code: 
  
  
  import json
  import os
  from dataclasses import dataclass
  
  import numpy as np
  import pyarrow as pa
  
  import datasets
  from utils import get_duration
  
  
  SCREAMING_SNAKE_CASE__				:    int         =    100_000_000_000
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    10_000
  
  SCREAMING_SNAKE_CASE__				:    int         =    os.path.split(__file__)
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
  
  
  
  
  def       a__						(		snake_case__				:       int	,      snake_case__				:       int    ):
    _UpperCAmelCase		:       List[Any]       =    pa.Table.from_pydict({"""col""": [0] * chunk_size}    )
    _UpperCAmelCase		:       Dict       =    pa.concat_tables([table] * (num_examples // chunk_size)    )
    return datasets.Dataset(snake_case__	,      fingerprint="""table_100B"""    )
  
  
  @dataclass
  class 		_SCREAMING_SNAKE_CASE    :
       __SCREAMING_SNAKE_CASE    				=						4_2
       __SCREAMING_SNAKE_CASE    				=						4_2
       __SCREAMING_SNAKE_CASE    				=						4_2
       __SCREAMING_SNAKE_CASE    				=						4_2
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Optional[int]       =    np.random.default_rng(self.seed						)
         _UpperCAmelCase		:       List[str]       =    rng.integers(low=self.low       ,				high=self.high       ,				size=self.size						).tolist()
  
  
       def __iter__(     self						):
         return iter(self._sampled_values						)
  
  
  
  
       def __len__(     self						):
         return self.size
  
  
  
  
  @get_duration
  def       a__						(		snake_case__				:       datasets.Dataset    ):
    _UpperCAmelCase		:       Any       =    dataset[0]
  
  
  
  
  @get_duration
  def       a__						(		snake_case__				:       datasets.Dataset    ):
    _UpperCAmelCase		:       str       =    dataset[-1]
  
  
  
  
  @get_duration
  def       a__						(		snake_case__				:       datasets.Dataset    ):
    _UpperCAmelCase		:       int       =    dataset[range(len(snake_case__    ) // 2	,      len(snake_case__    ) // 2 + 1024    )]
  
  
  
  
  @get_duration
  def       a__						(		snake_case__				:       datasets.Dataset    ):
    _UpperCAmelCase		:       Union[str, Any]       =    dataset[RandIter(0	,      len(snake_case__    )	,      1024	,      seed=42    )]
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[str]       =    {"""num examples""": SPEED_TEST_N_EXAMPLES}
    _UpperCAmelCase		:       Tuple       =    (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print("""generating dataset"""    )
    _UpperCAmelCase		:       Dict       =    generate_100B_dataset(num_examples=snake_case__	,      chunk_size=snake_case__    )
    print("""Functions"""    )
    for func in functions:
      print(func.__name__    )
      _UpperCAmelCase		:       Optional[int]       =    func(snake_case__    )
  
    with open(snake_case__	,      """wb"""    ) as f:
      f.write(json.dumps(snake_case__    ).encode("""utf-8"""    )    )
  
  
  if __name__ == "__main__":  # useful to run the profiler
     benchmark_table_100B()
  
  
  
  
  
  
style_context: 
  
  
  import os
  
  import requests
  from bsa import BeautifulSoup
  from fake_useragent import UserAgent
  
  SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
  SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
    _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
    with open(snake_case__	,      """wb"""    ) as file:
      file.write(image.content    )
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
    _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
    _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
    _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
    _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
    save_image(snake_case__	,      snake_case__    )
    return (title, description, image_title)
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
   print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')
  
  
  
  
  
  