


"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a=""    ,   __a="train"	):
										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	):
										return len(self.documents	)



							def __getitem__(     self    ,   __a	):
										__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 _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			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
			__lowerCAmelCase				    =			[_add_missing_period(_UpperCamelCase      ) for line in nonempty_lines]

			# gather article lines
			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			deque(_UpperCamelCase      )
			while True:
						try:
									__lowerCAmelCase				    =			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
			__lowerCAmelCase				    =			list(filter(lambda _UpperCamelCase      : not t.startswith("@highlight"      )    , _UpperCamelCase      )      )

			return story_lines, summary_lines


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.ones_like(_UpperCamelCase      )
			__lowerCAmelCase				    =			sequence == pad_token_id
			__lowerCAmelCase				    =			0
			return mask


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[tokenizer.encode(_UpperCamelCase      ) for line in story_lines]
			__lowerCAmelCase				    =			[token for sentence in story_lines_token_ids for token in sentence]
			__lowerCAmelCase				    =			[tokenizer.encode(_UpperCamelCase      ) 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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''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(_UpperCamelCase      )
			return torch.tensor(_UpperCamelCase      )








"""simple docstring"""


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			1

			while len(_UpperCamelCase      ) < 1e6:
						constant.append(str(_UpperCamelCase      )      )
						i += 1

			__lowerCAmelCase				    =			"".join(_UpperCamelCase      )

			return (
			    int(constant[0]      )
			    * int(constant[9]      )
			    * int(constant[99]      )
			    * int(constant[999]      )
			    * int(constant[9999]      )
			    * int(constant[9_9999]      )
			    * int(constant[99_9999]      )
			)


if __name__ == "__main__":
	print(solution())




