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

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 		A_(     A						:		BertModel		,     A						:		str		,     A						:		str):
							UpperCamelCase   			=     ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

							UpperCamelCase   			=     (
							    ('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(A):
														os.makedirs(A)

							UpperCamelCase   			=     model.state_dict()

							def to_tf_var_name(A						:		str):
														for patt, repl in iter(A):
																					UpperCamelCase   			=     name.replace(A		,     A)
														return f'''bert/{name}'''

							def create_tf_var(A						:		np.ndarray		,     A						:		str		,     A						:		tf.Session):
														UpperCamelCase   			=     tf.dtypes.as_dtype(tensor.dtype)
														UpperCamelCase   			=     tf.get_variable(dtype=A		,     shape=tensor.shape		,     name=A		,     initializer=tf.zeros_initializer())
														session.run(tf.variables_initializer([tf_var]))
														session.run(A)
														return tf_var

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					UpperCamelCase   			=     to_tf_var_name(A)
																					UpperCamelCase   			=     state_dict[var_name].numpy()
																					if any(x in var_name for x in tensors_to_transpose):
																												UpperCamelCase   			=     torch_tensor.T
																					UpperCamelCase   			=     create_tf_var(tensor=A		,     name=A		,     session=A)
																					tf.keras.backend.set_value(A		,     A)
																					UpperCamelCase   			=     session.run(A)
																					print(f'''Successfully created {tf_name}: {np.allclose(A		,     A)}''')

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







def 		A_(     A						:		Optional[int]=None):
							UpperCamelCase   			=     argparse.ArgumentParser()
							parser.add_argument('--model_name'		,     type=A		,     required=A		,     help='model name e.g. bert-base-uncased')
							parser.add_argument(
							    '--cache_dir'		,     type=A		,     default=A		,     required=A		,     help='Directory containing pytorch model')
							parser.add_argument('--pytorch_model_path'		,     type=A		,     required=A		,     help='/path/to/<pytorch-model-name>.bin')
							parser.add_argument('--tf_cache_dir'		,     type=A		,     required=A		,     help='Directory in which to save tensorflow model')
							UpperCamelCase   			=     parser.parse_args(A)

							UpperCamelCase   			=     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=A		,     ckpt_dir=args.tf_cache_dir		,     model_name=args.model_name)


if __name__ == "__main__":
						main()








'''simple docstring'''
from math import factorial

UpperCAmelCase_			:  List[str]										=   {str(d): factorial(d) for d in range(1_0)}


def       _lowercase							(					UpperCamelCase__							:       int      ):
 return sum(DIGIT_FACTORIAL[d] for d in str(UpperCamelCase__      )      )


def       _lowercase							(					):
 __A	:		Union[str, Any]	  =      7 * factorial(9      ) + 1
 return sum(i for i in range(3,					UpperCamelCase__      ) if sum_of_digit_factorial(UpperCamelCase__      ) == i      )


if __name__ == "__main__":
 print(f'''{solution() = }''')






