





'''simple docstring'''

from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def  a__    (    lowercase :					str = ""				)		->		dict[str, float]:





     """simple docstring"""

     _UpperCamelCase       =	url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
     _UpperCamelCase       =	BeautifulSoup(requests.get(lowercase				).text, '''html.parser'''				)
     _UpperCamelCase       =	soup.find_all('''td''', attrs='''titleColumn'''				)
     _UpperCamelCase       =	soup.find_all('''td''', class_='''ratingColumn imdbRating'''				)
     return {
         title.a.text: float(rating.strong.text				)
         for title, rating in zip(lowercase, lowercase				)
     }







def  a__    (    lowercase :					str = "IMDb_Top_250_Movies.csv"				)		->		None:





     """simple docstring"""

     _UpperCamelCase       =	get_imdb_top_aaa_movies()
     with open(lowercase, '''w''', newline=''''''				) as out_file:
          _UpperCamelCase       =	csv.writer(lowercase				)
          writer.writerow(['''Movie title''', '''IMDb rating''']				)
          for title, rating in movies.items():
               writer.writerow([title, rating]				)


if __name__ == "__main__":
   write_movies()










'''simple docstring'''

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__						:      Optional[Any]	=    {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      List[Any]	=    [
       'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'AutoformerForPrediction',
       'AutoformerModel',
       'AutoformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_autoformer import (
       AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AutoformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_autoformer import (
          AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          AutoformerForPrediction,
          AutoformerModel,
          AutoformerPreTrainedModel,
      )

else:
   import sys

   lowercase__						:      Tuple	=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



