






from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class 				__UpperCAmelCase		(_UpperCAmelCase   ):







			def __init__(     self:  Union[str, Any]				,     UpperCAmelCase_:  Distribution				,     UpperCAmelCase_:  int=None				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  int=0					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1.0 if scale is None else scale
									_SCREAMING_SNAKE_CASE     				=  0.0 if loc is None else loc

									super().__init__(UpperCAmelCase_				,     [AffineTransform(loc=self.loc				,     scale=self.scale				,     event_dim=UpperCAmelCase_					)]					)







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									return self.base_dist.mean * self.scale + self.loc







			@property
			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									return self.base_dist.variance * self.scale**2







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									return self.variance.sqrt()



class 				__UpperCAmelCase		(nn.Module   ):







			def __init__(     self:  List[Any]				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  Dict[str, int]				,     UpperCAmelCase_:  Callable[..., Tuple[torch.Tensor]]				,     **UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  args_dim
									_SCREAMING_SNAKE_CASE     				=  nn.ModuleList([nn.Linear(UpperCAmelCase_				,     UpperCAmelCase_					) for dim in args_dim.values()]					)
									_SCREAMING_SNAKE_CASE     				=  domain_map







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  torch.Tensor					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  [proj(UpperCAmelCase_					) for proj in self.proj]

									return self.domain_map(*UpperCAmelCase_					)



class 				__UpperCAmelCase		(nn.Module   ):







			def __init__(     self:  Dict				,     UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




									super().__init__()
									_SCREAMING_SNAKE_CASE     				=  function







			def 							UpperCamelCase				(     self:  Union[str, Any]				,     UpperCAmelCase_:  int				,     *UpperCAmelCase_:  str					):







									'''simple docstring'''




									return self.function(UpperCAmelCase_				,     *UpperCAmelCase_					)



class 				__UpperCAmelCase		:
			__snake_case    :				type
			__snake_case    :				int
			__snake_case    :				Dict[str, int]







			def __init__(     self:  int				,     UpperCAmelCase_:  int = 1					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  dim
									_SCREAMING_SNAKE_CASE     				=  {k: dim * self.args_dim[k] for k in self.args_dim}







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									if self.dim == 1:
															return self.distribution_class(*UpperCAmelCase_					)
									else:
															return Independent(self.distribution_class(*UpperCAmelCase_					)				,     1					)







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  Optional[torch.Tensor] = None				,     UpperCAmelCase_:  Optional[torch.Tensor] = None				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self._base_distribution(UpperCAmelCase_					)
									if loc is None and scale is None:
															return distr
									else:
															return AffineTransformed(UpperCAmelCase_				,     loc=UpperCAmelCase_				,     scale=UpperCAmelCase_				,     event_dim=self.event_dim					)







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									return () if self.dim == 1 else (self.dim,)







			@property
			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									return len(self.event_shape					)







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									return 0.0







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									return ParameterProjection(
									    in_features=UpperCAmelCase_				,     args_dim=self.args_dim				,     domain_map=LambdaLayer(self.domain_map					)				,     )







			def 							UpperCamelCase				(     self:  List[str]				,     *UpperCAmelCase_:  torch.Tensor					):







									'''simple docstring'''




									raise NotImplementedError()







			@staticmethod
			def 							UpperCamelCase				(     UpperCAmelCase_:  torch.Tensor					):







									'''simple docstring'''




									return (x + torch.sqrt(torch.square(UpperCAmelCase_					) + 4.0					)) / 2.0



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict[str, int]									=							{"df": 1, "loc": 1, "scale": 1}
			__snake_case    :				type									=							StudentT







			@classmethod
			def 							UpperCamelCase				(     cls:  List[Any]				,     UpperCAmelCase_:  torch.Tensor				,     UpperCAmelCase_:  torch.Tensor				,     UpperCAmelCase_:  torch.Tensor					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  cls.squareplus(UpperCAmelCase_					).clamp_min(torch.finfo(scale.dtype					).eps					)
									_SCREAMING_SNAKE_CASE     				=  2.0 + cls.squareplus(UpperCAmelCase_					)
									return df.squeeze(-1					), loc.squeeze(-1					), scale.squeeze(-1					)



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict[str, int]									=							{"loc": 1, "scale": 1}
			__snake_case    :				type									=							Normal







			@classmethod
			def 							UpperCamelCase				(     cls:  str				,     UpperCAmelCase_:  torch.Tensor				,     UpperCAmelCase_:  torch.Tensor					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  cls.squareplus(UpperCAmelCase_					).clamp_min(torch.finfo(scale.dtype					).eps					)
									return loc.squeeze(-1					), scale.squeeze(-1					)



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict[str, int]									=							{"total_count": 1, "logits": 1}
			__snake_case    :				type									=							NegativeBinomial







			@classmethod
			def 							UpperCamelCase				(     cls:  List[Any]				,     UpperCAmelCase_:  torch.Tensor				,     UpperCAmelCase_:  torch.Tensor					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  cls.squareplus(UpperCAmelCase_					)
									return total_count.squeeze(-1					), logits.squeeze(-1					)







			def 							UpperCamelCase				(     self:  Union[str, Any]				,     UpperCAmelCase_:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  distr_args
									if self.dim == 1:
															return self.distribution_class(total_count=UpperCAmelCase_				,     logits=UpperCAmelCase_					)
									else:
															return Independent(self.distribution_class(total_count=UpperCAmelCase_				,     logits=UpperCAmelCase_					)				,     1					)







			def 							UpperCamelCase				(     self:  Union[str, Any]				,     UpperCAmelCase_:  Any				,     UpperCAmelCase_:  Optional[torch.Tensor] = None				,     UpperCAmelCase_:  Optional[torch.Tensor] = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  distr_args

									if scale is not None:
															# See scaling property of Gamma.
															logits += scale.log()

									return self._base_distribution((total_count, logits)					)









import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
		import os

		from flax.core.frozen_dict import unfreeze
		from flax.traverse_util import flatten_dict

		from transformers import FlaxBertModel

		UpperCamelCase			    =     '''0.12'''  # assumed parallelism: 8



@require_flax
@is_staging_test
class 				__UpperCAmelCase		(unittest.TestCase   ):







			@classmethod
			def 							UpperCamelCase				(     cls:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TOKEN
									HfFolder.save_token(UpperCAmelCase_					)







			@classmethod
			def 							UpperCamelCase				(     cls:  Union[str, Any]					):







									'''simple docstring'''




									try:
															delete_repo(token=cls._token				,     repo_id="""test-model-flax"""					)
									except HTTPError:
															pass

									try:
															delete_repo(token=cls._token				,     repo_id="""valid_org/test-model-flax-org"""					)
									except HTTPError:
															pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    vocab_size=99				,     hidden_size=32				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=37					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)
									model.push_to_hub("""test-model-flax"""				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															_SCREAMING_SNAKE_CASE     				=  (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(UpperCAmelCase_				,     1E-3				,     msg=F'{key} not identical'					)

									# Reset repo
									delete_repo(token=self._token				,     repo_id="""test-model-flax"""					)

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(UpperCAmelCase_				,     repo_id="""test-model-flax"""				,     push_to_hub=UpperCAmelCase_				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															_SCREAMING_SNAKE_CASE     				=  (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(UpperCAmelCase_				,     1E-3				,     msg=F'{key} not identical'					)







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    vocab_size=99				,     hidden_size=32				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=37					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)
									model.push_to_hub("""valid_org/test-model-flax-org"""				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															_SCREAMING_SNAKE_CASE     				=  (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(UpperCAmelCase_				,     1E-3				,     msg=F'{key} not identical'					)

									# Reset repo
									delete_repo(token=self._token				,     repo_id="""valid_org/test-model-flax-org"""					)

									# Push to hub via save_pretrained
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(
															    UpperCAmelCase_				,     repo_id="""valid_org/test-model-flax-org"""				,     push_to_hub=UpperCAmelCase_				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															_SCREAMING_SNAKE_CASE     				=  (base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(UpperCAmelCase_				,     1E-3				,     msg=F'{key} not identical'					)

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  flatten_dict(modela.params		)
						_SCREAMING_SNAKE_CASE     				=  flatten_dict(modela.params		)
						for key in flat_params_a.keys():
												if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]		)		) > 1e-4:
																		_SCREAMING_SNAKE_CASE     				=  False

						return models_are_equal




@require_flax
class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)					)

															with self.assertRaises(UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertTrue(check_models_equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)				,     max_shard_size="""10KB"""					)

															with self.assertRaises(UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertTrue(check_models_equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """bert"""
									_SCREAMING_SNAKE_CASE     				=  """hf-internal-testing/tiny-random-bert-subfolder"""

									with self.assertRaises(UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertIsNotNone(UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """bert"""
									_SCREAMING_SNAKE_CASE     				=  """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
									with self.assertRaises(UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertIsNotNone(UpperCAmelCase_					)

