



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

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






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

						_UpperCamelCase   :						Union[str, 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_				)

						_UpperCamelCase   :						List[Any]												=  model.state_dict()

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

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

												_UpperCamelCase   :						Optional[Any]												=  tf.train.Saver(tf.trainable_variables()				)
												saver.save(UpperCAmelCase_    ,    os.path.join(UpperCAmelCase_    ,    model_name.replace('-'    ,    '_'				) + '.ckpt'				)				)






def A__       (	UpperCAmelCase_=None				):
						_UpperCamelCase   :						Union[str, 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'				)
						_UpperCamelCase   :						Optional[int]												=  parser.parse_args(UpperCAmelCase_				)

						_UpperCamelCase   :						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()







'''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)


