





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_  							=				{'configuration_mmbt': ['MMBTConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
  from .configuration_mmbt import MMBTConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
  import sys

  UpperCAmelCase_  							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








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_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    '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'''


   a		:							Optional[int]     	=	"marian"
   a		:							Optional[Any]     	=	["past_key_values"]
   a		:							Union[str, Any]     	=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(					self,	__magic_name__=58101,	__magic_name__=None,	__magic_name__=1024,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=True,	__magic_name__=True,	__magic_name__="gelu",	__magic_name__=1024,	__magic_name__=0.1,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.02,	__magic_name__=58100,	__magic_name__=False,	__magic_name__=58100,	__magic_name__=0,	__magic_name__=0,	__magic_name__=True,	**__magic_name__,	)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   vocab_size
       UpperCamelCase__							:       Dict      =   decoder_vocab_size or vocab_size
       UpperCamelCase__							:       Any      =   max_position_embeddings
       UpperCamelCase__							:       Any      =   d_model
       UpperCamelCase__							:       Union[str, Any]      =   encoder_ffn_dim
       UpperCamelCase__							:       Optional[Any]      =   encoder_layers
       UpperCamelCase__							:       List[str]      =   encoder_attention_heads
       UpperCamelCase__							:       str      =   decoder_ffn_dim
       UpperCamelCase__							:       str      =   decoder_layers
       UpperCamelCase__							:       Optional[int]      =   decoder_attention_heads
       UpperCamelCase__							:       str      =   dropout
       UpperCamelCase__							:       Optional[Any]      =   attention_dropout
       UpperCamelCase__							:       List[Any]      =   activation_dropout
       UpperCamelCase__							:       int      =   activation_function
       UpperCamelCase__							:       Optional[int]      =   init_std
       UpperCamelCase__							:       Any      =   encoder_layerdrop
       UpperCamelCase__							:       Tuple      =   decoder_layerdrop
       UpperCamelCase__							:       Dict      =   use_cache
       UpperCamelCase__							:       str      =   encoder_layers
       UpperCamelCase__							:       str      =   scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__							:       Union[str, Any]      =   share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=__magic_name__,	eos_token_id=__magic_name__,	is_encoder_decoder=__magic_name__,	decoder_start_token_id=__magic_name__,	forced_eos_token_id=__magic_name__,	**__magic_name__,	)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       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__							:       List[str]      =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}
               UpperCamelCase__							:       Optional[Any]      =   {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(__magic_name__,	direction='''inputs'''							)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           UpperCamelCase__							:       Any      =   OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)
           if self.use_past:
               UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:       Tuple      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:       List[Any]      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
           UpperCamelCase__							:       Optional[Any]      =   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    UpperCamelCase__						(					self							)				->   Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       Optional[Any]      =   super().outputs
       else:
           UpperCamelCase__							:       Tuple      =   super(__magic_name__,	self							).outputs
           if self.use_past:
               UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:       str      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:       Optional[Any]      =   {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

       # Generate decoder inputs
       UpperCamelCase__							:       Optional[Any]      =   seq_length if not self.use_past else 1
       UpperCamelCase__							:       List[str]      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       UpperCamelCase__							:       Dict      =   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase__							:       Dict      =   dict(**__magic_name__,	**__magic_name__							)

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

           UpperCamelCase__							:       List[str]      =   torch.cat(
               [common_inputs['''decoder_attention_mask'''], torch.ones(__magic_name__,	__magic_name__							)],	dim=1							)

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

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

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

           UpperCamelCase__							:       int      =   common_inputs['''attention_mask'''].dtype
           UpperCamelCase__							:       Union[str, Any]      =   torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(__magic_name__,	__magic_name__,	dtype=__magic_name__							)],	dim=1							)
           UpperCamelCase__							:       Union[str, Any]      =   [
               (torch.zeros(__magic_name__							), torch.zeros(__magic_name__							)) for _ in range(__magic_name__							)
           ]
       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       # 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__							:       Any      =   compute_effective_axis_dimension(
           __magic_name__,	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__							:       Optional[int]      =   tokenizer.num_special_tokens_to_add(__magic_name__							)
       UpperCamelCase__							:       Dict      =   compute_effective_axis_dimension(
           __magic_name__,	fixed_dimension=OnnxConfig.default_fixed_sequence,	num_token_to_add=__magic_name__							)

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__							:       Dict      =   [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
       UpperCamelCase__							:       Optional[Any]      =   dict(tokenizer(__magic_name__,	return_tensors=__magic_name__							)							)
       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Mapping[str, Any]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       int      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       else:
           UpperCamelCase__							:       Optional[Any]      =   self._generate_dummy_inputs_for_causal_lm(
               __magic_name__,	batch_size=__magic_name__,	seq_length=__magic_name__,	is_pair=__magic_name__,	framework=__magic_name__							)

       return common_inputs




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:       List[str]      =   super()._flatten_past_key_values_(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)
       else:
           UpperCamelCase__							:       Dict      =   super(__magic_name__,	self							)._flatten_past_key_values_(
               __magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   float:






       """simple docstring"""
       return 1E-4

