






'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__A     						= logging.get_logger(__name__)

__A     						= {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}




class a_     (     UpperCamelCase_				):
   _snake_case	   =				"""m2m_100"""
   _snake_case	   =				["""past_key_values"""]
   _snake_case	   =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







   def __init__(self       , __a=1_2_8_1_1_2       , __a=1_0_2_4       , __a=1_2       , __a=4_0_9_6       , __a=1_6       , __a=1_2       , __a=4_0_9_6       , __a=1_6       , __a=0.05       , __a=0.05       , __a=True       , __a=True       , __a="relu"       , __a=1_0_2_4       , __a=0.1       , __a=0.1       , __a=0.0       , __a=0.02       , __a=2       , __a=True       , __a=1       , __a=0       , __a=2       , **__a       , )      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			vocab_size
      __snake_case					:   Any  				=			max_position_embeddings
      __snake_case					:   Union[str, Any]  				=			d_model
      __snake_case					:   Any  				=			encoder_ffn_dim
      __snake_case					:   Any  				=			encoder_layers
      __snake_case					:   Dict  				=			encoder_attention_heads
      __snake_case					:   Any  				=			decoder_ffn_dim
      __snake_case					:   Any  				=			decoder_layers
      __snake_case					:   Any  				=			decoder_attention_heads
      __snake_case					:   List[Any]  				=			dropout
      __snake_case					:   Union[str, Any]  				=			attention_dropout
      __snake_case					:   Optional[Any]  				=			activation_dropout
      __snake_case					:   Tuple  				=			activation_function
      __snake_case					:   List[Any]  				=			init_std
      __snake_case					:   Dict  				=			encoder_layerdrop
      __snake_case					:   Tuple  				=			decoder_layerdrop
      __snake_case					:   Optional[Any]  				=			use_cache
      __snake_case					:   str  				=			encoder_layers
      __snake_case					:   Optional[Any]  				=			scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=__a       , bos_token_id=__a       , eos_token_id=__a       , is_encoder_decoder=__a       , decoder_start_token_id=__a       , **__a       , )




class a_     (     UpperCamelCase_				):







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Mapping[str, Mapping[int, str]]:
      """simple docstring"""






      __snake_case					:   Dict  				=			OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ])

      if self.use_past:
         __snake_case					:   Union[str, Any]  				=			{0: 'batch'}
         __snake_case					:   Optional[Any]  				=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
         __snake_case					:   Optional[Any]  				=			{0: 'batch', 1: 'decoder_sequence'}
         __snake_case					:   str  				=			{0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
         self.fill_with_past_key_values_(__a       , direction='inputs')
      return common_inputs







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a = -1       , __a = -1       , __a = False       , __a = None       , )      ->   Mapping[str, Any]:
      """simple docstring"""






      __snake_case					:   int  				=			compute_effective_axis_dimension(
          __a       , fixed_dimension=OnnxConfig.default_fixed_batch       , num_token_to_add=0)

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __snake_case					:   str  				=			tokenizer.num_special_tokens_to_add(__a)
      __snake_case					:   Optional[int]  				=			compute_effective_axis_dimension(
          __a       , fixed_dimension=OnnxConfig.default_fixed_sequence       , num_token_to_add=__a)

      # Generate dummy inputs according to compute batch and sequence
      __snake_case					:   List[str]  				=			[' '.join([tokenizer.unk_token]) * seq_length] * batch_size
      __snake_case					:   Tuple  				=			dict(tokenizer(__a       , return_tensors=__a))
      return common_inputs







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a = -1       , __a = -1       , __a = False       , __a = None       , )      ->   Mapping[str, Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __a       , __a       , __a       , __a       , __a)

      # Generate decoder inputs
      __snake_case					:   Optional[int]  				=			seq_length if not self.use_past else 1
      __snake_case					:   int  				=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __a       , __a       , __a       , __a       , __a)
      __snake_case					:   Optional[int]  				=			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
      __snake_case					:   Optional[Any]  				=			dict(**__a       , **__a)

      if self.use_past:
         if not is_torch_available():
            raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
         else:
            import torch
         __snake_case				,__snake_case					:   List[str]  				=			common_inputs['input_ids'].shape
         __snake_case					:   int  				=			common_inputs['decoder_input_ids'].shape[1]
         __snake_case				,__snake_case					:   Tuple  				=			self.num_attention_heads
         __snake_case					:   Optional[int]  				=			(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         __snake_case					:   Optional[Any]  				=			decoder_seq_length + 3
         __snake_case					:   Tuple  				=			(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         __snake_case					:   str  				=			torch.cat(
             [common_inputs['decoder_attention_mask'], torch.ones(__a       , __a)]       , dim=1)

         __snake_case					:   Union[str, Any]  				=			[]
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         __snake_case				,__snake_case					:   Any  				=			self.num_layers
         __snake_case					:   Dict  				=			min(__a       , __a)
         __snake_case					:   Tuple  				=			max(__a       , __a) - min_num_layers
         __snake_case					:   int  				=			'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

         for _ in range(__a):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(__a),
                    torch.zeros(__a),
                    torch.zeros(__a),
                    torch.zeros(__a),
                ))
         # TODO: test this.
         __snake_case					:   Tuple  				=			encoder_shape if remaining_side_name == 'encoder' else decoder_shape
         for _ in range(__a       , __a):
            common_inputs["past_key_values"].append((torch.zeros(__a), torch.zeros(__a)))
      return common_inputs


   _snake_case	   =				_generate_dummy_inputs_for_default_and_seqaseq_lm







'''simple docstring'''






import bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			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 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







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


if __name__ == "__main__":
      import json

      __A     						= 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)
          )
      )