


'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def __init__(			self     ,							__UpperCamelCase=""     ,							__UpperCamelCase="train"		)       ->    Union[str, Any]:
          assert os.path.isdir(__UpperCamelCase		)

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






     def __len__(			self		)       ->    Any:
          return len(self.documents		)






     def __getitem__(			self     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		self.documents[idx]
          _a						=		document_path.split("/"		)[-1]
          with open(__UpperCamelCase     ,							encoding="utf-8"		) as source:
               _a						=		source.read()
               _a			,			_a						=		process_story(__UpperCamelCase		)
          return document_name, story_lines, summary_lines







def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]				)      ->     List[Any]:







     '''simple docstring'''






     _a						=		list(filter(lambda __lowerCamelCase				: len(__lowerCamelCase				) != 0      ,  [line.strip() for line in raw_story.split("\n"				)]				)				)

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

     # gather article lines
     _a						=		[]
     _a						=		deque(__lowerCamelCase				)
     while True:
          try:
               _a						=		lines.popleft()
               if element.startswith("@highlight"				):
                    break
               story_lines.append(__lowerCamelCase				)
          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
     _a						=		list(filter(lambda __lowerCamelCase				: not t.startswith("@highlight"				)      ,  __lowerCamelCase				)				)

     return story_lines, summary_lines






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]				)      ->     Tuple:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: List[str]				)      ->     int:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: List[Any]				)      ->     str:







     '''simple docstring'''






     _a						=		torch.ones_like(__lowerCamelCase				)
     _a						=		sequence == pad_token_id
     _a						=		0
     return mask






def __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: int				)      ->     Union[str, Any]:







     '''simple docstring'''






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

     return story_token_ids, summary_token_ids






def __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: int				)      ->     int:







     '''simple docstring'''






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





'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def __UpperCamelCase   (			__lowerCamelCase		: BertModel      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     List[str]:







     '''simple docstring'''






     _a						=		("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     _a						=		(
         ("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				)

     _a						=		model.state_dict()

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

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

          _a						=		tf.train.Saver(tf.trainable_variables()				)
          saver.save(__lowerCamelCase      ,  os.path.join(__lowerCamelCase      ,  model_name.replace("-"      ,  "_"				) + ".ckpt"				)				)






def __UpperCamelCase   (			__lowerCamelCase		: str=None				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		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"				)
     _a						=		parser.parse_args(__lowerCamelCase				)

     _a						=		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()

