim_data: data dimensionality. + dim_hidden: sequence specifying size of hidden dimensions. The + output dimension of the last layer is 1 by default. + ranks: ranks of the matrices :math:`A_i` used as low-rank factors + for the quadratic potentials. If a sequence is passed, it must contain + ``len(dim_hidden) + 2`` elements, where the last 2 elements correspond + to the ranks of the final layer with dimension 1 and the potentials, + respectively. + init_fn: Initializer for the kernel weight matrices. + The default is :func:`~flax.linen.initializers.normal`. + act_fn: choice of activation function used in network architecture, + needs to be convex. The default is :func:`~flax.linen.activation.relu`. + pos_weights: Enforce positive weights with a projection. + If :obj:`False`, the positive weights should be enforced with clipping + or regularization in the loss. + rectifier_fn: function to ensure the non negativity of the weights. + The default is :func:`~flax.linen.activation.relu`. + gaussian_map_samples: Tuple of source and target points, used to initialize + the ICNN to mimic the linear Bures map that morphs the (Gaussian + approximation) of the input measure to that of the target measure. If + :obj:`None`, the identity initialization is used, and ICNN mimics half the + squared Euclidean norm. + """ + + dim_data: int + dim_hidden: Sequence[int] + ranks: Union[int, Tuple[int, ...]] = 1 + init_fn: Callable[[jax.Array, Tuple[int, ...], Any], + jnp.ndarray] = DEFAULT_KERNEL_INIT + act_fn: Callable[[jnp.ndarray], jnp.ndarray] = DEFAULT_ACTIVATION + pos_weights: bool = False + rectifier_fn: Callable[[jnp.ndarray], jnp.ndarray] = DEFAULT_RECTIFIER + gaussian_map_samples: Optional[Tuple[jnp.ndarray, jnp.ndarray]] = None + + def setup(self) -> None: # noqa: D102 + dim_hidden = list(self.dim_hidden) + [1] + *ranks, pos_def_rank = self._normalize_ranks() + + # final layer computes average, still with normalized rescaling + self.w_zs = [self._get_wz(dim) for dim in dim_hidden[1:]] + # subsequent layers re-injected into convex functions + self.w_xs = [ + self._get_wx(dim, rank) for dim, rank in zip(dim_hidden, ranks) + ] + self.pos_def_potentials = self._get_pos_def_potentials(pos_def_rank) + + @nn.compact + def __call__(self, x: jnp.ndarray) -> float: # noqa: D102 + w_x, *w_xs = self.w_xs + assert len(self.w_zs) == len(w_xs), (len(self.w_zs), len(w_xs)) + + z = self.act_fn(w_x(x)) + for w_z, w_x in zip(self.w_zs, w_xs): + z = self.act_fn(w_z(z) + w_x(x)) + z = z + self.pos_def_potentials(x) + + return z.squeeze() + + def _get_wz(self, dim: int) -> nn.Module: + if self.pos_weights: + return posdef.PositiveDense( + dim, + kernel_init=self.init_fn, + use_bias=False, + rectifier_fn=self.rectifier_fn, + ) + + return nn.Dense( + dim, + kernel_init=self.init_fn, + use_bias=False, + ) + + def _get_wx(self, dim: int, rank: int) -> nn.Module: + return posdef.PosDefPotentials( + rank=rank, + num_potentials=dim, + use_linear=True, + use_bias=True, + kernel_diag_init=nn.initializers.zeros, + kernel_lr_init=self.init_fn, + kernel_linear_init=self.init_fn, + bias_init=nn.initializers.zeros, + ) + + def _get_pos_def_potentials(self, rank: int) -> posdef.PosDefPotentials: + kwargs = { + "num_potentials": 1, + "use_linear": True, + "use_bias": True, + "bias_init": nn.initializers.zeros + } + + if self.gaussian_map_samples is None: + return posdef.PosDefPotentials( + rank=rank, + kernel_diag_init=nn.initializers.ones, + kernel_lr_init=nn.initializers.zeros, + kernel_linear_init=nn.initializers.zeros, + **kwargs, + ) + + source, target = self.gaussian_map_samples + return posdef.PosDefPotentials.init_from_samples( + source, + target, + rank=self.dim_data, + kernel_diag_init=nn.initializers.zeros, + **kwargs, + ) + + def _normalize_ranks(self) -> Tuple[int, ...]: + # +2 for the newly added layer with 1 + the final potentials + n_ranks = len(self.dim_hidden) + 2 + if isinstance(self.ranks, int): + return (self.ranks,) * n_ranks + + assert len(self.ranks) == n_ranks, (len(self.ranks), n_ranks) + return tuple(self.ranks) + + @property + def is_potential(self) -> bool: # noqa: D102 + return True diff --git a/src/ott/neural/networks/layers/__init__.py b/src/ott/neural/networks/layers/__init__.py new file mode 100644 index 000000000..237c5f275 --- /dev/null +++ b/src/ott/neural/networks/layers/__init__.py @@ -0,0 +1,14 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from . import conjugate, posdef, time_encoder diff --git a/src/ott/neural/solvers/conjugate.py b/src/ott/neural/networks/layers/conjugate.py similarity index 100% rename from src/ott/neural/solvers/conjugate.py rename to src/ott/neural/networks/layers/conjugate.py diff --git a/src/ott/neural/layers.py b/src/ott/neural/networks/layers/posdef.py similarity index 98% rename from src/ott/neural/layers.py rename to src/ott/neural/networks/layers/posdef.py index 78c2ef3b8..41663ffe3 100644 --- a/src/ott/neural/layers.py +++ b/src/ott/neural/networks/layers/posdef.py @@ -15,6 +15,7 @@ import jax import jax.numpy as jnp + from flax import linen as nn __all__ = ["PositiveDense", "PosDefPotentials"] @@ -24,7 +25,6 @@ Dtype = Any Array = jnp.ndarray -# wrap to silence docs linter DEFAULT_KERNEL_INIT = lambda *a, **k: nn.initializers.lecun_normal()(*a, **k) DEFAULT_BIAS_INIT = nn.initializers.zeros DEFAULT_RECTIFIER = nn.activation.relu @@ -78,7 +78,8 @@ def __call__(self, x: jnp.ndarray) -> jnp.ndarray: class PosDefPotentials(nn.Module): - r""":math:`\frac{1}{2} x^T (A_i A_i^T + \text{Diag}(d_i)) x + b_i^T x^2 + c_i` potentials. + r""":math:`\frac{1}{2} x^T (A_i A_i^T + \text{Diag}(d_i)) x + b_i^T x^2 + c_i` + potentials. This class implements a layer that takes (batched) ``d``-dimensional vectors ``x`` in, to output a ``num_potentials``-dimensional vector. Each of the @@ -110,7 +111,7 @@ class PosDefPotentials(nn.Module): bias_init: Initializer for the bias. The default is :func:`~flax.linen.initializers.zeros`. precision: Numerical precision of the computation. - """ # noqa: E501 + """ # noqa: D205,E501 num_potentials: int rank: int = 0 diff --git a/src/ott/neural/networks/layers/time_encoder.py b/src/ott/neural/networks/layers/time_encoder.py new file mode 100644 index 000000000..b02bd125c --- /dev/null +++ b/src/ott/neural/networks/layers/time_encoder.py @@ -0,0 +1,34 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import jax.numpy as jnp + +__all__ = ["cyclical_time_encoder"] + + +def cyclical_time_encoder(t: jnp.ndarray, n_freqs: int = 128) -> jnp.ndarray: + r"""Encode time :math:`t` into a cyclical representation. + + Time :math:`t` is encoded as :math:`cos(\hat{t})` and :math:`sin(\hat{t})` + where :math:`\hat{t} = [2\pi t, 2\pi 2 t,\dots, 2\pi n_f t]`. + + Args: + t: Time of shape ``[n, 1]``. + n_freqs: Frequency :math:`n_f` of the cyclical encoding. + + Returns: + Encoded time of shape ``[n, 2 * n_freqs]``. + """ + freq = 2 * jnp.arange(n_freqs) * jnp.pi + t = freq * t + return jnp.concatenate([jnp.cos(t), jnp.sin(t)], axis=-1) diff --git a/src/ott/neural/networks/potentials.py b/src/ott/neural/networks/potentials.py new file mode 100644 index 000000000..563f4537c --- /dev/null +++ b/src/ott/neural/networks/potentials.py @@ -0,0 +1,185 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import abc +from typing import Any, Callable, Optional, Sequence, Tuple, Union + +import jax +import jax.numpy as jnp + +import optax +from flax import linen as nn +from flax import struct +from flax.core import frozen_dict +from flax.training import train_state + +__all__ = ["PotentialTrainState", "BasePotential", "PotentialMLP"] + +PotentialValueFn_t = Callable[[jnp.ndarray], jnp.ndarray] +PotentialGradientFn_t = Callable[[jnp.ndarray], jnp.ndarray] + + +class PotentialTrainState(train_state.TrainState): + """Adds information about the model's value and gradient to the state. + + This extends :class:`~flax.training.train_state.TrainState` to include + the potential methods from the + :class:`~ott.neural.networks.potentials.BasePotential` used during training. + + Args: + potential_value_fn: the potential's value function + potential_gradient_fn: the potential's gradient function + """ + potential_value_fn: Callable[ + [frozen_dict.FrozenDict[str, jnp.ndarray], Optional[PotentialValueFn_t]], + PotentialValueFn_t] = struct.field(pytree_node=False) + potential_gradient_fn: Callable[[frozen_dict.FrozenDict[str, jnp.ndarray]], + PotentialGradientFn_t] = struct.field( + pytree_node=False + ) + + +class BasePotential(abc.ABC, nn.Module): + """Base class for the neural solver models.""" + + @property + @abc.abstractmethod + def is_potential(self) -> bool: + """Indicates if the module implements a potential value or a vector field. + + Returns: + ``True`` if the module defines a potential, ``False`` if it defines a + vector field. + """ + + def potential_value_fn( + self, + params: frozen_dict.FrozenDict[str, jnp.ndarray], + other_potential_value_fn: Optional[PotentialValueFn_t] = None, + ) -> PotentialValueFn_t: + r"""Return a function giving the value of the potential. + + Applies the module if :attr:`is_potential` is ``True``, otherwise + constructs the value of the potential from the gradient with + + .. math:: + + g(y) = -f(\nabla_y g(y)) + y^T \nabla_y g(y) + + where :math:`\nabla_y g(y)` is detached for the envelope theorem + :cite:`danskin:67,bertsekas:71` + to give the appropriate first derivatives of this construction. + + Args: + params: parameters of the module + other_potential_value_fn: function giving the value of the other + potential. Only needed when :attr:`is_potential` is ``False``. + + Returns: + A function that can be evaluated to obtain a potential value, or a linear + interpolation of a potential. + """ + if self.is_potential: + return lambda x: self.apply({"params": params}, x) + + assert other_potential_value_fn is not None, \ + "The value of the gradient-based potential depends " \ + "on the value of the other potential." + + def value_fn(x: jnp.ndarray) -> jnp.ndarray: + squeeze = x.ndim == 1 + if squeeze: + x = jnp.expand_dims(x, 0) + grad_g_x = jax.lax.stop_gradient(self.apply({"params": params}, x)) + value = -other_potential_value_fn(grad_g_x) + \ + jax.vmap(jnp.dot)(grad_g_x, x) + return value.squeeze(0) if squeeze else value + + return value_fn + + def potential_gradient_fn( + self, + params: frozen_dict.FrozenDict[str, jnp.ndarray], + ) -> PotentialGradientFn_t: + """Return a function returning a vector or the gradient of the potential. + + Args: + params: parameters of the module + + Returns: + A function that can be evaluated to obtain the potential's gradient + """ + if self.is_potential: + return jax.vmap(jax.grad(self.potential_value_fn(params))) + return lambda x: self.apply({"params": params}, x) + + def create_train_state( + self, + rng: jax.Array, + optimizer: optax.OptState, + input: Union[int, Tuple[int, ...]], + **kwargs: Any, + ) -> PotentialTrainState: + """Create initial training state.""" + params = self.init(rng, jnp.ones(input))["params"] + + return PotentialTrainState.create( + apply_fn=self.apply, + params=params, + tx=optimizer, + potential_value_fn=self.potential_value_fn, + potential_gradient_fn=self.potential_gradient_fn, + **kwargs, + ) + + +class PotentialMLP(BasePotential): + """Potential MLP. + + Args: + dim_hidden: sequence specifying size of hidden dimensions. The output + dimension of the last layer is automatically set to 1 if + :attr:`is_potential` is ``True``, or the dimension of the input otherwise. + is_potential: Model the potential if ``True``, otherwise + model the gradient of the potential. + act_fn: Activation function. + """ + + dim_hidden: Sequence[int] + is_potential: bool = True + act_fn: Callable[[jnp.ndarray], jnp.ndarray] = nn.leaky_relu + + @nn.compact + def __call__(self, x: jnp.ndarray) -> jnp.ndarray: # noqa: D102 + squeeze = x.ndim == 1 + if squeeze: + x = jnp.expand_dims(x, 0) + assert x.ndim == 2, x.ndim + n_input = x.shape[-1] + + z = x + for n_hidden in self.dim_hidden: + Wx = nn.Dense(n_hidden, use_bias=True) + z = self.act_fn(Wx(z)) + + if self.is_potential: + Wx = nn.Dense(1, use_bias=True) + z = Wx(z).squeeze(-1) + + quad_term = 0.5 * jax.vmap(jnp.dot)(x, x) + z += quad_term + else: + Wx = nn.Dense(n_input, use_bias=True) + z = x + Wx(z) + + return z.squeeze(0) if squeeze else z diff --git a/src/ott/neural/networks/velocity_field.py b/src/ott/neural/networks/velocity_field.py new file mode 100644 index 000000000..39c7d98da --- /dev/null +++ b/src/ott/neural/networks/velocity_field.py @@ -0,0 +1,124 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Callable, Optional, Sequence + +import jax +import jax.numpy as jnp + +import optax +from flax import linen as nn +from flax.training import train_state + +from ott.neural.networks.layers import time_encoder + +__all__ = ["VelocityField"] + + +class VelocityField(nn.Module): + r"""Neural vector field. + + This class learns a map :math:`v: \mathbb{R}\times \mathbb{R}^d + \rightarrow \mathbb{R}^d` solving the ODE :math:`\frac{dx}{dt} = v(t, x)`. + Given a source distribution at time :math:`t_0`, the velocity field can be + used to transport the source distribution given at :math:`t_0` to + a target distribution given at :math:`t_1` by integrating :math:`v(t, x)` + from :math:`t=t_0` to :math:`t=t_1`. + + Args: + hidden_dims: Dimensionality of the embedding of the data. + output_dims: Dimensionality of the embedding of the output. + condition_dims: Dimensionality of the embedding of the condition. + If :obj:`None`, the velocity field has no conditions. + time_dims: Dimensionality of the time embedding. + If :obj:`None`, ``hidden_dims`` is used. + time_encoder: Time encoder for the velocity field. + act_fn: Activation function. + """ + hidden_dims: Sequence[int] + output_dims: Sequence[int] + condition_dims: Optional[Sequence[int]] = None + time_dims: Optional[Sequence[int]] = None + time_encoder: Callable[[jnp.ndarray], + jnp.ndarray] = time_encoder.cyclical_time_encoder + act_fn: Callable[[jnp.ndarray], jnp.ndarray] = nn.silu + + @nn.compact + def __call__( + self, + t: jnp.ndarray, + x: jnp.ndarray, + condition: Optional[jnp.ndarray] = None, + ) -> jnp.ndarray: + """Forward pass through the neural vector field. + + Args: + t: Time of shape ``[batch, 1]``. + x: Data of shape ``[batch, ...]``. + condition: Conditioning vector of shape ``[batch, ...]``. + + Returns: + Output of the neural vector field of shape ``[batch, output_dim]``. + """ + time_dims = self.hidden_dims if self.time_dims is None else self.time_dims + + t = self.time_encoder(t) + for time_dim in time_dims: + t = self.act_fn(nn.Dense(time_dim)(t)) + + for hidden_dim in self.hidden_dims: + x = self.act_fn(nn.Dense(hidden_dim)(x)) + + if self.condition_dims is not None: + assert condition is not None, "No condition was passed." + for cond_dim in self.condition_dims: + condition = self.act_fn(nn.Dense(cond_dim)(condition)) + feats = jnp.concatenate([t, x, condition], axis=-1) + else: + feats = jnp.concatenate([t, x], axis=-1) + + for output_dim in self.output_dims[:-1]: + feats = self.act_fn(nn.Dense(output_dim)(feats)) + + # no activation function for the final layer + return nn.Dense(self.output_dims[-1])(feats) + + def create_train_state( + self, + rng: jax.Array, + optimizer: optax.OptState, + input_dim: int, + condition_dim: Optional[int] = None, + ) -> train_state.TrainState: + """Create the training state. + + Args: + rng: Random number generator. + optimizer: Optimizer. + input_dim: Dimensionality of the velocity field. + condition_dim: Dimensionality of the condition of the velocity field. + + Returns: + The training state. + """ + t, x = jnp.ones((1, 1)), jnp.ones((1, input_dim)) + if self.condition_dims is None: + cond = None + else: + assert condition_dim > 0, "Condition dimension must be positive." + cond = jnp.ones((1, condition_dim)) + + params = self.init(rng, t, x, cond)["params"] + return train_state.TrainState.create( + apply_fn=self.apply, params=params, tx=optimizer + ) diff --git a/src/ott/problems/linear/potentials.py b/src/ott/problems/linear/potentials.py index b08939d9b..c142efde6 100644 --- a/src/ott/problems/linear/potentials.py +++ b/src/ott/problems/linear/potentials.py @@ -11,15 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from typing import ( - Any, - Callable, - Dict, - Literal, - Optional, - Sequence, - Tuple, -) +from typing import Any, Callable, Dict, Literal, Optional, Sequence, Tuple import jax import jax.numpy as jnp diff --git a/src/ott/solvers/__init__.py b/src/ott/solvers/__init__.py index 1303312f9..283fca465 100644 --- a/src/ott/solvers/__init__.py +++ b/src/ott/solvers/__init__.py @@ -11,4 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import linear, quadratic, was_solver +from . import linear, quadratic, utils, was_solver diff --git a/src/ott/solvers/linear/lineax_implicit.py b/src/ott/solvers/linear/lineax_implicit.py index 79b9e7c95..30200b073 100644 --- a/src/ott/solvers/linear/lineax_implicit.py +++ b/src/ott/solvers/linear/lineax_implicit.py @@ -14,11 +14,12 @@ from typing import Any, Callable, Optional, TypeVar import equinox as eqx +import lineax as lx +from jaxtyping import Array, Float, PyTree + import jax import jax.numpy as jnp import jax.tree_util as jtu -import lineax as lx -from jaxtyping import Array, Float, PyTree _T = TypeVar("_T") _FlatPyTree = tuple[list[_T], jtu.PyTreeDef] diff --git a/src/ott/solvers/utils.py b/src/ott/solvers/utils.py new file mode 100644 index 000000000..f7bdae63a --- /dev/null +++ b/src/ott/solvers/utils.py @@ -0,0 +1,182 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Literal, Optional, Tuple, Union + +import jax +import jax.numpy as jnp + +from ott.geometry import costs, pointcloud +from ott.solvers import linear, quadratic + +__all__ = [ + "match_linear", + "match_quadratic", + "sample_joint", + "sample_conditional", + "uniform_sampler", +] + +ScaleCost_t = Union[float, Literal["mean", "max_cost", "median"]] + + +def match_linear( + x: jnp.ndarray, + y: Optional[jnp.ndarray], + cost_fn: Optional[costs.CostFn] = None, + epsilon: Optional[float] = None, + scale_cost: ScaleCost_t = 1.0, + **kwargs: Any +) -> jnp.ndarray: + """Compute solution to a linear OT problem. + + Args: + x: Source point cloud of shape ``[n, d]``. + y: Target point cloud of shape ``[m, d]``. + cost_fn: Cost function. + epsilon: Regularization parameter. + scale_cost: Scaling of the cost matrix. + kwargs: Additional arguments for :func:`ott.solvers.linear.solve`. + + Returns: + Optimal transport matrix. + """ + geom = pointcloud.PointCloud( + x, y, cost_fn=cost_fn, epsilon=epsilon, scale_cost=scale_cost + ) + out = linear.solve(geom, **kwargs) + return out.matrix + + +def match_quadratic( + xx: jnp.ndarray, + yy: jnp.ndarray, + x: Optional[jnp.ndarray] = None, + y: Optional[jnp.ndarray] = None, + scale_cost: ScaleCost_t = 1.0, + cost_fn: Optional[costs.CostFn] = None, + **kwargs: Any +) -> jnp.ndarray: + """Compute solution to a quadratic OT problem. + + Args: + xx: Source point cloud of shape ``[n, d1]``. + yy: Target point cloud of shape ``[m, d2]``. + x: Linear (fused) term of the source point cloud. + y: Linear (fused) term of the target point cloud. + scale_cost: Scaling of the cost matrix. + cost_fn: Cost function. + kwargs: Additional arguments for :func:`ott.solvers.quadratic.solve`. + + Returns: + Optimal transport matrix. + """ + geom_xx = pointcloud.PointCloud(xx, cost_fn=cost_fn, scale_cost=scale_cost) + geom_yy = pointcloud.PointCloud(yy, cost_fn=cost_fn, scale_cost=scale_cost) + if x is None: + geom_xy = None + else: + geom_xy = pointcloud.PointCloud( + x, y, cost_fn=cost_fn, scale_cost=scale_cost + ) + + out = quadratic.solve(geom_xx, geom_yy, geom_xy, **kwargs) + return out.matrix + + +def sample_joint(rng: jax.Array, + tmat: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]: + """Sample jointly from a transport matrix. + + Args: + rng: Random number generator. + tmat: Transport matrix of shape ``[n, m]``. + + Returns: + Source and target indices of shape ``[n,]`` and ``[m,]``, respectively. + """ + n, m = tmat.shape + tmat_flattened = tmat.flatten() + indices = jax.random.choice( + rng, len(tmat_flattened), p=tmat_flattened, shape=[n] + ) + src_ixs = indices // m + tgt_ixs = indices % m + return src_ixs, tgt_ixs + + +def sample_conditional( + rng: jax.Array, + tmat: jnp.ndarray, + *, + k: int = 1, +) -> Tuple[jnp.ndarray, jnp.ndarray]: + """Sample conditionally from a transport matrix. + + Args: + rng: Random number generator. + tmat: Transport matrix of shape ``[n, m]``. + k: Expected number of samples to sample per source sample. + + Returns: + Source and target indices of shape ``[n, k]`` and ``[m, k]``, respectively. + """ + assert k > 0, "Number of samples per source must be positive." + n, m = tmat.shape + + src_marginals = tmat.sum(axis=1) + rng, rng_ixs = jax.random.split(rng, 2) + indices = jax.random.choice(rng_ixs, a=n, p=src_marginals, shape=(n,)) + tmat = tmat[indices] + + rngs = jax.random.split(rng, n) + tgt_ixs = jax.vmap( + lambda rng, row: jax.random.choice(rng, a=m, p=row, shape=(k,)), + in_axes=[0, 0], + )(rngs, tmat) # (m, k) + + src_ixs = jnp.repeat(indices[:, None], k, axis=1) # (n, k) + return src_ixs, tgt_ixs + + +def uniform_sampler( + rng: jax.Array, + num_samples: int, + low: float = 0.0, + high: float = 1.0, + offset: Optional[float] = None +) -> jnp.ndarray: + r"""Sample from a uniform distribution. + + Sample :math:`t` from a uniform distribution :math:`[low, high]`. + If `offset` is not :obj:`None`, one element :math:`t` is sampled from + :math:`[low, high]` and the K samples are constructed via + :math:`(t + k)/K \mod (high - low - offset) + low`. + + Args: + rng: Random number generator. + num_samples: Number of samples to generate. + low: Lower bound of the uniform distribution. + high: Upper bound of the uniform distribution. + offset: Offset of the uniform distribution. + If :obj:`None`, no offset is used. + + Returns: + An array of shape ``[num_samples, 1]``. + """ + if offset is None: + return jax.random.uniform(rng, (num_samples, 1), minval=low, maxval=high) + + t = jax.random.uniform(rng, (1, 1), minval=low, maxval=high) + mod_term = ((high - low) - offset) + return (t + jnp.arange(num_samples)[:, None] / num_samples) % mod_term diff --git a/src/ott/tools/soft_sort.py b/src/ott/tools/soft_sort.py index faf52f2b2..edd784be9 100644 --- a/src/ott/tools/soft_sort.py +++ b/src/ott/tools/soft_sort.py @@ -459,7 +459,7 @@ def _quantile( def multivariate_cdf_quantile_maps( inputs: jnp.ndarray, target_sampler: Optional[Callable[[jax.Array, Tuple[int, int]], - jnp.ndarray]] = None, + jax.Array]] = None, rng: Optional[jax.Array] = None, num_target_samples: Optional[int] = None, cost_fn: Optional[costs.CostFn] = None, diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/conftest.py b/tests/conftest.py index bc4570343..8fe7166aa 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -15,11 +15,12 @@ import itertools from typing import Any, Mapping, Optional, Sequence +import pytest +from _pytest.python import Metafunc + import jax import jax.experimental import jax.numpy as jnp -import pytest -from _pytest.python import Metafunc def pytest_generate_tests(metafunc: Metafunc) -> None: diff --git a/tests/geometry/costs_test.py b/tests/geometry/costs_test.py index 0e9c8342e..71b826de6 100644 --- a/tests/geometry/costs_test.py +++ b/tests/geometry/costs_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Type +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, pointcloud from ott.solvers import linear diff --git a/tests/geometry/geodesic_test.py b/tests/geometry/geodesic_test.py index 97867a78d..4cf7aa44b 100644 --- a/tests/geometry/geodesic_test.py +++ b/tests/geometry/geodesic_test.py @@ -13,14 +13,17 @@ # limitations under the License. from typing import Optional, Union +import networkx as nx +from networkx.algorithms import shortest_paths +from networkx.generators import balanced_tree, random_graphs + +import pytest + import jax import jax.experimental.sparse as jesp import jax.numpy as jnp -import networkx as nx import numpy as np -import pytest -from networkx.algorithms import shortest_paths -from networkx.generators import balanced_tree, random_graphs + from ott.geometry import geodesic, geometry, graph from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn diff --git a/tests/geometry/graph_test.py b/tests/geometry/graph_test.py index e18d39e44..14485c3b6 100644 --- a/tests/geometry/graph_test.py +++ b/tests/geometry/graph_test.py @@ -13,14 +13,17 @@ # limitations under the License. from typing import Literal, Optional, Tuple, Union -import jax -import jax.numpy as jnp import networkx as nx -import numpy as np -import pytest -from jax.experimental import sparse from networkx.algorithms import shortest_paths from networkx.generators import balanced_tree, random_graphs + +import pytest + +import jax +import jax.experimental.sparse as jesp +import jax.numpy as jnp +import numpy as np + from ott.geometry import geometry, graph from ott.problems.linear import linear_problem from ott.solvers.linear import implicit_differentiation as implicit_lib @@ -256,7 +259,7 @@ def callback( data: jnp.ndarray, rows: jnp.ndarray, cols: jnp.ndarray, shape: Tuple[int, int] ) -> float: - G = sparse.BCOO((data, jnp.c_[rows, cols]), shape=shape).todense() + G = jesp.BCOO((data, jnp.c_[rows, cols]), shape=shape).todense() geom = graph.Graph.from_graph(G, t=1.0) solver = sinkhorn.Sinkhorn(lse_mode=False, **kwargs) @@ -271,7 +274,7 @@ def callback( eps = 1e-3 G = random_graph(20, p=0.5) - G = sparse.BCOO.fromdense(G) + G = jesp.BCOO.fromdense(G) w, rows, cols = G.data, G.indices[:, 0], G.indices[:, 1] v_w = jax.random.normal(rng, shape=w.shape) diff --git a/tests/geometry/lr_cost_test.py b/tests/geometry/lr_cost_test.py index 7c40bdfe7..7b495a49f 100644 --- a/tests/geometry/lr_cost_test.py +++ b/tests/geometry/lr_cost_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Callable, Optional, Tuple, Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, geometry, grid, low_rank, pointcloud diff --git a/tests/geometry/lr_kernel_test.py b/tests/geometry/lr_kernel_test.py index 1f0a42e7d..6db247179 100644 --- a/tests/geometry/lr_kernel_test.py +++ b/tests/geometry/lr_kernel_test.py @@ -1,9 +1,11 @@ from typing import Literal, Optional +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, low_rank, pointcloud from ott.solvers import linear diff --git a/tests/geometry/pointcloud_test.py b/tests/geometry/pointcloud_test.py index d8c05077e..197284f68 100644 --- a/tests/geometry/pointcloud_test.py +++ b/tests/geometry/pointcloud_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, geometry, pointcloud diff --git a/tests/geometry/scaling_cost_test.py b/tests/geometry/scaling_cost_test.py index 35240f8b7..4f58c66ea 100644 --- a/tests/geometry/scaling_cost_test.py +++ b/tests/geometry/scaling_cost_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Optional, Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import geometry, low_rank, pointcloud from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn, sinkhorn_lr diff --git a/tests/geometry/subsetting_test.py b/tests/geometry/subsetting_test.py index d7c714f2e..360b830f9 100644 --- a/tests/geometry/subsetting_test.py +++ b/tests/geometry/subsetting_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Optional, Sequence, Tuple, Type, Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import geometry, low_rank, pointcloud Geom_t = Union[pointcloud.PointCloud, geometry.Geometry, low_rank.LRCGeometry] diff --git a/tests/initializers/linear/sinkhorn_init_test.py b/tests/initializers/linear/sinkhorn_init_test.py index 88308a0d7..e8e163709 100644 --- a/tests/initializers/linear/sinkhorn_init_test.py +++ b/tests/initializers/linear/sinkhorn_init_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Literal, Optional +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import geometry, pointcloud from ott.initializers.linear import initializers as linear_init from ott.problems.linear import linear_problem diff --git a/tests/initializers/linear/sinkhorn_lr_init_test.py b/tests/initializers/linear/sinkhorn_lr_init_test.py index 3c89fd137..3c6e50c86 100644 --- a/tests/initializers/linear/sinkhorn_lr_init_test.py +++ b/tests/initializers/linear/sinkhorn_lr_init_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import geometry, pointcloud from ott.initializers.linear import initializers_lr from ott.problems.linear import linear_problem diff --git a/tests/initializers/neural/__init__.py b/tests/initializers/neural/__init__.py new file mode 100644 index 000000000..8c23e4ba8 --- /dev/null +++ b/tests/initializers/neural/__init__.py @@ -0,0 +1,16 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest + +_ = pytest.importorskip("ott.initializers.neural") diff --git a/tests/neural/meta_initializer_test.py b/tests/initializers/neural/meta_initializer_test.py similarity index 96% rename from tests/neural/meta_initializer_test.py rename to tests/initializers/neural/meta_initializer_test.py index ec8340741..3e04556f9 100644 --- a/tests/neural/meta_initializer_test.py +++ b/tests/initializers/neural/meta_initializer_test.py @@ -13,13 +13,16 @@ # limitations under the License. from typing import Optional +import pytest + import jax import jax.numpy as jnp -import pytest + from flax import linen as nn + from ott.geometry import pointcloud from ott.initializers.linear import initializers as linear_init -from ott.neural import models as nn_init +from ott.initializers.neural import meta_initializer as meta_init from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn @@ -106,7 +109,7 @@ def test_meta_initializer(self, rng: jax.Array, lse_mode: bool): # overfit the initializer to the problem. meta_model = MetaMLP(n) - meta_initializer = nn_init.MetaInitializer(geom, meta_model) + meta_initializer = meta_init.MetaInitializer(geom, meta_model) for _ in range(50): _, _, meta_initializer.state = meta_initializer.update( meta_initializer.state, a=a, b=b diff --git a/tests/initializers/quadratic/gw_init_test.py b/tests/initializers/quadratic/gw_init_test.py index 09346d2ac..43f9dd4b4 100644 --- a/tests/initializers/quadratic/gw_init_test.py +++ b/tests/initializers/quadratic/gw_init_test.py @@ -11,9 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import numpy as np -import pytest + from ott.geometry import pointcloud from ott.initializers.linear import initializers as lin_init from ott.initializers.linear import initializers_lr diff --git a/tests/math/lse_test.py b/tests/math/lse_test.py index 9f22e4d9f..e3790bbb6 100644 --- a/tests/math/lse_test.py +++ b/tests/math/lse_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.math import utils as mu diff --git a/tests/math/math_utils_test.py b/tests/math/math_utils_test.py index 5a5e3a69a..a3afb0dca 100644 --- a/tests/math/math_utils_test.py +++ b/tests/math/math_utils_test.py @@ -13,10 +13,12 @@ # limitations under the License. import functools +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.math import utils as mu diff --git a/tests/math/matrix_square_root_test.py b/tests/math/matrix_square_root_test.py index 3a6c71637..8a4f2b282 100644 --- a/tests/math/matrix_square_root_test.py +++ b/tests/math/matrix_square_root_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Any, Callable +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.math import matrix_square_root diff --git a/tests/neural/__init__.py b/tests/neural/__init__.py index f642d8b21..278074b14 100644 --- a/tests/neural/__init__.py +++ b/tests/neural/__init__.py @@ -1,3 +1,16 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import pytest -_ = pytest.importorskip("flax") +_ = pytest.importorskip("ott.neural") diff --git a/tests/neural/conftest.py b/tests/neural/conftest.py new file mode 100644 index 000000000..41b5ea71a --- /dev/null +++ b/tests/neural/conftest.py @@ -0,0 +1,197 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections import defaultdict +from typing import Dict, NamedTuple, Optional, Union + +import pytest + +import jax.numpy as jnp +import numpy as np + +from ott.neural import datasets + + +class SimpleDataLoader: + + def __init__( + self, + dataset: datasets.OTDataset, + batch_size: int, + seed: Optional[int] = None + ): + self.dataset = dataset + self.batch_size = batch_size + self.seed = seed + + def __iter__(self): + self._rng = np.random.default_rng(self.seed) + return self + + def __next__(self) -> Dict[str, jnp.ndarray]: + data = defaultdict(list) + for _ in range(self.batch_size): + ix = self._rng.integers(0, len(self.dataset)) + for k, v in self.dataset[ix].items(): + data[k].append(v) + + return {k: jnp.vstack(v) for k, v in data.items()} + + +class OTLoader(NamedTuple): + loader: SimpleDataLoader + lin_dim: int = 0 + quad_src_dim: int = 0 + quad_tgt_dim: int = 0 + cond_dim: Optional[int] = None + + +def _ot_data( + rng: np.random.Generator, + *, + n: int = 100, + lin_dim: Optional[int] = None, + quad_dim: Optional[int] = None, + condition: Optional[Union[float, np.ndarray]] = None, + cond_dim: Optional[int] = None, + offset: float = 0.0 +) -> datasets.OTData: + assert lin_dim or quad_dim, \ + "Either linear or quadratic dimension has to be specified." + + lin_data = None if lin_dim is None else ( + rng.normal(size=(n, lin_dim)) + offset + ) + quad_data = None if quad_dim is None else ( + rng.normal(size=(n, quad_dim)) + offset + ) + + if isinstance(condition, float): + _dim = lin_dim if lin_dim is not None else quad_dim + cond_dim = _dim if cond_dim is None else cond_dim + condition = np.full((n, cond_dim), fill_value=condition) + + return datasets.OTData(lin=lin_data, quad=quad_data, condition=condition) + + +@pytest.fixture() +def lin_dl() -> OTLoader: + n, d = 128, 2 + rng = np.random.default_rng(0) + + src = _ot_data(rng, n=n, lin_dim=d) + tgt = _ot_data(rng, n=n, lin_dim=d, offset=1.0) + ds = datasets.OTDataset(src, tgt) + + return OTLoader( + SimpleDataLoader(ds, batch_size=13), + lin_dim=d, + ) + + +@pytest.fixture() +def lin_cond_dl() -> OTLoader: + n, d, cond_dim = 128, 2, 3 + rng = np.random.default_rng(13) + + src_cond = rng.normal(size=(n, cond_dim)) + tgt_cond = rng.normal(size=(n, cond_dim)) + src = _ot_data(rng, n=n, lin_dim=d, condition=src_cond) + tgt = _ot_data(rng, n=n, lin_dim=d, condition=tgt_cond) + + ds = datasets.OTDataset(src, tgt) + return OTLoader( + SimpleDataLoader(ds, batch_size=14), + lin_dim=d, + cond_dim=cond_dim, + ) + + +@pytest.fixture() +def quad_dl() -> OTLoader: + n, quad_src_dim, quad_tgt_dim = 128, 2, 4 + rng = np.random.default_rng(11) + + src = _ot_data(rng, n=n, quad_dim=quad_src_dim) + tgt = _ot_data(rng, n=n, quad_dim=quad_tgt_dim, offset=1.0) + ds = datasets.OTDataset(src, tgt) + + return OTLoader( + SimpleDataLoader(ds, batch_size=15), + quad_src_dim=quad_src_dim, + quad_tgt_dim=quad_tgt_dim, + ) + + +@pytest.fixture() +def quad_cond_dl() -> OTLoader: + n, quad_src_dim, quad_tgt_dim, cond_dim = 128, 2, 4, 5 + rng = np.random.default_rng(414) + + src_cond = rng.normal(size=(n, cond_dim)) + tgt_cond = rng.normal(size=(n, cond_dim)) + src = _ot_data(rng, n=n, quad_dim=quad_src_dim, condition=src_cond) + tgt = _ot_data(rng, n=n, quad_dim=quad_tgt_dim, offset=1.0, cond_dim=tgt_cond) + ds = datasets.OTDataset(src, tgt) + + return OTLoader( + SimpleDataLoader(ds, batch_size=16), + quad_src_dim=quad_src_dim, + quad_tgt_dim=quad_tgt_dim, + cond_dim=cond_dim, + ) + + +@pytest.fixture() +def fused_dl() -> OTLoader: + n, lin_dim, quad_src_dim, quad_tgt_dim = 128, 6, 2, 4 + rng = np.random.default_rng(11) + + src = _ot_data(rng, n=n, lin_dim=lin_dim, quad_dim=quad_src_dim) + tgt = _ot_data(rng, n=n, lin_dim=lin_dim, quad_dim=quad_tgt_dim, offset=1.0) + ds = datasets.OTDataset(src, tgt) + + return OTLoader( + SimpleDataLoader(ds, batch_size=17), + lin_dim=lin_dim, + quad_src_dim=quad_src_dim, + quad_tgt_dim=quad_tgt_dim, + ) + + +@pytest.fixture() +def fused_cond_dl() -> OTLoader: + n, lin_dim, quad_src_dim, quad_tgt_dim, cond_dim = 128, 6, 2, 4, 7 + rng = np.random.default_rng(11) + + src_cond = rng.normal(size=(n, cond_dim)) + tgt_cond = rng.normal(size=(n, cond_dim)) + src = _ot_data( + rng, n=n, lin_dim=lin_dim, quad_dim=quad_src_dim, condition=src_cond + ) + tgt = _ot_data( + rng, + n=n, + lin_dim=lin_dim, + quad_dim=quad_tgt_dim, + offset=1.0, + condition=tgt_cond + ) + ds = datasets.OTDataset(src, tgt) + + return OTLoader( + SimpleDataLoader(ds, batch_size=18), + lin_dim=lin_dim, + quad_src_dim=quad_src_dim, + quad_tgt_dim=quad_tgt_dim, + ) diff --git a/tests/neural/map_estimator_test.py b/tests/neural/map_estimator_test.py deleted file mode 100644 index f3bddae4b..000000000 --- a/tests/neural/map_estimator_test.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright OTT-JAX -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import Optional - -import jax.numpy as jnp -import pytest -from ott import datasets -from ott.geometry import pointcloud -from ott.neural import losses, models -from ott.neural.solvers import map_estimator -from ott.tools import sinkhorn_divergence - - -@pytest.mark.fast() -class TestMapEstimator: - - def test_map_estimator_convergence(self): - """Tests convergence of a simple - map estimator with Sinkhorn divergence fitting loss - and Monge (coupling) gap regularizer. - """ - - # define the fitting loss and the regularizer - def fitting_loss( - samples: jnp.ndarray, - mapped_samples: jnp.ndarray, - ) -> Optional[float]: - r"""Sinkhorn divergence fitting loss.""" - div = sinkhorn_divergence.sinkhorn_divergence( - pointcloud.PointCloud, - x=samples, - y=mapped_samples, - ).divergence - return (div, None) - - def regularizer(x, y): - gap, out = losses.monge_gap_from_samples(x, y, return_output=True) - return gap, out.n_iters - - # define the model - model = models.MLP(dim_hidden=[16, 8], is_potential=False) - - # generate data - train_dataset, valid_dataset, dim_data = ( - datasets.create_gaussian_mixture_samplers( - name_source="simple", - name_target="circle", - train_batch_size=30, - valid_batch_size=30, - ) - ) - - # fit the map - solver = map_estimator.MapEstimator( - dim_data=dim_data, - fitting_loss=fitting_loss, - regularizer=regularizer, - model=model, - regularizer_strength=1.0, - num_train_iters=15, - logging=True, - valid_freq=5, - ) - neural_state, logs = solver.train_map_estimator( - *train_dataset, *valid_dataset - ) - - # check if the loss has decreased during training - assert logs["train"]["total_loss"][0] > logs["train"]["total_loss"][-1] - - # check dimensionality of the mapped source - source = next(train_dataset.source_iter) - mapped_source = neural_state.apply_fn({"params": neural_state.params}, - source) - assert mapped_source.shape[1] == dim_data diff --git a/tests/neural/methods/genot_test.py b/tests/neural/methods/genot_test.py new file mode 100644 index 000000000..2c746596c --- /dev/null +++ b/tests/neural/methods/genot_test.py @@ -0,0 +1,92 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import functools +from typing import Literal, Optional + +import pytest + +import jax +import jax.numpy as jnp +import jax.tree_util as jtu +import numpy as np + +import optax + +from ott.neural.methods.flows import dynamics, genot +from ott.neural.networks import velocity_field +from ott.solvers import utils as solver_utils + + +def data_match_fn( + src_lin: Optional[jnp.ndarray], tgt_lin: Optional[jnp.ndarray], + src_quad: Optional[jnp.ndarray], tgt_quad: Optional[jnp.ndarray], *, + typ: Literal["lin", "quad", "fused"] +) -> jnp.ndarray: + if typ == "lin": + return solver_utils.match_linear(x=src_lin, y=tgt_lin) + if typ == "quad": + return solver_utils.match_quadratic(xx=src_quad, yy=tgt_quad) + if typ == "fused": + return solver_utils.match_quadratic( + xx=src_quad, yy=tgt_quad, x=src_lin, y=tgt_lin + ) + raise NotImplementedError(f"Unknown type: {typ}.") + + +class TestGENOT: + + @pytest.mark.parametrize( + "dl", [ + "lin_dl", "quad_dl", "fused_dl", "lin_cond_dl", "quad_cond_dl", + "fused_cond_dl" + ] + ) + def test_genot(self, rng: jax.Array, dl: str, request): + rng_init, rng_call, rng_data = jax.random.split(rng, 3) + problem_type = dl.split("_")[0] + dl = request.getfixturevalue(dl) + + src_dim = dl.lin_dim + dl.quad_src_dim + tgt_dim = dl.lin_dim + dl.quad_tgt_dim + cond_dim = dl.cond_dim + + vf = velocity_field.VelocityField( + hidden_dims=[7, 7, 7], + output_dims=[15, tgt_dim], + condition_dims=None if cond_dim is None else [1, 3, 2], + ) + model = genot.GENOT( + vf, + flow=dynamics.ConstantNoiseFlow(0.0), + data_match_fn=functools.partial(data_match_fn, typ=problem_type), + source_dim=src_dim, + target_dim=tgt_dim, + condition_dim=cond_dim, + rng=rng_init, + optimizer=optax.adam(learning_rate=1e-4), + ) + + _logs = model(dl.loader, n_iters=2, rng=rng_call) + + batch = next(iter(dl.loader)) + batch = jtu.tree_map(jnp.asarray, batch) + src_cond = batch.get("src_condition") + batch_size = 4 if src_cond is None else src_cond.shape[0] + src = jax.random.normal(rng_data, (batch_size, src_dim)) + + res = model.transport(src, condition=src_cond) + + assert len(_logs["loss"]) == 2 + np.testing.assert_array_equal(jnp.isfinite(res), True) + assert res.shape == (batch_size, tgt_dim) diff --git a/tests/neural/losses_test.py b/tests/neural/methods/monge_gap_test.py similarity index 58% rename from tests/neural/losses_test.py rename to tests/neural/methods/monge_gap_test.py index 31b5f417b..68d885537 100644 --- a/tests/neural/losses_test.py +++ b/tests/neural/methods/monge_gap_test.py @@ -11,12 +11,19 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from typing import Optional + +import pytest import jax +import jax.numpy as jnp import numpy as np -import pytest -from ott.geometry import costs -from ott.neural import losses, models + +from ott import datasets +from ott.geometry import costs, pointcloud +from ott.neural.methods import monge_gap +from ott.neural.networks import potentials +from ott.tools import sinkhorn_divergence @pytest.mark.fast() @@ -32,18 +39,18 @@ def test_monge_gap_non_negativity( rng1, rng2 = jax.random.split(rng, 2) reference_points = jax.random.normal(rng1, (n_samples, n_features)) - model = models.MLP(dim_hidden=[8, 8], is_potential=False) + model = potentials.PotentialMLP(dim_hidden=[8, 8], is_potential=False) params = model.init(rng2, x=reference_points[0]) target = model.apply(params, reference_points) # compute the Monge gap based on samples - monge_gap_from_samples_value = losses.monge_gap_from_samples( + monge_gap_from_samples_value = monge_gap.monge_gap_from_samples( source=reference_points, target=target ) np.testing.assert_array_equal(monge_gap_from_samples_value >= 0, True) # Compute the Monge gap using model directly - monge_gap_value = losses.monge_gap( + monge_gap_value = monge_gap.monge_gap( map_fn=lambda x: model.apply(params, x), reference_points=reference_points ) @@ -58,10 +65,10 @@ def test_monge_gap_jit(self, rng: jax.Array): source = jax.random.normal(rng1, (n_samples, n_features)) target = jax.random.normal(rng2, (n_samples, n_features)) # define jitted monge gap - jit_monge_gap = jax.jit(losses.monge_gap_from_samples) + jit_monge_gap = jax.jit(monge_gap.monge_gap_from_samples) # compute the Monge gaps for different costs - monge_gap_value = losses.monge_gap_from_samples( + monge_gap_value = monge_gap.monge_gap_from_samples( source=source, target=target ) jit_monge_gap_value = jit_monge_gap(source, target) @@ -99,10 +106,10 @@ def test_monge_gap_from_samples_different_cost( target = jax.random.normal(rng2, (n_samples, n_features)) * 0.1 + 3.0 # compute the Monge gaps for the euclidean cost - monge_gap_from_samples_value_eucl = losses.monge_gap_from_samples( + monge_gap_from_samples_value_eucl = monge_gap.monge_gap_from_samples( source=source, target=target, cost_fn=costs.Euclidean() ) - monge_gap_from_samples_value_cost_fn = losses.monge_gap_from_samples( + monge_gap_from_samples_value_cost_fn = monge_gap.monge_gap_from_samples( source=source, target=target, cost_fn=cost_fn ) @@ -120,3 +127,67 @@ def test_monge_gap_from_samples_different_cost( np.testing.assert_array_equal( np.isfinite(monge_gap_from_samples_value_cost_fn), True ) + + +@pytest.mark.fast() +class TestMongeGapEstimator: + + def test_map_estimator_convergence(self): + """Tests convergence of a simple + map estimator with Sinkhorn divergence fitting loss + and Monge (coupling) gap regularizer. + """ + + # define the fitting loss and the regularizer + def fitting_loss( + samples: jnp.ndarray, + mapped_samples: jnp.ndarray, + ) -> Optional[float]: + r"""Sinkhorn divergence fitting loss.""" + div = sinkhorn_divergence.sinkhorn_divergence( + pointcloud.PointCloud, + x=samples, + y=mapped_samples, + ).divergence + return div, None + + def regularizer(x, y): + gap, out = monge_gap.monge_gap_from_samples(x, y, return_output=True) + return gap, out.n_iters + + # define the model + model = potentials.PotentialMLP(dim_hidden=[16, 8], is_potential=False) + + # generate data + train_dataset, valid_dataset, dim_data = ( + datasets.create_gaussian_mixture_samplers( + name_source="simple", + name_target="circle", + train_batch_size=30, + valid_batch_size=30, + ) + ) + + # fit the map + solver = monge_gap.MongeGapEstimator( + dim_data=dim_data, + fitting_loss=fitting_loss, + regularizer=regularizer, + model=model, + regularizer_strength=1.0, + num_train_iters=15, + logging=True, + valid_freq=5, + ) + neural_state, logs = solver.train_map_estimator( + *train_dataset, *valid_dataset + ) + + # check if the loss has decreased during training + assert logs["train"]["total_loss"][0] > logs["train"]["total_loss"][-1] + + # check dimensionality of the mapped source + source = next(train_dataset.source_iter) + mapped_source = neural_state.apply_fn({"params": neural_state.params}, + source) + assert mapped_source.shape[1] == dim_data diff --git a/tests/neural/neuraldual_test.py b/tests/neural/methods/neuraldual_test.py similarity index 86% rename from tests/neural/neuraldual_test.py rename to tests/neural/methods/neuraldual_test.py index 1c7e4e88c..b0d847abb 100644 --- a/tests/neural/neuraldual_test.py +++ b/tests/neural/methods/neuraldual_test.py @@ -13,14 +13,17 @@ # limitations under the License. from typing import Optional, Sequence, Tuple +import pytest + import jax import numpy as np -import pytest + from ott import datasets -from ott.neural import models -from ott.neural.solvers import conjugate, neuraldual +from ott.neural.methods import neuraldual +from ott.neural.networks import icnn, potentials +from ott.neural.networks.layers import conjugate -ModelPair_t = Tuple[neuraldual.BaseW2NeuralDual, neuraldual.BaseW2NeuralDual] +ModelPair_t = Tuple[potentials.BasePotential, potentials.BasePotential] DatasetPair_t = Tuple[datasets.Dataset, datasets.Dataset] @@ -36,15 +39,16 @@ def ds(request: Tuple[str, str]) -> DatasetPair_t: def neural_models(request: str) -> ModelPair_t: if request.param == "icnns": return ( - models.ICNN(dim_data=2, - dim_hidden=[32]), models.ICNN(dim_data=2, dim_hidden=[32]) + icnn.ICNN(dim_data=2, + dim_hidden=[32]), icnn.ICNN(dim_data=2, dim_hidden=[32]) ) if request.param == "mlps": - return models.MLP(dim_hidden=[32]), models.MLP(dim_hidden=[32]), + return potentials.PotentialMLP(dim_hidden=[32] + ), potentials.PotentialMLP(dim_hidden=[32]), if request.param == "mlps-grad": return ( - models.MLP(dim_hidden=[32]), - models.MLP(is_potential=False, dim_hidden=[128]) + potentials.PotentialMLP(dim_hidden=[32]), + potentials.PotentialMLP(is_potential=False, dim_hidden=[128]) ) raise ValueError(f"Invalid request: {request.param}") @@ -80,7 +84,7 @@ def decreasing(losses: Sequence[float]) -> bool: train_dataset, valid_dataset = ds if test_gaussian_init: - neural_f = models.ICNN( + neural_f = icnn.ICNN( dim_data=2, dim_hidden=[32], gaussian_map_samples=[ @@ -88,7 +92,7 @@ def decreasing(losses: Sequence[float]) -> bool: next(train_dataset.target_iter) ] ) - neural_g = models.ICNN( + neural_g = icnn.ICNN( dim_data=2, dim_hidden=[32], gaussian_map_samples=[ diff --git a/tests/neural/methods/otfm_test.py b/tests/neural/methods/otfm_test.py new file mode 100644 index 000000000..f1ccae767 --- /dev/null +++ b/tests/neural/methods/otfm_test.py @@ -0,0 +1,63 @@ +# Copyright OTT-JAX +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pytest + +import jax +import jax.numpy as jnp +import jax.tree_util as jtu +import numpy as np + +import optax + +from ott.neural.methods.flows import dynamics, otfm +from ott.neural.networks import velocity_field +from ott.solvers import utils as solver_utils + + +class TestOTFM: + + @pytest.mark.parametrize("dl", ["lin_dl", "lin_cond_dl"]) + def test_otfm(self, rng: jax.Array, dl: str, request): + dl = request.getfixturevalue(dl) + dim, cond_dim = dl.lin_dim, dl.cond_dim + + vf = velocity_field.VelocityField( + hidden_dims=[5, 5, 5], + output_dims=[7, dim], + condition_dims=None if cond_dim is None else [4, 3, 2], + ) + fm = otfm.OTFlowMatching( + vf, + dynamics.ConstantNoiseFlow(0.0), + match_fn=jax.jit(solver_utils.match_linear), + rng=rng, + optimizer=optax.adam(learning_rate=1e-3), + condition_dim=cond_dim, + ) + + _logs = fm(dl.loader, n_iters=3) + + batch = next(iter(dl.loader)) + batch = jtu.tree_map(jnp.asarray, batch) + src_cond = batch.get("src_condition") + + res_fwd = fm.transport(batch["src_lin"], condition=src_cond) + res_bwd = fm.transport(batch["tgt_lin"], t0=1.0, t1=0.0, condition=src_cond) + + assert len(_logs["loss"]) == 3 + + assert res_fwd.shape == batch["src_lin"].shape + assert res_bwd.shape == batch["tgt_lin"].shape + np.testing.assert_array_equal(jnp.isfinite(res_fwd), True) + np.testing.assert_array_equal(jnp.isfinite(res_bwd), True) diff --git a/tests/neural/icnn_test.py b/tests/neural/networks/icnn_test.py similarity index 93% rename from tests/neural/icnn_test.py rename to tests/neural/networks/icnn_test.py index 377214812..b07e4994f 100644 --- a/tests/neural/icnn_test.py +++ b/tests/neural/networks/icnn_test.py @@ -11,11 +11,13 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest -from ott.neural import models + +from ott.neural.networks import icnn @pytest.mark.fast() @@ -27,7 +29,7 @@ def test_icnn_convexity(self, rng: jax.Array): dim_hidden = (64, 64) # define icnn model - model = models.ICNN(n_features, dim_hidden=dim_hidden) + model = icnn.ICNN(n_features, dim_hidden=dim_hidden) # initialize model rng1, rng2 = jax.random.split(rng, 2) @@ -53,7 +55,7 @@ def test_icnn_hessian(self, rng: jax.Array): # define icnn model n_features = 2 dim_hidden = (64, 64) - model = models.ICNN(n_features, dim_hidden=dim_hidden) + model = icnn.ICNN(n_features, dim_hidden=dim_hidden) # initialize model rng1, rng2 = jax.random.split(rng) diff --git a/tests/problems/linear/potentials_test.py b/tests/problems/linear/potentials_test.py index e74f32ef3..eed44365a 100644 --- a/tests/problems/linear/potentials_test.py +++ b/tests/problems/linear/potentials_test.py @@ -12,11 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp -import matplotlib.pyplot as plt import numpy as np -import pytest + +import matplotlib.pyplot as plt + from ott.geometry import costs, pointcloud from ott.problems.linear import linear_problem, potentials from ott.solvers.linear import sinkhorn diff --git a/tests/solvers/linear/continuous_barycenter_test.py b/tests/solvers/linear/continuous_barycenter_test.py index 76dd62b6f..92a0f431e 100644 --- a/tests/solvers/linear/continuous_barycenter_test.py +++ b/tests/solvers/linear/continuous_barycenter_test.py @@ -14,10 +14,12 @@ import functools from typing import Tuple +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, segment from ott.problems.linear import barycenter_problem from ott.solvers.linear import continuous_barycenter as cb diff --git a/tests/solvers/linear/discrete_barycenter_test.py b/tests/solvers/linear/discrete_barycenter_test.py index e89c912ee..8bb5ad98c 100644 --- a/tests/solvers/linear/discrete_barycenter_test.py +++ b/tests/solvers/linear/discrete_barycenter_test.py @@ -11,8 +11,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import jax.numpy as jnp import pytest + +import jax.numpy as jnp + from ott.geometry import grid, pointcloud from ott.problems.linear import barycenter_problem as bp from ott.solvers.linear import discrete_barycenter as db diff --git a/tests/solvers/linear/sinkhorn_diff_test.py b/tests/solvers/linear/sinkhorn_diff_test.py index 4c1404252..17f746f08 100644 --- a/tests/solvers/linear/sinkhorn_diff_test.py +++ b/tests/solvers/linear/sinkhorn_diff_test.py @@ -14,10 +14,12 @@ import functools from typing import Callable, List, Optional, Tuple +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, geometry, grid, pointcloud from ott.problems.linear import linear_problem from ott.solvers.linear import implicit_differentiation as implicit_lib diff --git a/tests/solvers/linear/sinkhorn_grid_test.py b/tests/solvers/linear/sinkhorn_grid_test.py index 925af7278..d73bc124b 100644 --- a/tests/solvers/linear/sinkhorn_grid_test.py +++ b/tests/solvers/linear/sinkhorn_grid_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import grid, pointcloud from ott.problems.linear import linear_problem from ott.solvers import linear diff --git a/tests/solvers/linear/sinkhorn_lr_test.py b/tests/solvers/linear/sinkhorn_lr_test.py index 1d8591d20..e5fc121d7 100644 --- a/tests/solvers/linear/sinkhorn_lr_test.py +++ b/tests/solvers/linear/sinkhorn_lr_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Any, Tuple +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import low_rank, pointcloud from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn_lr diff --git a/tests/solvers/linear/sinkhorn_misc_test.py b/tests/solvers/linear/sinkhorn_misc_test.py index 77bc34766..d2c476d43 100644 --- a/tests/solvers/linear/sinkhorn_misc_test.py +++ b/tests/solvers/linear/sinkhorn_misc_test.py @@ -14,15 +14,19 @@ from typing import Optional import chex + +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, geometry, pointcloud from ott.problems.linear import linear_problem from ott.solvers import linear -from ott.solvers.linear import acceleration, sinkhorn +from ott.solvers.linear import acceleration from ott.solvers.linear import implicit_differentiation as implicit_lib +from ott.solvers.linear import sinkhorn class TestSinkhornAnderson: diff --git a/tests/solvers/linear/sinkhorn_test.py b/tests/solvers/linear/sinkhorn_test.py index 91bb9e2fe..5d3fc7751 100644 --- a/tests/solvers/linear/sinkhorn_test.py +++ b/tests/solvers/linear/sinkhorn_test.py @@ -15,10 +15,12 @@ import sys from typing import Optional, Tuple +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott import utils from ott.geometry import costs, epsilon_scheduler, geometry, grid, pointcloud from ott.problems.linear import linear_problem diff --git a/tests/solvers/linear/univariate_test.py b/tests/solvers/linear/univariate_test.py index 9aa671fba..6e0263611 100644 --- a/tests/solvers/linear/univariate_test.py +++ b/tests/solvers/linear/univariate_test.py @@ -13,11 +13,13 @@ # limitations under the License. import functools +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest import scipy as sp + from ott.geometry import costs, pointcloud from ott.problems.linear import linear_problem from ott.solvers import linear diff --git a/tests/solvers/quadratic/fgw_test.py b/tests/solvers/quadratic/fgw_test.py index 58dbb630d..1e7e7d33a 100644 --- a/tests/solvers/quadratic/fgw_test.py +++ b/tests/solvers/quadratic/fgw_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Literal, Tuple, Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import geometry, low_rank, pointcloud from ott.problems.quadratic import quadratic_problem from ott.solvers.linear import implicit_differentiation as implicit_lib diff --git a/tests/solvers/quadratic/gw_barycenter_test.py b/tests/solvers/quadratic/gw_barycenter_test.py index d07247fef..02ecc953b 100644 --- a/tests/solvers/quadratic/gw_barycenter_test.py +++ b/tests/solvers/quadratic/gw_barycenter_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Any, Optional, Sequence, Tuple +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import pointcloud from ott.problems.quadratic import gw_barycenter as gwb from ott.solvers.quadratic import gw_barycenter as gwb_solver diff --git a/tests/solvers/quadratic/gw_test.py b/tests/solvers/quadratic/gw_test.py index a4f23c6e2..7b4bb7eb4 100644 --- a/tests/solvers/quadratic/gw_test.py +++ b/tests/solvers/quadratic/gw_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Tuple, Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott import utils from ott.geometry import geometry, low_rank, pointcloud from ott.problems.quadratic import quadratic_problem @@ -521,7 +523,7 @@ def callback(x: jnp.ndarray, y: jnp.ndarray): geom_yy = pointcloud.PointCloud(y) prob = quadratic_problem.QuadraticProblem(geom_xx, geom_yy) - lin_solver = sinkhorn.Sinkhorn(progress_fn=utils.default_progress_fn(),) + lin_solver = sinkhorn.Sinkhorn(progress_fn=utils.default_progress_fn()) quad_solver = gromov_wasserstein.GromovWasserstein( linear_ot_solver=lin_solver, progress_fn=utils.default_progress_fn(), diff --git a/tests/solvers/quadratic/lower_bound_test.py b/tests/solvers/quadratic/lower_bound_test.py index 7e8a7a160..d27c040a0 100644 --- a/tests/solvers/quadratic/lower_bound_test.py +++ b/tests/solvers/quadratic/lower_bound_test.py @@ -12,9 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp -import pytest + from ott.geometry import costs, distrib_costs, pointcloud from ott.problems.quadratic import quadratic_problem from ott.solvers.quadratic import lower_bound diff --git a/tests/tools/gaussian_mixture/fit_gmm_pair_test.py b/tests/tools/gaussian_mixture/fit_gmm_pair_test.py index ef4450be8..49ae2ff55 100644 --- a/tests/tools/gaussian_mixture/fit_gmm_pair_test.py +++ b/tests/tools/gaussian_mixture/fit_gmm_pair_test.py @@ -11,9 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp -import pytest + from ott.tools.gaussian_mixture import ( fit_gmm, fit_gmm_pair, diff --git a/tests/tools/gaussian_mixture/fit_gmm_test.py b/tests/tools/gaussian_mixture/fit_gmm_test.py index 3423f2830..9b835af97 100644 --- a/tests/tools/gaussian_mixture/fit_gmm_test.py +++ b/tests/tools/gaussian_mixture/fit_gmm_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import jax.test_util -import pytest + from ott.tools.gaussian_mixture import fit_gmm, gaussian_mixture diff --git a/tests/tools/gaussian_mixture/gaussian_mixture_pair_test.py b/tests/tools/gaussian_mixture/gaussian_mixture_pair_test.py index 8eaa7a08b..93d346495 100644 --- a/tests/tools/gaussian_mixture/gaussian_mixture_pair_test.py +++ b/tests/tools/gaussian_mixture/gaussian_mixture_pair_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.tools.gaussian_mixture import gaussian_mixture, gaussian_mixture_pair diff --git a/tests/tools/gaussian_mixture/gaussian_mixture_test.py b/tests/tools/gaussian_mixture/gaussian_mixture_test.py index b70e65e0e..fa81c723a 100644 --- a/tests/tools/gaussian_mixture/gaussian_mixture_test.py +++ b/tests/tools/gaussian_mixture/gaussian_mixture_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.tools.gaussian_mixture import gaussian_mixture, linalg diff --git a/tests/tools/gaussian_mixture/gaussian_test.py b/tests/tools/gaussian_mixture/gaussian_test.py index 34fd0a44f..9fc4feda0 100644 --- a/tests/tools/gaussian_mixture/gaussian_test.py +++ b/tests/tools/gaussian_mixture/gaussian_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.tools.gaussian_mixture import gaussian, scale_tril diff --git a/tests/tools/gaussian_mixture/linalg_test.py b/tests/tools/gaussian_mixture/linalg_test.py index b764c2a26..651905ea6 100644 --- a/tests/tools/gaussian_mixture/linalg_test.py +++ b/tests/tools/gaussian_mixture/linalg_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.tools.gaussian_mixture import linalg diff --git a/tests/tools/gaussian_mixture/probabilities_test.py b/tests/tools/gaussian_mixture/probabilities_test.py index 1a6e18a1e..ec2c74a56 100644 --- a/tests/tools/gaussian_mixture/probabilities_test.py +++ b/tests/tools/gaussian_mixture/probabilities_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.tools.gaussian_mixture import probabilities diff --git a/tests/tools/gaussian_mixture/scale_tril_test.py b/tests/tools/gaussian_mixture/scale_tril_test.py index facd21b57..3ef487f45 100644 --- a/tests/tools/gaussian_mixture/scale_tril_test.py +++ b/tests/tools/gaussian_mixture/scale_tril_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.math import matrix_square_root from ott.tools.gaussian_mixture import scale_tril diff --git a/tests/tools/k_means_test.py b/tests/tools/k_means_test.py index 967c5e41f..909440461 100644 --- a/tests/tools/k_means_test.py +++ b/tests/tools/k_means_test.py @@ -15,16 +15,18 @@ import sys from typing import Any, Literal, Optional, Tuple, Union +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest -from ott.geometry import costs, pointcloud -from ott.tools import k_means from sklearn import datasets from sklearn.cluster import KMeans, kmeans_plusplus from sklearn.cluster._k_means_common import _is_same_clustering +from ott.geometry import costs, pointcloud +from ott.tools import k_means + def make_blobs( *args: Any, diff --git a/tests/tools/plot_test.py b/tests/tools/plot_test.py index 80e374bb6..2d8ba55ac 100644 --- a/tests/tools/plot_test.py +++ b/tests/tools/plot_test.py @@ -13,15 +13,16 @@ # limitations under the License. import jax + import matplotlib.pyplot as plt -import ott + from ott.geometry import pointcloud from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn from ott.tools import plot -class TestSoftSort: +class TestPlotting: def test_plot(self, monkeypatch): monkeypatch.setattr(plt, "show", lambda: None) @@ -42,5 +43,5 @@ def test_plot(self, monkeypatch): plott = plot.Plot() _ = plott(ots[0]) fig = plt.figure(figsize=(8, 5)) - plott = ott.tools.plot.Plot(fig=fig, title="test") + plott = plot.Plot(fig=fig, title="test") plott.animate(ots, frame_rate=2, titles=["test1", "test2"]) diff --git a/tests/tools/segment_sinkhorn_test.py b/tests/tools/segment_sinkhorn_test.py index 2e56af4c3..53fb4ae85 100644 --- a/tests/tools/segment_sinkhorn_test.py +++ b/tests/tools/segment_sinkhorn_test.py @@ -11,10 +11,12 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, pointcloud from ott.problems.linear import linear_problem from ott.solvers.linear import sinkhorn diff --git a/tests/tools/sinkhorn_divergence_test.py b/tests/tools/sinkhorn_divergence_test.py index 1f0f024cd..de1edb3eb 100644 --- a/tests/tools/sinkhorn_divergence_test.py +++ b/tests/tools/sinkhorn_divergence_test.py @@ -13,10 +13,12 @@ # limitations under the License. from typing import Any, Dict, Optional +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.geometry import costs, geometry, pointcloud from ott.solvers import linear from ott.solvers.linear import acceleration diff --git a/tests/tools/soft_sort_test.py b/tests/tools/soft_sort_test.py index f09ea93a1..9b7b88d76 100644 --- a/tests/tools/soft_sort_test.py +++ b/tests/tools/soft_sort_test.py @@ -14,10 +14,12 @@ import functools from typing import Tuple +import pytest + import jax import jax.numpy as jnp import numpy as np -import pytest + from ott.solvers.linear import acceleration from ott.solvers.linear import implicit_differentiation as implicit_lib from ott.tools import soft_sort diff --git a/tests/utils_test.py b/tests/utils_test.py index 768a498b5..192ed59f4 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -14,6 +14,7 @@ from typing import Optional import pytest + from ott import utils