
'''simple docstring'''



import bsa
import requests
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_  )   ->   dict[str, str]:




   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find("""span"""			,					class_="""genre"""  ).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"https://www.imdb.com{soup.a.get('href'  )}",
   }
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_ = 5  )   ->   tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = int(float(SCREAMING_SNAKE_CASE_  )  )
   if num_movies < 1:
      return ()
   _SCREAMING_SNAKE_CASE          = (
       """https://www.imdb.com/search/title?title_type="""
       F"feature&sort=num_votes,desc&count={num_movies}"
   )
   _SCREAMING_SNAKE_CASE          = bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_  ).content			,					"""html.parser"""  )
   return tuple(
       get_movie_data_from_soup(SCREAMING_SNAKE_CASE_  )
       for movie in source.find_all("""div"""			,					class_="""lister-item mode-advanced"""  )  )


if __name__ == "__main__":
     import json

     UpperCamelCase__					:						str											=     int(input("How many movies would you like to see? "))
     print(
         ", ".join(
             json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
         )
     )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__							= {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= ['DebertaV2TokenizerFast']

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFDebertaV2ForMaskedLM',
        'TFDebertaV2ForQuestionAnswering',
        'TFDebertaV2ForSequenceClassification',
        'TFDebertaV2ForTokenClassification',
        'TFDebertaV2Model',
        'TFDebertaV2PreTrainedModel',
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__							= [
        'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DebertaV2ForMaskedLM',
        'DebertaV2ForMultipleChoice',
        'DebertaV2ForQuestionAnswering',
        'DebertaV2ForSequenceClassification',
        'DebertaV2ForTokenClassification',
        'DebertaV2Model',
        'DebertaV2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deberta_va import (
        DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DebertaVaConfig,
        DebertaVaOnnxConfig,
    )
    from .tokenization_deberta_va import DebertaVaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta_va import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
            TFDebertaVaModel,
            TFDebertaVaPreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta_va import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaVaForMaskedLM,
            DebertaVaForMultipleChoice,
            DebertaVaForQuestionAnswering,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaModel,
            DebertaVaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
