






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase     			=  logging.get_logger(__name__)

_UpperCamelCase     			=  {
    "nghuyong/ernie-1.0-base-zh": "https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-2.0-base-en": "https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json",
    "nghuyong/ernie-2.0-large-en": "https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json",
    "nghuyong/ernie-3.0-base-zh": "https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-medium-zh": "https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-mini-zh": "https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-micro-zh": "https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json",
    "nghuyong/ernie-3.0-nano-zh": "https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json",
    "nghuyong/ernie-gram-zh": "https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json",
    "nghuyong/ernie-health-zh": "https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json",
}







class 					lowerCamelCase__					(       snake_case_ ):
    SCREAMING_SNAKE_CASE		=			'ernie'






    def __init__( self	,A=30_522	,A=768	,A=12	,A=12	,A=3_072	,A="gelu"	,A=0.1	,A=0.1	,A=512	,A=2	,A=3	,A=False	,A=0.02	,A=1e-1_2	,A=0	,A="absolute"	,A=True	,A=None	,**A	,):
          super().__init__(pad_token_id=lowerCamelCase_	,**lowerCamelCase_					)

          UpperCAmelCase							=       vocab_size
          UpperCAmelCase							=       hidden_size
          UpperCAmelCase							=       num_hidden_layers
          UpperCAmelCase							=       num_attention_heads
          UpperCAmelCase							=       hidden_act
          UpperCAmelCase							=       intermediate_size
          UpperCAmelCase							=       hidden_dropout_prob
          UpperCAmelCase							=       attention_probs_dropout_prob
          UpperCAmelCase							=       max_position_embeddings
          UpperCAmelCase							=       type_vocab_size
          UpperCAmelCase							=       task_type_vocab_size
          UpperCAmelCase							=       use_task_id
          UpperCAmelCase							=       initializer_range
          UpperCAmelCase							=       layer_norm_eps
          UpperCAmelCase							=       position_embedding_type
          UpperCAmelCase							=       use_cache
          UpperCAmelCase							=       classifier_dropout







class 					lowerCamelCase__					(       snake_case_ ):






    @property
    def 				_UpperCamelCase							( self					):



          if self.task == "multiple-choice":
                UpperCAmelCase							=       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
                UpperCAmelCase							=       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ("""input_ids""", dynamic_axis),
                  ("""attention_mask""", dynamic_axis),
                  ("""token_type_ids""", dynamic_axis),
                  ("""task_type_ids""", dynamic_axis),
              ]					)











"""simple docstring"""
import bsa
import requests






def        lowerCamelCase_  (						_lowerCamelCase				):
 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_  (						_lowerCamelCase = 5				):
 lowerCamelCase__     :			Optional[int]     				=						int(float(_lowerCamelCase				)				)
 if num_movies < 1:
  return ()
 lowerCamelCase__     :			Any     				=						(
     'https://www.imdb.com/search/title?title_type='
     f'''feature&sort=num_votes,desc&count={num_movies}'''
 )
 lowerCamelCase__     :			Union[str, Any]     				=						bsa.BeautifulSoup(requests.get(_lowerCamelCase				).content			,   'html.parser'				)
 return tuple(
     get_movie_data_from_soup(_lowerCamelCase				)
     for movie in source.find_all('div'			,   class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
      import json

      A_			: 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)
          )
      )

