






import os
from collections.abc import Iterator






def   a			(						lowerCamelCase_ = "."				):






				'''simple docstring'''
				for dir_path, dir_names, filenames in os.walk(lowerCamelCase_				):
								lowercase__    =			[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
								for filename in filenames:
												if filename == "__init__.py":
																continue
												if os.path.splitext(lowerCamelCase_				)[1] in (".py", ".ipynb"):
																yield os.path.join(lowerCamelCase_		,   lowerCamelCase_				).lstrip('''./'''				)






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				return F"""{i * '  '}*""" if i else "\n##"






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			old_path.split(os.sep				)
				for i, new_part in enumerate(new_path.split(os.sep				)				):
								if (i + 1 > len(lowerCamelCase_				) or old_parts[i] != new_part) and new_part:
												print(F"""{md_prefix(lowerCamelCase_				)} {new_part.replace('_'		,   ' '				).title()}"""				)
				return new_path






def   a			(						lowerCamelCase_ = "."				):






				'''simple docstring'''
				lowercase__    =			''''''
				for filepath in sorted(good_file_paths(lowerCamelCase_				)				):
								lowercase__ ,						lowercase__    =			os.path.split(lowerCamelCase_				)
								if filepath != old_path:
												lowercase__    =			print_path(lowerCamelCase_		,   lowerCamelCase_				)
								lowercase__    =			(filepath.count(os.sep				) + 1) if filepath else 0
								lowercase__    =			F"""{filepath}/{filename}""".replace(''' '''		,   '''%20'''				)
								lowercase__    =			os.path.splitext(filename.replace('''_'''		,   ''' '''				).title()				)[0]
								print(F"""{md_prefix(lowerCamelCase_				)} [{filename}]({url})"""				)


if __name__ == "__main__":
							print_directory_md('.')












import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






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

				lowercase__    =			(
				    ('''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_				)

				lowercase__    =			model.state_dict()

				def to_tf_var_name(lowerCamelCase_				):
								for patt, repl in iter(lowerCamelCase_				):
												lowercase__    =			name.replace(lowerCamelCase_		,   lowerCamelCase_				)
								return F"""bert/{name}"""

				def create_tf_var(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):
								lowercase__    =			tf.dtypes.as_dtype(tensor.dtype				)
								lowercase__    =			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:
												lowercase__    =			to_tf_var_name(lowerCamelCase_				)
												lowercase__    =			state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose				):
																lowercase__    =			torch_tensor.T
												lowercase__    =			create_tf_var(tensor=lowerCamelCase_		,   name=lowerCamelCase_		,   session=lowerCamelCase_				)
												tf.keras.backend.set_value(lowerCamelCase_		,   lowerCamelCase_				)
												lowercase__    =			session.run(lowerCamelCase_				)
												print(F"""Successfully created {tf_name}: {np.allclose(lowerCamelCase_		,   lowerCamelCase_				)}"""				)

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






def   a			(						lowerCamelCase_=None				):






				'''simple docstring'''
				lowercase__    =			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'''				)
				lowercase__    =			parser.parse_args(lowerCamelCase_				)

				lowercase__    =			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()




