






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  str					)		->			int:






	"""simple docstring"""



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

	UpperCamelCase     :List[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(lowerCAmelCase__					):
		os.makedirs(lowerCAmelCase__					)

	UpperCamelCase     :int          =	model.state_dict()

	def to_tf_var_name(__magic_name__					:  Union[str, Any]					):
		for patt, repl in iter(lowerCAmelCase__					):
			UpperCamelCase     :Union[str, Any]          =	name.replace(lowerCAmelCase__	,			lowerCAmelCase__					)
		return f"""bert/{name}"""

	def create_tf_var(__magic_name__					:  List[str]	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Optional[int]					):
		UpperCamelCase     :Union[str, Any]          =	tf.dtypes.as_dtype(tensor.dtype					)
		UpperCamelCase     :Dict          =	tf.get_variable(dtype=lowerCAmelCase__	,			shape=tensor.shape	,			name=lowerCAmelCase__	,			initializer=tf.zeros_initializer()					)
		session.run(tf.variables_initializer([tf_var]					)					)
		session.run(lowerCAmelCase__					)
		return tf_var

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any=None					)		->			Optional[int]:






	"""simple docstring"""



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

	UpperCamelCase     :int          =	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=lowerCAmelCase__	,			ckpt_dir=args.tf_cache_dir	,			model_name=args.model_name					)


if __name__ == "__main__":
	main()


def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




