
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						BertModel							,						__UpperCamelCase :						str							,						__UpperCamelCase :						str       ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Any         =							('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

				A__  :	Any         =							(
				    ('''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       )

				A__  :	str         =							model.state_dict()

				def to_tf_var_name(__UpperCamelCase :						str       ):
								for patt, repl in iter(__UpperCamelCase       ):
												A__  :	Tuple         =							name.replace(__UpperCamelCase							,						__UpperCamelCase       )
								return F"bert/{name}"

				def create_tf_var(__UpperCamelCase :						np.ndarray							,						__UpperCamelCase :						str							,						__UpperCamelCase :						tf.Session       ):
								A__  :	Tuple         =							tf.dtypes.as_dtype(tensor.dtype       )
								A__  :	str         =							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:
												A__  :	Tuple         =							to_tf_var_name(__UpperCamelCase       )
												A__  :	List[str]         =							state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose       ):
																A__  :	Any         =							torch_tensor.T
												A__  :	int         =							create_tf_var(tensor=__UpperCamelCase							,						name=__UpperCamelCase							,						session=__UpperCamelCase       )
												tf.keras.backend.set_value(__UpperCamelCase							,						__UpperCamelCase       )
												A__  :	List[Any]         =							session.run(__UpperCamelCase       )
												print(F"Successfully created {tf_name}: {np.allclose(__UpperCamelCase							,						__UpperCamelCase       )}"       )

								A__  :	Any         =							tf.train.Saver(tf.trainable_variables()       )
								saver.save(__UpperCamelCase							,						os.path.join(__UpperCamelCase							,						model_name.replace('''-'''							,						'''_'''       ) + '''.ckpt'''       )       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]=None       ) ->       List[str]:

				"""simple docstring"""







				A__  :	Optional[int]         =							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'''       )
				A__  :	Dict         =							parser.parse_args(__UpperCamelCase       )

				A__  :	List[Any]         =							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()

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.feature_extraction_class(**self.feat_extract_dict		)
					A__  :	Tuple         =							json.loads(feat_extract.to_json_string()		)
					for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							os.path.join(UpperCamelCase__				,       '''feat_extract.json'''		)
									feat_extract_first.to_json_file(UpperCamelCase__		)
									A__  :	Dict         =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

					self.assertEqual(feat_extract_second.to_dict()				,       feat_extract_first.to_dict()		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							feat_extract_first.save_pretrained(UpperCamelCase__		)[0]
									check_json_file_has_correct_format(UpperCamelCase__		)
									A__  :	Optional[int]         =							self.feature_extraction_class.from_pretrained(UpperCamelCase__		)

					self.assertEqual(feat_extract_second.to_dict()				,       feat_extract_first.to_dict()		)





	def   __snake_case				(			self		):
					A__  :	str         =							self.feature_extraction_class()
					self.assertIsNotNone(UpperCamelCase__		)