


'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset
class _lowerCamelCase	(						a__							):



		'''simple docstring'''







		def __init__( self		:						str  ,					_A		:						Optional[int]=""  ,					_A		:						Any="train"      )  ->			Tuple:
			assert os.path.isdir(SCREAMING_SNAKE_CASE_      )

			__magic_name__	:   Optional[int]					     =					[]
			__magic_name__	:   str					     =					os.listdir(SCREAMING_SNAKE_CASE_      )
			for story_filename in story_filenames_list:
				if "summary" in story_filename:
					continue
				__magic_name__	:   List[Any]					     =					os.path.join(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_      )
				if not os.path.isfile(SCREAMING_SNAKE_CASE_      ):
					continue
				self.documents.append(SCREAMING_SNAKE_CASE_      )







		def __len__( self		:						Optional[Any]      )  ->			List[Any]:
			return len(self.documents      )







		def __getitem__( self		:						List[str]  ,					_A		:						List[str]      )  ->			Optional[int]:
			__magic_name__	:   Any					     =					self.documents[idx]
			__magic_name__	:   Tuple					     =					document_path.split('/'      )[-1]
			with open(SCREAMING_SNAKE_CASE_  ,					encoding='utf-8'      ) as source:
				__magic_name__	:   List[str]					     =					source.read()
				__magic_name__	:   str					     =					process_story(SCREAMING_SNAKE_CASE_      )
			return document_name, story_lines, summary_lines







def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







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

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

	# gather article lines
	__magic_name__	:   List[str]					     =					[]
	__magic_name__	:   Union[str, Any]					     =					deque(snake_case__		)
	while True:
		try:
			__magic_name__	:   Tuple					     =					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
	__magic_name__	:   List[str]					     =					list(filter(lambda lowerCAmelCase		: not t.startswith('@highlight'		)		,		snake_case__		)		)

	return story_lines, summary_lines





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     List[Any]		):





	"""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 lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Optional[Any]					     =					torch.ones_like(snake_case__		)
	__magic_name__	:   List[Any]					     =					sequence == pad_token_id
	__magic_name__	:   List[str]					     =					0
	return mask





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[tokenizer.encode(snake_case__		) for line in story_lines]
	__magic_name__	:   Any					     =					[token for sentence in story_lines_token_ids for token in sentence]
	__magic_name__	:   Optional[int]					     =					[tokenizer.encode(snake_case__		) for line in summary_lines]
	__magic_name__	:   Any					     =					[token for sentence in summary_lines_token_ids for token in sentence]

	return story_token_ids, summary_token_ids





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					[]
	for sequence in batch:
		__magic_name__	:   Optional[int]					     =					-1
		__magic_name__	:   Optional[int]					     =					[]
		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__		)



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	return "".join(chr(ord(lowerCAmelCase		) - 32		) if 'a' <= char <= 'z' else char for char in word		)


if __name__ == "__main__":
							from doctest import testmod

							testmod()