


"""simple docstring"""




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 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       lowerCAmelCase__=0				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					1.0 if scale is None else scale
								SCREAMING_SNAKE_CASE													=					0.0 if loc is None else loc

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







		@property
		def 							__A						(	self				)     ->							Tuple:
								return self.base_dist.mean * self.scale + self.loc







		@property
		def 							__A						(	self				)     ->							Union[str, Any]:
								return self.base_dist.variance * self.scale**2







		@property
		def 							__A						(	self				)     ->							List[Any]:
								return self.variance.sqrt()





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							None:
								super().__init__(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					args_dim
								SCREAMING_SNAKE_CASE													=					nn.ModuleList([nn.Linear(lowerCAmelCase__						,       lowerCAmelCase__				) for dim in args_dim.values()]				)
								SCREAMING_SNAKE_CASE													=					domain_map







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Tuple[torch.Tensor]:
								SCREAMING_SNAKE_CASE													=					[proj(lowerCAmelCase__				) for proj in self.proj]

								return self.domain_map(*lowerCAmelCase__				)





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							int:
								super().__init__()
								SCREAMING_SNAKE_CASE													=					function







		def 							__A						(	self						,       lowerCAmelCase__						,       *lowerCAmelCase__				)     ->							List[Any]:
								return self.function(lowerCAmelCase__						,       *lowerCAmelCase__				)





class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     type
		SCREAMING_SNAKE_CASE_						:     int
		SCREAMING_SNAKE_CASE_						:     Dict[str, int]







		def __init__(	self						,       lowerCAmelCase__ = 1				)     ->							None:
								SCREAMING_SNAKE_CASE													=					dim
								SCREAMING_SNAKE_CASE													=					{k: dim * self.args_dim[k] for k in self.args_dim}







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Union[str, Any]:
								if self.dim == 1:
														return self.distribution_class(*lowerCAmelCase__				)
								else:
														return Independent(self.distribution_class(*lowerCAmelCase__				)						,       1				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       )     ->							Distribution:
								SCREAMING_SNAKE_CASE													=					self._base_distribution(lowerCAmelCase__				)
								if loc is None and scale is None:
														return distr
								else:
														return AffineTransformed(lowerCAmelCase__						,       loc=lowerCAmelCase__						,       scale=lowerCAmelCase__						,       event_dim=self.event_dim				)







		@property
		def 							__A						(	self				)     ->							Tuple:
								return () if self.dim == 1 else (self.dim,)







		@property
		def 							__A						(	self				)     ->							int:
								return len(self.event_shape				)







		@property
		def 							__A						(	self				)     ->							float:
								return 0.0







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							nn.Module:
								return ParameterProjection(
								    in_features=lowerCAmelCase__						,       args_dim=self.args_dim						,       domain_map=LambdaLayer(self.domain_map				)						,       )







		def 							__A						(	self						,       *lowerCAmelCase__				)     ->							Dict:
								raise NotImplementedError()







		@staticmethod
		def 							__A						(	lowerCAmelCase__				)     ->							torch.Tensor:
								return (x + torch.sqrt(torch.square(lowerCAmelCase__				) + 4.0				)) / 2.0





class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Dict[str, int]								=     {"df": 1, "loc": 1, "scale": 1}
		SCREAMING_SNAKE_CASE_						:     type								=     StudentT







		@classmethod
		def 							__A						(	cls						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					cls.squareplus(lowerCAmelCase__				).clamp_min(torch.finfo(scale.dtype				).eps				)
								SCREAMING_SNAKE_CASE													=					2.0 + cls.squareplus(lowerCAmelCase__				)
								return df.squeeze(-1				), loc.squeeze(-1				), scale.squeeze(-1				)





class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Dict[str, int]								=     {"loc": 1, "scale": 1}
		SCREAMING_SNAKE_CASE_						:     type								=     Normal







		@classmethod
		def 							__A						(	cls						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					cls.squareplus(lowerCAmelCase__				).clamp_min(torch.finfo(scale.dtype				).eps				)
								return loc.squeeze(-1				), scale.squeeze(-1				)





class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Dict[str, int]								=     {"total_count": 1, "logits": 1}
		SCREAMING_SNAKE_CASE_						:     type								=     NegativeBinomial







		@classmethod
		def 							__A						(	cls						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							int:
								SCREAMING_SNAKE_CASE													=					cls.squareplus(lowerCAmelCase__				)
								return total_count.squeeze(-1				), logits.squeeze(-1				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Distribution:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					distr_args
								if self.dim == 1:
														return self.distribution_class(total_count=lowerCAmelCase__						,       logits=lowerCAmelCase__				)
								else:
														return Independent(self.distribution_class(total_count=lowerCAmelCase__						,       logits=lowerCAmelCase__				)						,       1				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None				)     ->							Distribution:
								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)				)






"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


