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
  
  
  UpperCAmelCase			:			Tuple	  = logging.get_logger(__name__)
  
  UpperCAmelCase			:			Union[str, Any]	  = {
      """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        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      Any        =    """mbart"""
        _lowercase       :      Union[str, Any]        =    ["""past_key_values"""]
        _lowercase       :      List[str]        =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=5_0_2_6_5	,   lowerCAmelCase__=1_0_2_4	,   lowerCAmelCase__=1_2	,   lowerCAmelCase__=4_0_9_6	,   lowerCAmelCase__=1_6	,   lowerCAmelCase__=1_2	,   lowerCAmelCase__=4_0_9_6	,   lowerCAmelCase__=1_6	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=True	,   lowerCAmelCase__="gelu"	,   lowerCAmelCase__=1_0_2_4	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=0.02	,   lowerCAmelCase__=0.0	,   lowerCAmelCase__=False	,   lowerCAmelCase__=1	,   lowerCAmelCase__=0	,   lowerCAmelCase__=2	,   lowerCAmelCase__=2	,   **lowerCAmelCase__	,   )     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Union[str, Any]            =vocab_size
          a__			:      str            =max_position_embeddings
          a__			:      Any            =d_model
          a__			:      int            =encoder_ffn_dim
          a__			:      Union[str, Any]            =encoder_layers
          a__			:      Optional[int]            =encoder_attention_heads
          a__			:      Any            =decoder_ffn_dim
          a__			:      List[str]            =decoder_layers
          a__			:      Dict            =decoder_attention_heads
          a__			:      int            =dropout
          a__			:      Optional[int]            =attention_dropout
          a__			:      Dict            =activation_dropout
          a__			:      Dict            =activation_function
          a__			:      List[Any]            =init_std
          a__			:      int            =encoder_layerdrop
          a__			:      Optional[int]            =decoder_layerdrop
          a__			:      Union[str, Any]            =classifier_dropout
          a__			:      Dict            =use_cache
          a__			:      int            =encoder_layers
          a__			:      Optional[int]            =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        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if self.task in ["default", "seq2seq-lm"]:
            a__			:      Optional[Any]            =OrderedDict(
                [
                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ]							)
  
            if self.use_past:
              a__			:      Optional[int]            ={0: "batch"}
              a__			:      Optional[Any]            ={0: "batch", 1: "past_decoder_sequence + sequence"}
            else:
              a__			:      int            ={0: "batch", 1: "decoder_sequence"}
              a__			:      int            ={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.
            a__			:      Dict            =OrderedDict(
                [
                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ]							)
            if self.use_past:
              a__      ,							a__			:      Optional[Any]            =self.num_layers
              for i in range(lowerCAmelCase__							):
                a__			:      Optional[Any]            ={0: "batch", 2: "past_sequence + sequence"}
                a__			:      Optional[Any]            ={0: "batch", 2: "past_sequence + sequence"}
          else:
            a__			:      str            =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      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if self.task in ["default", "seq2seq-lm"]:
            a__			:      Tuple            =super().outputs
          else:
            a__			:      Any            =super(lowerCAmelCase__	,   self							).outputs
            if self.use_past:
              a__      ,							a__			:      str            =self.num_layers
              for i in range(lowerCAmelCase__							):
                a__			:      str            ={0: "batch", 2: "past_sequence + sequence"}
                a__			:      Dict            ={0: "batch", 2: "past_sequence + sequence"}
          return common_outputs
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = None	,   )     ->		Mapping[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
  
          # Generate decoder inputs
          a__			:      List[str]            =seq_length if not self.use_past else 1
          a__			:      str            =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
          a__			:      str            ={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          a__			:      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
            a__      ,							a__			:      Union[str, Any]            =common_inputs["input_ids"].shape
            a__			:      Optional[int]            =common_inputs["decoder_input_ids"].shape[1]
            a__      ,							a__			:      Tuple            =self.num_attention_heads
            a__			:      Dict            =(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            a__			:      Tuple            =decoder_seq_length + 3
            a__			:      List[str]            =(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )
  
            a__			:      Tuple            =torch.cat(
                [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase__	,   lowerCAmelCase__							)]	,   dim=1							)
  
            a__			:      List[Any]            =[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            a__      ,							a__			:      Union[str, Any]            =self.num_layers
            a__			:      Any            =min(lowerCAmelCase__	,   lowerCAmelCase__							)
            a__			:      Union[str, Any]            =max(lowerCAmelCase__	,   lowerCAmelCase__							) - min_num_layers
            a__			:      Optional[Any]            ="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.
            a__			:      str            =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      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = None	,   )     ->		Mapping[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      str            =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
            a__      ,							a__			:      Dict            =common_inputs["input_ids"].shape
            # Not using the same length for past_key_values
            a__			:      Tuple            =seqlen + 2
            a__      ,							a__			:      str            =self.num_layers
            a__      ,							a__			:      int            =self.num_attention_heads
            a__			:      List[Any]            =(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
  
            a__			:      str            =common_inputs["attention_mask"].dtype
            a__			:      List[Any]            =torch.cat(
                [common_inputs["attention_mask"], torch.ones(lowerCAmelCase__	,   lowerCAmelCase__	,   dtype=lowerCAmelCase__							)]	,   dim=1							)
            a__			:      List[Any]            =[
                (torch.zeros(lowerCAmelCase__							), torch.zeros(lowerCAmelCase__							)) for _ in range(lowerCAmelCase__							)
            ]
          return common_inputs
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = None	,   )     ->		Mapping[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =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
          a__			:      Any            =tokenizer.num_special_tokens_to_add(lowerCAmelCase__							)
          a__			:      Optional[Any]            =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
          a__			:      Optional[int]            =[" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
          a__			:      Optional[int]            =dict(tokenizer(lowerCAmelCase__	,   return_tensors=lowerCAmelCase__							)							)
          return common_inputs
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = -1	,   lowerCAmelCase__ = False	,   lowerCAmelCase__ = None	,   )     ->		Mapping[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if self.task in ["default", "seq2seq-lm"]:
            a__			:      Optional[int]            =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":
            a__			:      Any            =self._generate_dummy_inputs_for_causal_lm(
                lowerCAmelCase__	,   batch_size=lowerCAmelCase__	,   seq_length=lowerCAmelCase__	,   is_pair=lowerCAmelCase__	,   framework=lowerCAmelCase__							)
          else:
            a__			:      Dict            =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      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if self.task in ["default", "seq2seq-lm"]:
            a__			:      Optional[Any]            =super()._flatten_past_key_values_(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
          else:
            a__			:      str            =super(lowerCAmelCase__	,   self							)._flatten_past_key_values_(
                lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    barcode //= 10  # exclude the last digit
    a__			:      int            =False
    a__			:      List[str]            =0
  
    # extract and check each digit
    while barcode != 0:
      a__			:      Optional[Any]            =1 if checker else 3
      s += mult * (barcode % 10)
      barcode //= 10
      a__			:      Union[str, Any]            =not checker
  
    return (10 - (s % 10)) % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    return len(str(SCREAMING_SNAKE_CASE		)		) == 13 and get_check_digit(SCREAMING_SNAKE_CASE		) == barcode % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if str(SCREAMING_SNAKE_CASE		).isalpha():
      a__			:      Tuple            =f'''Barcode \'{barcode}\' has alphabetic characters.'''
      raise ValueError(SCREAMING_SNAKE_CASE		)
    elif int(SCREAMING_SNAKE_CASE		) < 0:
      raise ValueError("The entered barcode has a negative value. Try again."		)
    else:
      return int(SCREAMING_SNAKE_CASE		)
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
       UpperCAmelCase			:			List[str]	  = get_barcode(input("""Barcode: """).strip())
  
       if is_valid(barcode):
            print(F"""'{barcode}' is a valid barcode.""")
       else:
            print(F"""'{barcode}' is NOT a valid barcode.""")
  
  
  
  
  
  
  