



import os
from collections import deque

import torch
from torch.utils.data import Dataset



class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				A_     :       Dict=""				,				A_     :       Tuple="train"):
									assert os.path.isdir(A_)

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




							def __len__(       self     :       Optional[int]):
									return len(self.documents)







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





def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Tuple									= 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_			:     Union[str, Any]									= [_add_missing_period(__UpperCamelCase ) for line in nonempty_lines]

		# gather article lines
		lowerCAmelCase_			:     Any									= []
		lowerCAmelCase_			:     Any									= deque(__UpperCamelCase )
		while True:
				try:
						lowerCAmelCase_			:     Any									= 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_			:     str									= list(filter(lambda __UpperCamelCase : not t.startswith('''@highlight''' )     ,__UpperCamelCase ) )

		return story_lines, summary_lines


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


def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		Union[str, Any] ):
		if len(__UpperCamelCase ) > block_size:
				return sequence[:block_size]
		else:
				sequence.extend([pad_token_id] * (block_size - len(__UpperCamelCase )) )
				return sequence


def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Union[str, Any]									= torch.ones_like(__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= sequence == pad_token_id
		lowerCAmelCase_			:     int									= 0
		return mask


def 			UpperCamelCase(  __UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Optional[Any] ):
		lowerCAmelCase_			:     Dict									= [tokenizer.encode(__UpperCamelCase ) for line in story_lines]
		lowerCAmelCase_			:     Optional[Any]									= [token for sentence in story_lines_token_ids for token in sentence]
		lowerCAmelCase_			:     Tuple									= [tokenizer.encode(__UpperCamelCase ) for line in summary_lines]
		lowerCAmelCase_			:     Optional[Any]									= [token for sentence in summary_lines_token_ids for token in sentence]

		return story_token_ids, summary_token_ids





def 			UpperCamelCase(  __UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Tuple ):
		lowerCAmelCase_			:     Union[str, Any]									= []
		for sequence in batch:
				lowerCAmelCase_			:     str									= -1
				lowerCAmelCase_			:     List[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 collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


if __name__ == "__main__":
				print(F"{solution() = }")




