





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE = ""	)       ->     dict[str, float]:
  lowercase__			    =						url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  lowercase__			    =						BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE	).text	,		'html.parser'	)
  lowercase__			    =						soup.find_all('td'	,		attrs='titleColumn'	)
  lowercase__			    =						soup.find_all('td'	,		class_='ratingColumn imdbRating'	)
  return {
      title.a.text: float(rating.strong.text	)
      for title, rating in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
  }




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE = "IMDb_Top_250_Movies.csv"	)       ->     None:
  lowercase__			    =						get_imdb_top_aaa_movies()
  with open(_SCREAMING_SNAKE_CASE	,		'w'	,		newline=''	) as out_file:
    lowercase__			    =						csv.writer(_SCREAMING_SNAKE_CASE	)
    writer.writerow(['Movie title', 'IMDb rating']	)
    for title, rating in movies.items():
      writer.writerow([title, rating]	)


if __name__ == "__main__":
      write_movies()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_					     =	{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	["""SqueezeBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      lowercase_					     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


