



import os
from collections import deque

import torch
from torch.utils.data import Dataset



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase=""	,__lowerCamelCase="train"				)  ->    str:

						"""simple docstring"""






						assert os.path.isdir(__lowerCamelCase				)

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



		def __len__(self				)  ->    int:

						"""simple docstring"""






						return len(self.documents				)



		def __getitem__(self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.documents[idx]
						lowerCAmelCase__  :       str				=  document_path.split('''/'''				)[-1]
						with open(__lowerCamelCase	,encoding='''utf-8'''				) as source:
										lowerCAmelCase__  :       Dict				=  source.read()
										lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  process_story(__lowerCamelCase				)
						return document_name, story_lines, summary_lines






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  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
				lowerCAmelCase__  :       Optional[int]				=  [_add_missing_period(lowerCamelCase_) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__  :       Tuple				=  []
				lowerCAmelCase__  :       Tuple				=  deque(lowerCamelCase_)
				while True:
								try:
												lowerCAmelCase__  :       Dict				=  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
				lowerCAmelCase__  :       Any				=  list(filter(lambda lowerCamelCase_: not t.startswith('''@highlight''') ,lowerCamelCase_))

				return story_lines, summary_lines






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  torch.ones_like(lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  sequence == pad_token_id
				lowerCAmelCase__  :       List[str]				=  0
				return mask






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  [tokenizer.encode(lowerCamelCase_) for line in story_lines]
				lowerCAmelCase__  :       Union[str, Any]				=  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__  :       Tuple				=  [tokenizer.encode(lowerCamelCase_) for line in summary_lines]
				lowerCAmelCase__  :       List[Any]				=  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	str):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  []
				for sequence in batch:
								lowerCAmelCase__  :       str				=  -1
								lowerCAmelCase__  :       Tuple				=  []
								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_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       Dict				=  f"""Input value of [number={number}] must be an integer"""
								raise TypeError(lowerCamelCase_)

				if number < 1:
								lowerCAmelCase__  :       Any				=  f"""Input value of [number={number}] must be > 0"""
								raise ValueError(lowerCamelCase_)

				lowerCAmelCase__  :       List[str]				=  1

				for i in range(1 ,lowerCamelCase_):
								current_number *= 4 * i - 2
								current_number //= i + 1

				return current_number


if __name__ == "__main__":
							import doctest

							doctest.testmod()

