






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def   a_			(   _A   , _A   , _A	)      ->      List[str]:







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

				snake_case__						=  (
				    ('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_	)

				snake_case__						=  model.state_dict()

				def to_tf_var_name(_A	):
								for patt, repl in iter(UpperCAmelCase_	):
												snake_case__						=  name.replace(UpperCAmelCase_   , UpperCAmelCase_	)
								return f'''bert/{name}'''

				def create_tf_var(_A   , _A   , _A	):
								snake_case__						=  tf.dtypes.as_dtype(tensor.dtype	)
								snake_case__						=  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:
												snake_case__						=  to_tf_var_name(UpperCAmelCase_	)
												snake_case__						=  state_dict[var_name].numpy()
												if any(x in var_name for x in tensors_to_transpose	):
																snake_case__						=  torch_tensor.T
												snake_case__						=  create_tf_var(tensor=UpperCAmelCase_   , name=UpperCAmelCase_   , session=UpperCAmelCase_	)
												tf.keras.backend.set_value(UpperCAmelCase_   , UpperCAmelCase_	)
												snake_case__						=  session.run(UpperCAmelCase_	)
												print(f'''Successfully created {tf_name}: {np.allclose(UpperCAmelCase_   , UpperCAmelCase_	)}'''	)

								snake_case__						=  tf.train.Saver(tf.trainable_variables()	)
								saver.save(UpperCAmelCase_   , os.path.join(UpperCAmelCase_   , model_name.replace('-'   , '_'	) + '.ckpt'	)	)
def   a_			(   _A=None	)      ->      Dict:







				"""simple docstring"""
				snake_case__						=  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'	)
				snake_case__						=  parser.parse_args(UpperCAmelCase_	)

				snake_case__						=  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'''
from pathlib import Path

import fire






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  Path(UpperCAmelCase_				)
						_UpperCamelCase   :						str												=  Path(UpperCAmelCase_				)
						dest_dir.mkdir(exist_ok=UpperCAmelCase_				)
						for path in src_dir.iterdir():
												_UpperCamelCase   :						int												=  [x.rstrip() for x in list(path.open().readlines()				)][:n]
												_UpperCamelCase   :						Any												=  dest_dir.joinpath(path.name				)
												print(UpperCAmelCase_				)
												dest_path.open('w'				).write('\n'.join(UpperCAmelCase_				)				)


if __name__ == "__main__":
					fire.Fire(minify)


