






import os
from collections import deque

import torch
from torch.utils.data import Dataset



class 				_UpperCamelCase      (a_      ):


      def __init__(						self       ,    __UpperCamelCase=""       ,    __UpperCamelCase="train"						)->						Tuple:
           assert os.path.isdir(__UpperCamelCase						)

           __lowerCAmelCase             =       []
           __lowerCAmelCase             =       os.listdir(__UpperCamelCase						)
           for story_filename in story_filenames_list:
                if "summary" in story_filename:
                     continue
                __lowerCAmelCase             =       os.path.join(__UpperCamelCase       ,    __UpperCamelCase						)
                if not os.path.isfile(__UpperCamelCase						):
                     continue
                self.documents.append(__UpperCamelCase						)


      def __len__(						self						)->						Tuple:
           return len(self.documents						)


      def __getitem__(						self       ,    __UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       self.documents[idx]
           __lowerCAmelCase             =       document_path.split("/"						)[-1]
           with open(__UpperCamelCase       ,    encoding="utf-8"						) as source:
                __lowerCAmelCase             =       source.read()
                __lowerCAmelCase             =       process_story(__UpperCamelCase						)
           return document_name, story_lines, summary_lines





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       list(filter(lambda __snake_case							: len(__snake_case							) != 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(__snake_case							) for line in nonempty_lines]

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

     return story_lines, summary_lines





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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):




     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   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       torch.ones_like(__snake_case							)
     __lowerCAmelCase             =       sequence == pad_token_id
     __lowerCAmelCase             =       0
     return mask





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       [tokenizer.encode(__snake_case							) for line in story_lines]
     __lowerCAmelCase             =       [token for sentence in story_lines_token_ids for token in sentence]
     __lowerCAmelCase             =       [tokenizer.encode(__snake_case							) 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   (    __snake_case       ,			__snake_case							):
     __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(__snake_case							)
     return torch.tensor(__snake_case							)










from timeit import timeit


def     lowercase	(    a ):



		'''simple docstring'''




		if number < 0:
				raise ValueError("the value of input must not be negative" )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0
		while number:
				number &= number - 1
				result += 1
		return result


def     lowercase	(    a ):



		'''simple docstring'''




		if number < 0:
				raise ValueError("the value of input must not be negative" )
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						0
		while number:
				if number % 2 == 1:
						result += 1
				number >>= 1
		return result


def     lowercase	(    ):



		'''simple docstring'''




		def do_benchmark(a ) -> None:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"import __main__ as z"
				print(F"Benchmark when {number = }:" )
				print(F"{get_set_bits_count_using_modulo_operator(a ) = }" )
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						timeit("z.get_set_bits_count_using_modulo_operator(25)"     , setup=a )
				print(F"timeit() runs in {timing} seconds" )
				print(F"{get_set_bits_count_using_brian_kernighans_algorithm(a ) = }" )
				SCREAMING_SNAKE_CASE_				:str				     =						timeit(
				    "z.get_set_bits_count_using_brian_kernighans_algorithm(25)"     , setup=a     , )
				print(F"timeit() runs in {timing} seconds" )

		for number in (25, 37, 58, 0):
				do_benchmark(a )
				print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()






