

'''simple docstring'''


import os
from collections import deque

import torch
from torch.utils.data import Dataset



class 					lowercase						(  a_ ):


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

								UpperCAmelCase_		:      Tuple       	=					[]
								UpperCAmelCase_		:      Optional[int]       	=					os.listdir(_snake_case)
								for story_filename in story_filenames_list:
									if "summary" in story_filename:
										continue
									UpperCAmelCase_		:      Dict       	=					os.path.join(_snake_case    ,			_snake_case)
									if not os.path.isfile(_snake_case):
										continue
									self.documents.append(_snake_case)


							def __len__(			self)							->				Union[str, Any]:
								return len(self.documents)





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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_		:      Optional[Any]       	=					list(filter(lambda UpperCamelCase  : len(UpperCamelCase  ) != 0   ,[line.strip() for line in raw_story.split('\n'  )]  )  )

	# for some unknown reason some lines miss a period, add it
	UpperCAmelCase_		:      str       	=					[_add_missing_period(UpperCamelCase  ) for line in nonempty_lines]

	# gather article lines
	UpperCAmelCase_		:      str       	=					[]
	UpperCAmelCase_		:      List[Any]       	=					deque(UpperCamelCase  )
	while True:
		try:
			UpperCAmelCase_		:      List[str]       	=					lines.popleft()
			if element.startswith('@highlight'  ):
				break
			story_lines.append(UpperCamelCase  )
		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
	UpperCAmelCase_		:      Tuple       	=					list(filter(lambda UpperCamelCase  : not t.startswith('@highlight'  )   ,UpperCamelCase  )  )

	return story_lines, summary_lines







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      Union[str, Any]       	=					['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
	if line.startswith('@highlight'  ):
		return line
	if line[-1] in END_TOKENS:
		return line
	return line + "."







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    str:
	if len(UpperCamelCase  ) > block_size:
		return sequence[:block_size]
	else:
		sequence.extend([pad_token_id] * (block_size - len(UpperCamelCase  ))  )
		return sequence







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_		:      int       	=					torch.ones_like(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					sequence == pad_token_id
	UpperCAmelCase_		:      str       	=					0
	return mask







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Optional[int]       	=					[tokenizer.encode(UpperCamelCase  ) for line in story_lines]
	UpperCAmelCase_		:      Optional[Any]       	=					[token for sentence in story_lines_token_ids for token in sentence]
	UpperCAmelCase_		:      Tuple       	=					[tokenizer.encode(UpperCamelCase  ) for line in summary_lines]
	UpperCAmelCase_		:      Optional[int]       	=					[token for sentence in summary_lines_token_ids for token in sentence]

	return story_token_ids, summary_token_ids







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      List[str]       	=					[]
	for sequence in batch:
		UpperCAmelCase_		:      Optional[Any]       	=					-1
		UpperCAmelCase_		:      str       	=					[]
		for s in sequence:
			if s == separator_token_id:
				sentence_num += 1
			embeddings.append(sentence_num % 2  )
		batch_embeddings.append(UpperCamelCase  )
	return torch.tensor(UpperCamelCase  )









'''simple docstring'''


from __future__ import annotations

from typing import Any



class 					lowercase						(  a_ ):
							pass



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None


							def __iter__(			self)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					self
								UpperCAmelCase_		:      List[str]       	=					[]
								while node:
									if node in visited:
										raise ContainsLoopError
									visited.append(_snake_case)
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next_node





							@property
							def 						_snake_case							(			self)							->				bool:
								try:
									list(self)
									return False
								except ContainsLoopError:
									return True


if __name__ == "__main__":
						lowerCAmelCase__										= Node(1)
						lowerCAmelCase__										= Node(2)
						lowerCAmelCase__										= Node(3)
						lowerCAmelCase__										= Node(4)
						print(root_node.has_loop)  # False
						lowerCAmelCase__										= root_node.next_node
						print(root_node.has_loop)  # True

						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						print(root_node.has_loop)  # False

						lowerCAmelCase__										= Node(1)
						print(root_node.has_loop)  # False






