code: 
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  SCREAMING_SNAKE_CASE__				:    str         =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    {
      """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  class 		_SCREAMING_SNAKE_CASE    (	UpperCAmelCase__    ):
       __SCREAMING_SNAKE_CASE    				=						'mbart'
       __SCREAMING_SNAKE_CASE    				=						['past_key_values']
       __SCREAMING_SNAKE_CASE    				=						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
  
       def __init__(     self       ,				A_=5_02_65       ,				A_=10_24       ,				A_=12       ,				A_=40_96       ,				A_=16       ,				A_=12       ,				A_=40_96       ,				A_=16       ,				A_=0.0       ,				A_=0.0       ,				A_=True       ,				A_=True       ,				A_="gelu"       ,				A_=10_24       ,				A_=0.1       ,				A_=0.0       ,				A_=0.0       ,				A_=0.0_2       ,				A_=0.0       ,				A_=False       ,				A_=1       ,				A_=0       ,				A_=2       ,				A_=2       ,				**A_       ,				):
         _UpperCAmelCase		:       Any       =    vocab_size
         _UpperCAmelCase		:       List[Any]       =    max_position_embeddings
         _UpperCAmelCase		:       Optional[int]       =    d_model
         _UpperCAmelCase		:       List[str]       =    encoder_ffn_dim
         _UpperCAmelCase		:       Union[str, Any]       =    encoder_layers
         _UpperCAmelCase		:       List[str]       =    encoder_attention_heads
         _UpperCAmelCase		:       Union[str, Any]       =    decoder_ffn_dim
         _UpperCAmelCase		:       Optional[Any]       =    decoder_layers
         _UpperCAmelCase		:       List[Any]       =    decoder_attention_heads
         _UpperCAmelCase		:       Tuple       =    dropout
         _UpperCAmelCase		:       Any       =    attention_dropout
         _UpperCAmelCase		:       List[Any]       =    activation_dropout
         _UpperCAmelCase		:       List[str]       =    activation_function
         _UpperCAmelCase		:       List[str]       =    init_std
         _UpperCAmelCase		:       Dict       =    encoder_layerdrop
         _UpperCAmelCase		:       Any       =    decoder_layerdrop
         _UpperCAmelCase		:       Dict       =    classifier_dropout
         _UpperCAmelCase		:       Optional[Any]       =    use_cache
         _UpperCAmelCase		:       List[str]       =    encoder_layers
         _UpperCAmelCase		:       Tuple       =    scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=lowerCamelCase__       ,				bos_token_id=lowerCamelCase__       ,				eos_token_id=lowerCamelCase__       ,				is_encoder_decoder=lowerCamelCase__       ,				forced_eos_token_id=lowerCamelCase__       ,				**lowerCamelCase__       ,				)
  
  
  class 		_SCREAMING_SNAKE_CASE    (	UpperCAmelCase__    ):
  
  
       @property
       def 					__snake_case(     self						):
         if self.task in ["default", "seq2seq-lm"]:
           _UpperCAmelCase		:       str       =    OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]						)
  
           if self.use_past:
             _UpperCAmelCase		:       str       =    {0: "batch"}
             _UpperCAmelCase		:       Tuple       =    {0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
             _UpperCAmelCase		:       Optional[Any]       =    {0: "batch", 1: "decoder_sequence"}
             _UpperCAmelCase		:       Dict       =    {0: "batch", 1: "decoder_sequence"}
  
           if self.use_past:
             self.fill_with_past_key_values_(lowerCamelCase__       ,				direction="""inputs"""						)
         elif self.task == "causal-lm":
           # TODO: figure this case out.
           _UpperCAmelCase		:       Dict       =    OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]						)
           if self.use_past:
             _UpperCAmelCase		:       Optional[Any]       =    self.num_layers
             for i in range(lowerCamelCase__						):
               _UpperCAmelCase		:       Dict       =    {0: "batch", 2: "past_sequence + sequence"}
               _UpperCAmelCase		:       List[str]       =    {0: "batch", 2: "past_sequence + sequence"}
         else:
           _UpperCAmelCase		:       int       =    OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                   ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
               ]						)
  
         return common_inputs
  
  
       @property
       def 					__snake_case(     self						):
         if self.task in ["default", "seq2seq-lm"]:
           _UpperCAmelCase		:       Tuple       =    super().outputs
         else:
           _UpperCAmelCase		:       Union[str, Any]       =    super(lowerCamelCase__       ,				self						).outputs
           if self.use_past:
             _UpperCAmelCase		:       int       =    self.num_layers
             for i in range(lowerCamelCase__						):
               _UpperCAmelCase		:       Union[str, Any]       =    {0: "batch", 2: "past_sequence + sequence"}
               _UpperCAmelCase		:       int       =    {0: "batch", 2: "past_sequence + sequence"}
         return common_outputs
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = -1       ,				A_ = -1       ,				A_ = False       ,				A_ = None       ,				):
         _UpperCAmelCase		:       Optional[Any]       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__						)
  
         # Generate decoder inputs
         _UpperCAmelCase		:       str       =    seq_length if not self.use_past else 1
         _UpperCAmelCase		:       str       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__						)
         _UpperCAmelCase		:       List[Any]       =    {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
         _UpperCAmelCase		:       Tuple       =    dict(**lowerCamelCase__       ,				**lowerCamelCase__						)
  
         if self.use_past:
           if not is_torch_available():
             raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
           else:
             import torch
           _UpperCAmelCase		:       Tuple       =    common_inputs["input_ids"].shape
           _UpperCAmelCase		:       str       =    common_inputs["decoder_input_ids"].shape[1]
           _UpperCAmelCase		:       str       =    self.num_attention_heads
           _UpperCAmelCase		:       Any       =    (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           _UpperCAmelCase		:       Optional[int]       =    decoder_seq_length + 3
           _UpperCAmelCase		:       Any       =    (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
  
           _UpperCAmelCase		:       Tuple       =    torch.cat(
               [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase__       ,				lowerCamelCase__						)]       ,				dim=1						)
  
           _UpperCAmelCase		:       Tuple       =    []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           _UpperCAmelCase		:       Union[str, Any]       =    self.num_layers
           _UpperCAmelCase		:       Optional[int]       =    min(lowerCamelCase__       ,				lowerCamelCase__						)
           _UpperCAmelCase		:       Dict       =    max(lowerCamelCase__       ,				lowerCamelCase__						) - min_num_layers
           _UpperCAmelCase		:       List[str]       =    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
  
           for _ in range(lowerCamelCase__						):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(lowerCamelCase__						),
                     torch.zeros(lowerCamelCase__						),
                     torch.zeros(lowerCamelCase__						),
                     torch.zeros(lowerCamelCase__						),
                 )						)
           # TODO: test this.
           _UpperCAmelCase		:       Dict       =    encoder_shape if remaining_side_name == "encoder" else decoder_shape
           for _ in range(lowerCamelCase__       ,				lowerCamelCase__						):
             common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase__						), torch.zeros(lowerCamelCase__						))						)
         return common_inputs
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = -1       ,				A_ = -1       ,				A_ = False       ,				A_ = None       ,				):
         _UpperCAmelCase		:       Any       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__						)
  
         if self.use_past:
           if not is_torch_available():
             raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""						)
           else:
             import torch
           _UpperCAmelCase		:       List[Any]       =    common_inputs["input_ids"].shape
           # Not using the same length for past_key_values
           _UpperCAmelCase		:       str       =    seqlen + 2
           _UpperCAmelCase		:       Union[str, Any]       =    self.num_layers
           _UpperCAmelCase		:       Dict       =    self.num_attention_heads
           _UpperCAmelCase		:       Any       =    (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
  
           _UpperCAmelCase		:       List[str]       =    common_inputs["attention_mask"].dtype
           _UpperCAmelCase		:       Optional[Any]       =    torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase__       ,				lowerCamelCase__       ,				dtype=lowerCamelCase__						)]       ,				dim=1						)
           _UpperCAmelCase		:       str       =    [
               (torch.zeros(lowerCamelCase__						), torch.zeros(lowerCamelCase__						)) for _ in range(lowerCamelCase__						)
           ]
         return common_inputs
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = -1       ,				A_ = -1       ,				A_ = False       ,				A_ = None       ,				):
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         _UpperCAmelCase		:       Dict       =    compute_effective_axis_dimension(
             lowerCamelCase__       ,				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
         _UpperCAmelCase		:       int       =    tokenizer.num_special_tokens_to_add(lowerCamelCase__						)
         _UpperCAmelCase		:       Tuple       =    compute_effective_axis_dimension(
             lowerCamelCase__       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=lowerCamelCase__						)
  
         # Generate dummy inputs according to compute batch and sequence
         _UpperCAmelCase		:       Union[str, Any]       =    [" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
         _UpperCAmelCase		:       int       =    dict(tokenizer(lowerCamelCase__       ,				return_tensors=lowerCamelCase__						)						)
         return common_inputs
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = -1       ,				A_ = -1       ,				A_ = False       ,				A_ = None       ,				):
         if self.task in ["default", "seq2seq-lm"]:
           _UpperCAmelCase		:       List[str]       =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               lowerCamelCase__       ,				batch_size=lowerCamelCase__       ,				seq_length=lowerCamelCase__       ,				is_pair=lowerCamelCase__       ,				framework=lowerCamelCase__						)
  
         elif self.task == "causal-lm":
           _UpperCAmelCase		:       Optional[Any]       =    self._generate_dummy_inputs_for_causal_lm(
               lowerCamelCase__       ,				batch_size=lowerCamelCase__       ,				seq_length=lowerCamelCase__       ,				is_pair=lowerCamelCase__       ,				framework=lowerCamelCase__						)
         else:
           _UpperCAmelCase		:       Any       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowerCamelCase__       ,				batch_size=lowerCamelCase__       ,				seq_length=lowerCamelCase__       ,				is_pair=lowerCamelCase__       ,				framework=lowerCamelCase__						)
  
         return common_inputs
  
  
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_						):
         if self.task in ["default", "seq2seq-lm"]:
           _UpperCAmelCase		:       Optional[int]       =    super()._flatten_past_key_values_(lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__						)
         else:
           _UpperCAmelCase		:       Tuple       =    super(lowerCamelCase__       ,				self						)._flatten_past_key_values_(
               lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__       ,				lowerCamelCase__						)
  
  
  
  
  
  
style_context: 
  
  
  import os
  
  import requests
  from bsa import BeautifulSoup
  from fake_useragent import UserAgent
  
  SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
  SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
    _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
    with open(snake_case__	,      """wb"""    ) as file:
      file.write(image.content    )
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
    _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
    _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
    _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
    _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
    save_image(snake_case__	,      snake_case__    )
    return (title, description, image_title)
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
   print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')
  
  
  
  
  
  