
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[str]:







	"""simple docstring"""



	__A             =			("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

	__A             =			(
	    ("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(_lowercase						):
		os.makedirs(_lowercase						)

	__A             =			model.state_dict()

	def to_tf_var_name(a_						):
		for patt, repl in iter(_lowercase						):
			__A             =			name.replace(_lowercase		,       _lowercase						)
		return F'''bert/{name}'''

	def create_tf_var(a_		,       a_		,       a_						):
		__A             =			tf.dtypes.as_dtype(tensor.dtype						)
		__A             =			tf.get_variable(dtype=_lowercase		,       shape=tensor.shape		,       name=_lowercase		,       initializer=tf.zeros_initializer()						)
		session.run(tf.variables_initializer([tf_var]						)						)
		session.run(_lowercase						)
		return tf_var

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

		__A             =			tf.train.Saver(tf.trainable_variables()						)
		saver.save(_lowercase		,       os.path.join(_lowercase		,       model_name.replace("-"		,       "_"						) + ".ckpt"						)						)


def 						UpperCAmelCase      (     a_=None						)		->     Any:







	"""simple docstring"""



	__A             =			argparse.ArgumentParser()
	parser.add_argument("--model_name"		,       type=_lowercase		,       required=_lowercase		,       help="model name e.g. bert-base-uncased"						)
	parser.add_argument(
	    "--cache_dir"		,       type=_lowercase		,       default=_lowercase		,       required=_lowercase		,       help="Directory containing pytorch model"						)
	parser.add_argument("--pytorch_model_path"		,       type=_lowercase		,       required=_lowercase		,       help="/path/to/<pytorch-model-name>.bin"						)
	parser.add_argument("--tf_cache_dir"		,       type=_lowercase		,       required=_lowercase		,       help="Directory in which to save tensorflow model"						)
	__A             =			parser.parse_args(_lowercase						)

	__A             =			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=_lowercase		,       ckpt_dir=args.tf_cache_dir		,       model_name=args.model_name						)


if __name__ == "__main__":
					main()


import copy
import re





class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					"hp"
	snake_case_    =					{}
	snake_case_    =					None






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			Dict							,A :			Any			):
					__A             =			prefix
					__A             =			defaults
					cls.build_naming_info()






	@staticmethod
	def     UpperCamelCase_  (	A :			Dict							,A :			int			):
					if len(A			) == 0:
									return ""
					__A             =			None
					if any(char.isdigit() for char in word			):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''			)
					if word in info["short_word"]:
									return info["short_word"][word]
					for prefix_len in range(1							,len(A			) + 1			):
									__A             =			word[:prefix_len]
									if prefix in info["reverse_short_word"]:
													continue
									else:
													__A             =			prefix
													break

					if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(A :			str			):
													__A             =			""
													while integer != 0:
																	__A             =			chr(ord("A"			) + integer % 10			) + s
																	integer //= 10
													return s

									__A             =			0
									while True:
													__A             =			word + "#" + int_to_alphabetic(A			)
													if sword in info["reverse_short_word"]:
																	continue
													else:
																	__A             =			sword
																	break

					__A             =			short_word
					__A             =			word
					return short_word






	@staticmethod
	def     UpperCamelCase_  (	A :			int							,A :			Tuple			):
					__A             =			param_name.split("_"			)

					__A             =			[TrialShortNamer.shortname_for_word(A							,A			) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					__A             =			["", "_"]

					for separator in separators:
									__A             =			separator.join(A			)
									if shortname not in info["reverse_short_param"]:
													__A             =			shortname
													__A             =			param_name
													return shortname

					return param_name






	@staticmethod
	def     UpperCamelCase_  (	A :			Optional[Any]							,A :			Tuple			):
					__A             =			TrialShortNamer.shortname_for_key(A							,A			)
					__A             =			short_name
					__A             =			param_name






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict			):
					if cls.NAMING_INFO is not None:
									return

					__A             =			{
					    "short_word": {},
					    "reverse_short_word": {},
					    "short_param": {},
					    "reverse_short_param": {},
					}

					__A             =			list(cls.DEFAULTS.keys()			)

					for k in field_keys:
									cls.add_new_param_name(A							,A			)

					__A             =			info






	@classmethod
	def     UpperCamelCase_  (	cls :			Dict							,A :			List[str]			):
					cls.build_naming_info()
					assert cls.PREFIX is not None
					__A             =			[copy.copy(cls.PREFIX			)]

					for k, v in params.items():
									if k not in cls.DEFAULTS:
													raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''			)
									if v == cls.DEFAULTS[k]:
													# The default value is not added to the name
													continue

									__A             =			cls.NAMING_INFO["short_param"][k]

									if isinstance(A							,A			):
													__A             =			1 if v else 0

									__A             =			"" if isinstance(A							,(int, float)			) else "-"
									__A             =			f'''{key}{sep}{v}'''
									name.append(A			)

					return "_".join(A			)







	@classmethod
	def     UpperCamelCase_  (	cls :			Tuple							,A :			Tuple			):
					__A             =			repr[len(cls.PREFIX			) + 1 :]
					if repr == "":
									__A             =			[]
					else:
									__A             =			repr.split("_"			)

					__A             =			{}

					for value in values:
									if "-" in value:
													__A      ,							__A             =			value.split("-"			)
									else:
													__A             =			re.sub("[0-9.]"							,""							,A			)
													__A             =			float(re.sub("[^0-9.]"							,""							,A			)			)

									__A             =			cls.NAMING_INFO["reverse_short_param"][p_k]

									__A             =			p_v

					for k in cls.DEFAULTS:
									if k not in parameters:
													__A             =			cls.DEFAULTS[k]

					return parameters
