

'''simple docstring'''






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


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  int							=			'marian'
  SCREAMING_SNAKE_CASE    :  Tuple							=			['past_key_values']
  SCREAMING_SNAKE_CASE    :  List[Any]							=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






  def __init__(      self   :     Any					,lowercase__   :     Tuple=5_8_1_0_1					,lowercase__   :     Any=None					,lowercase__   :     List[Any]=1_0_2_4					,lowercase__   :     Tuple=1_2					,lowercase__   :     Optional[int]=4_0_9_6					,lowercase__   :     Any=1_6					,lowercase__   :     Dict=1_2					,lowercase__   :     int=4_0_9_6					,lowercase__   :     Tuple=1_6					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=0.0					,lowercase__   :     int=True					,lowercase__   :     str=True					,lowercase__   :     Union[str, Any]="gelu"					,lowercase__   :     Optional[int]=1_0_2_4					,lowercase__   :     Optional[Any]=0.1					,lowercase__   :     List[Any]=0.0					,lowercase__   :     List[str]=0.0					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Dict=5_8_1_0_0					,lowercase__   :     List[Any]=False					,lowercase__   :     Dict=5_8_1_0_0					,lowercase__   :     List[str]=0					,lowercase__   :     Optional[Any]=0					,lowercase__   :     int=True					,**lowercase__   :     Any					,):
   __lowercase							  = vocab_size
   __lowercase							  = decoder_vocab_size or vocab_size
   __lowercase							  = max_position_embeddings
   __lowercase							  = d_model
   __lowercase							  = encoder_ffn_dim
   __lowercase							  = encoder_layers
   __lowercase							  = encoder_attention_heads
   __lowercase							  = decoder_ffn_dim
   __lowercase							  = decoder_layers
   __lowercase							  = decoder_attention_heads
   __lowercase							  = dropout
   __lowercase							  = attention_dropout
   __lowercase							  = activation_dropout
   __lowercase							  = activation_function
   __lowercase							  = init_std
   __lowercase							  = encoder_layerdrop
   __lowercase							  = decoder_layerdrop
   __lowercase							  = use_cache
   __lowercase							  = encoder_layers
   __lowercase							  = scale_embedding  # scale factor will be sqrt(d_model) if True
   __lowercase							  = share_encoder_decoder_embeddings
   super().__init__(
       pad_token_id=lowercase__					,eos_token_id=lowercase__					,is_encoder_decoder=lowercase__					,decoder_start_token_id=lowercase__					,forced_eos_token_id=lowercase__					,**lowercase__					,)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
        ]			)

    if self.use_past:
     __lowercase							  = {0: '''batch'''}
     __lowercase							  = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
    else:
     __lowercase							  = {0: '''batch''', 1: '''decoder_sequence'''}
     __lowercase							  = {0: '''batch''', 1: '''decoder_sequence'''}

    if self.use_past:
     self.fill_with_past_key_values_(lowercase__					,direction='''inputs'''			)
   elif self.task == "causal-lm":
    # TODO: figure this case out.
    __lowercase							  = OrderedDict(
        [
            ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
        ]			)
    if self.use_past:
     __lowercase  ,	__lowercase							  = self.num_layers
     for i in range(lowercase__			):
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
   else:
    __lowercase							  = 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
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = super().outputs
   else:
    __lowercase							  = super(lowercase__					,self			).outputs
    if self.use_past:
     __lowercase  ,	__lowercase							  = self.num_layers
     for i in range(lowercase__			):
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
      __lowercase							  = {0: '''batch''', 2: '''past_sequence + sequence'''}
   return common_outputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = None					,):
   __lowercase							  = self._generate_dummy_inputs_for_encoder_and_decoder(
       lowercase__					,lowercase__					,lowercase__					,lowercase__					,lowercase__			)

   # Generate decoder inputs
   __lowercase							  = seq_length if not self.use_past else 1
   __lowercase							  = self._generate_dummy_inputs_for_encoder_and_decoder(
       lowercase__					,lowercase__					,lowercase__					,lowercase__					,lowercase__			)
   __lowercase							  = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
   __lowercase							  = dict(**lowercase__					,**lowercase__			)

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

    __lowercase							  = torch.cat(
        [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase__					,lowercase__			)]					,dim=1			)

    __lowercase							  = []
    # If the number of encoder and decoder layers are present in the model configuration, both are considered
    __lowercase  ,	__lowercase							  = self.num_layers
    __lowercase							  = min(lowercase__					,lowercase__			)
    __lowercase							  = max(lowercase__					,lowercase__			) - min_num_layers
    __lowercase							  = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = None					,):
   __lowercase							  = self._generate_dummy_inputs_for_encoder_and_decoder(
       lowercase__					,lowercase__					,lowercase__					,lowercase__					,lowercase__			)

   if self.use_past:
    if not is_torch_available():
     raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''			)
    else:
     import torch
    __lowercase  ,	__lowercase							  = common_inputs['''input_ids'''].shape
    # Not using the same length for past_key_values
    __lowercase							  = seqlen + 2
    __lowercase  ,	__lowercase							  = self.num_layers
    __lowercase  ,	__lowercase							  = self.num_attention_heads
    __lowercase							  = (
        batch,
        num_encoder_attention_heads,
        past_key_values_length,
        self._config.hidden_size // num_encoder_attention_heads,
    )

    __lowercase							  = common_inputs['''attention_mask'''].dtype
    __lowercase							  = torch.cat(
        [common_inputs['''attention_mask'''], torch.ones(lowercase__					,lowercase__					,dtype=lowercase__			)]					,dim=1			)
    __lowercase							  = [
        (torch.zeros(lowercase__			), torch.zeros(lowercase__			)) for _ in range(lowercase__			)
    ]
   return common_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = 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
   __lowercase							  = compute_effective_axis_dimension(
       lowercase__					,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
   __lowercase							  = tokenizer.num_special_tokens_to_add(lowercase__			)
   __lowercase							  = compute_effective_axis_dimension(
       lowercase__					,fixed_dimension=OnnxConfig.default_fixed_sequence					,num_token_to_add=lowercase__			)

   # Generate dummy inputs according to compute batch and sequence
   __lowercase							  = [''' '''.join([tokenizer.unk_token]			) * seq_length] * batch_size
   __lowercase							  = dict(tokenizer(lowercase__					,return_tensors=lowercase__			)			)
   return common_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     PreTrainedTokenizer					,lowercase__   :     int = -1					,lowercase__   :     int = -1					,lowercase__   :     bool = False					,lowercase__   :     Optional[TensorType] = None					,):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
        lowercase__					,batch_size=lowercase__					,seq_length=lowercase__					,is_pair=lowercase__					,framework=lowercase__			)

   else:
    __lowercase							  = self._generate_dummy_inputs_for_causal_lm(
        lowercase__					,batch_size=lowercase__					,seq_length=lowercase__					,is_pair=lowercase__					,framework=lowercase__			)

   return common_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     Optional[int]					,lowercase__   :     int					,lowercase__   :     Optional[int]					,lowercase__   :     str			):
   if self.task in ["default", "seq2seq-lm"]:
    __lowercase							  = super()._flatten_past_key_values_(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
   else:
    __lowercase							  = super(lowercase__					,self			)._flatten_past_key_values_(
        lowercase__					,lowercase__					,lowercase__					,lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   return 1e-4




'''simple docstring'''






lowerCAmelCase__											=	'''Input must be a string of 8 numbers plus letter'''
lowerCAmelCase__											=	'''TRWAGMYFPDXBNJZSQVHLCKE'''


def 		_A     (    A__      ):






 """simple docstring"""



 if not isinstance(A__     ,				A__      ):
  __lowercase							  = F"Expected string as input, found {type(A__      ).__name__}"
  raise TypeError(A__      )

 __lowercase							  = spanish_id.replace('''-'''     ,				''''''      ).upper()
 if len(A__      ) != 9:
  raise ValueError(A__      )

 try:
  __lowercase							  = int(spanish_id_clean[0:8]      )
  __lowercase							  = spanish_id_clean[8]
 except ValueError as ex:
  raise ValueError(A__      ) from ex

 if letter.isdigit():
  raise ValueError(A__      )

 return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

