

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						=					{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class  a     (		snake_case_ ):
						_lowercase			  =	'rembert'


						def __init__(				self  ,    A_=250300  ,    A_=1152  ,    A_=32  ,    A_=18  ,    A_=256  ,    A_=1664  ,    A_=4608  ,    A_="gelu"  ,    A_=0.0  ,    A_=0.0  ,    A_=0.1  ,    A_=512  ,    A_=2  ,    A_=0.02  ,    A_=1e-12  ,    A_=True  ,    A_=0  ,    A_=312  ,    A_=313  ,    **A_  ,    ):







										'''simple docstring'''


										super().__init__(pad_token_id=lowerCamelCase_  ,    bos_token_id=lowerCamelCase_  ,    eos_token_id=lowerCamelCase_  ,    **lowerCamelCase_		)

										_UpperCAmelCase				:						Dict       						=			vocab_size
										_UpperCAmelCase				:						List[Any]       						=			input_embedding_size
										_UpperCAmelCase				:						Any       						=			output_embedding_size
										_UpperCAmelCase				:						Union[str, Any]       						=			max_position_embeddings
										_UpperCAmelCase				:						Optional[int]       						=			hidden_size
										_UpperCAmelCase				:						Union[str, Any]       						=			num_hidden_layers
										_UpperCAmelCase				:						int       						=			num_attention_heads
										_UpperCAmelCase				:						str       						=			intermediate_size
										_UpperCAmelCase				:						Dict       						=			hidden_act
										_UpperCAmelCase				:						Dict       						=			hidden_dropout_prob
										_UpperCAmelCase				:						Any       						=			attention_probs_dropout_prob
										_UpperCAmelCase				:						Union[str, Any]       						=			classifier_dropout_prob
										_UpperCAmelCase				:						List[Any]       						=			initializer_range
										_UpperCAmelCase				:						Union[str, Any]       						=			type_vocab_size
										_UpperCAmelCase				:						List[Any]       						=			layer_norm_eps
										_UpperCAmelCase				:						Dict       						=			use_cache
										_UpperCAmelCase				:						Any       						=			False







class  a     (		snake_case_ ):


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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





						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return 1e-4










"""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)
          )
      )

