





"""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)		->		str:
				__a			:    List[str]      							=	('''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(a_):
								os.makedirs(a_)

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

				def to_tf_var_name(a_    :str):
								for patt, repl in iter(a_):
												__a			:    int      							=	name.replace(a_ ,							a_)
								return F"""bert/{name}"""

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

								__a			:    Tuple      							=	tf.train.Saver(tf.trainable_variables())
								saver.save(a_ ,							os.path.join(a_ ,							model_name.replace('''-''' ,							'''_''') + '''.ckpt'''))

def      __A   (      a_    :int=None)		->		str:
				__a			:    str      							=	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''')
				__a			:    Optional[Any]      							=	parser.parse_args(a_)

				__a			:    Optional[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=a_ ,							ckpt_dir=args.tf_cache_dir ,							model_name=args.model_name)


if __name__ == "__main__":
		main()




import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
