



'''simple docstring'''
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        __A   (							_SCREAMING_SNAKE_CASE     :  BertModel					,   _SCREAMING_SNAKE_CASE     :  str					,   _SCREAMING_SNAKE_CASE     :  str	):







					"""simple docstring"""






					__SCREAMING_SNAKE_CASE				:    int						    =							("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

					__SCREAMING_SNAKE_CASE				:    str						    =							(
					    ("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(UpperCamelCase__	):
										os.makedirs(UpperCamelCase__	)

					__SCREAMING_SNAKE_CASE				:    Any						    =							model.state_dict()

					def to_tf_var_name(_SCREAMING_SNAKE_CASE     :  str	):
										for patt, repl in iter(UpperCamelCase__	):
															__SCREAMING_SNAKE_CASE				:    int						    =							name.replace(UpperCamelCase__					,   UpperCamelCase__	)
										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				:    Union[str, Any]						    =							tf.dtypes.as_dtype(tensor.dtype	)
										__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							tf.get_variable(dtype=UpperCamelCase__					,   shape=tensor.shape					,   name=UpperCamelCase__					,   initializer=tf.zeros_initializer()	)
										session.run(tf.variables_initializer([tf_var]	)	)
										session.run(UpperCamelCase__	)
										return tf_var

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

										__SCREAMING_SNAKE_CASE				:    Tuple						    =							tf.train.Saver(tf.trainable_variables()	)
										saver.save(UpperCamelCase__					,   os.path.join(UpperCamelCase__					,   model_name.replace("-"					,   "_"	) + ".ckpt"	)	)



def        __A   (							_SCREAMING_SNAKE_CASE     :  List[str]=None	):







					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE				:    Dict						    =							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=UpperCamelCase__					,   ckpt_dir=args.tf_cache_dir					,   model_name=args.model_name	)


if __name__ == "__main__":
					main()



'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def    lowerCAmelCase       (		UpperCamelCase__					: BertModel     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	__UpperCAmelCase      					=      ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

	__UpperCAmelCase      					=      (
	    ('''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(UpperCamelCase__							):
		os.makedirs(UpperCamelCase__							)

	__UpperCAmelCase      					=      model.state_dict()

	def to_tf_var_name(UpperCamelCase__					: str							):
		for patt, repl in iter(UpperCamelCase__							):
			__UpperCAmelCase      					=      name.replace(UpperCamelCase__     ,       UpperCamelCase__							)
		return f"""bert/{name}"""

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

	tf.reset_default_graph()
	with tf.Session() as session:
		for var_name in state_dict:
			__UpperCAmelCase      					=      to_tf_var_name(UpperCamelCase__							)
			__UpperCAmelCase      					=      state_dict[var_name].numpy()
			if any(x in var_name for x in tensors_to_transpose							):
				__UpperCAmelCase      					=      torch_tensor.T
			__UpperCAmelCase      					=      create_tf_var(tensor=UpperCamelCase__     ,       name=UpperCamelCase__     ,       session=UpperCamelCase__							)
			tf.keras.backend.set_value(UpperCamelCase__     ,       UpperCamelCase__							)
			__UpperCAmelCase      					=      session.run(UpperCamelCase__							)
			print(f"""Successfully created {tf_name}: {np.allclose(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)

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





def    lowerCAmelCase       (		UpperCamelCase__					: List[str]=None							):


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

	__UpperCAmelCase      					=      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=UpperCamelCase__     ,       ckpt_dir=args.tf_cache_dir     ,       model_name=args.model_name							)


if __name__ == "__main__":
					main()
