






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     :							Dict    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''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       __a  (   __SCREAMING_SNAKE_CASE	):
 _lowerCAmelCase	:	List[str]				=			'marian'
 _lowerCAmelCase	:	Any				=			['past_key_values']
 _lowerCAmelCase	:	int				=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







 def __init__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Dict=5_81_01							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Optional[int]=12							,       SCREAMING_SNAKE_CASE :       str=40_96							,       SCREAMING_SNAKE_CASE :       Dict=16							,       SCREAMING_SNAKE_CASE :       Tuple=12							,       SCREAMING_SNAKE_CASE :       List[Any]=40_96							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=16							,       SCREAMING_SNAKE_CASE :       Optional[int]=0.0							,       SCREAMING_SNAKE_CASE :       int=0.0							,       SCREAMING_SNAKE_CASE :       Optional[int]=True							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       str="gelu"							,       SCREAMING_SNAKE_CASE :       Optional[int]=10_24							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=0.1							,       SCREAMING_SNAKE_CASE :       int=0.0							,       SCREAMING_SNAKE_CASE :       Dict=0.0							,       SCREAMING_SNAKE_CASE :       Dict=0.0_2							,       SCREAMING_SNAKE_CASE :       Any=5_81_00							,       SCREAMING_SNAKE_CASE :       Optional[Any]=False							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=5_81_00							,       SCREAMING_SNAKE_CASE :       Optional[int]=0							,       SCREAMING_SNAKE_CASE :       List[str]=0							,       SCREAMING_SNAKE_CASE :       List[Any]=True							,       **SCREAMING_SNAKE_CASE :       Optional[Any]							,       ):


      '''simple docstring'''






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







class       __a  (   __SCREAMING_SNAKE_CASE	):







 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__      :       int	    =  OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]						)

           if self.use_past:
                UpperCamelCase__      :       Dict	    =  {0: "batch"}
                UpperCamelCase__      :       str	    =  {0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
                UpperCamelCase__      :       int	    =  {0: "batch", 1: "decoder_sequence"}
                UpperCamelCase__      :       Tuple	    =  {0: "batch", 1: "decoder_sequence"}

           if self.use_past:
                self.fill_with_past_key_values_(_a							,       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__      :       str	    =  self.num_layers
                for i in range(_a						):
                     UpperCamelCase__      :       Any	    =  {0: "batch", 2: "past_sequence + sequence"}
                     UpperCamelCase__      :       int	    =  {0: "batch", 2: "past_sequence + sequence"}
      else:
           UpperCamelCase__      :       List[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
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = -1							,       SCREAMING_SNAKE_CASE :       Optional[Any] = -1							,       SCREAMING_SNAKE_CASE :       Optional[int] = False							,       SCREAMING_SNAKE_CASE :       List[str] = None							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  self._generate_dummy_inputs_for_encoder_and_decoder(
          _a							,       _a							,       _a							,       _a							,       _a						)

      # Generate decoder inputs
      UpperCamelCase__      :       Tuple	    =  seq_length if not self.use_past else 1
      UpperCamelCase__      :       str	    =  self._generate_dummy_inputs_for_encoder_and_decoder(
          _a							,       _a							,       _a							,       _a							,       _a						)
      UpperCamelCase__      :       Union[str, Any]	    =  {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
      UpperCamelCase__      :       str	    =  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
           UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  common_inputs["input_ids"].shape
           UpperCamelCase__      :       Optional[int]	    =  common_inputs["decoder_input_ids"].shape[1]
           UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  self.num_attention_heads
           UpperCamelCase__      :       Dict	    =  (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCamelCase__      :       Union[str, Any]	    =  decoder_seq_length + 3
           UpperCamelCase__      :       Tuple	    =  (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           UpperCamelCase__      :       Dict	    =  torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(_a							,       _a						)]							,       dim=1						)

           UpperCamelCase__      :       Any	    =  []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  self.num_layers
           UpperCamelCase__      :       Any	    =  min(_a							,       _a						)
           UpperCamelCase__      :       Optional[int]	    =  max(_a							,       _a						) - min_num_layers
           UpperCamelCase__      :       List[str]	    =  "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.
           UpperCamelCase__      :       Union[str, Any]	    =  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







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Optional[Any] = -1							,       SCREAMING_SNAKE_CASE :       List[str] = -1							,       SCREAMING_SNAKE_CASE :       List[Any] = False							,       SCREAMING_SNAKE_CASE :       Union[str, Any] = None							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  self._generate_dummy_inputs_for_encoder_and_decoder(
          _a							,       _a							,       _a							,       _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
           UpperCamelCase__    ,					UpperCamelCase__      :       int	    =  common_inputs["input_ids"].shape
           # Not using the same length for past_key_values
           UpperCamelCase__      :       Any	    =  seqlen + 2
           UpperCamelCase__    ,					UpperCamelCase__      :       str	    =  self.num_layers
           UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  self.num_attention_heads
           UpperCamelCase__      :       List[str]	    =  (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           UpperCamelCase__      :       Optional[Any]	    =  common_inputs["attention_mask"].dtype
           UpperCamelCase__      :       str	    =  torch.cat(
               [common_inputs["attention_mask"], torch.ones(_a							,       _a							,       dtype=_a						)]							,       dim=1						)
           UpperCamelCase__      :       Optional[Any]	    =  [
               (torch.zeros(_a						), torch.zeros(_a						)) for _ in range(_a						)
           ]
      return common_inputs







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       int = -1							,       SCREAMING_SNAKE_CASE :       Union[str, Any] = -1							,       SCREAMING_SNAKE_CASE :       int = False							,       SCREAMING_SNAKE_CASE :       Tuple = None							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  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
      UpperCamelCase__      :       List[Any]	    =  tokenizer.num_special_tokens_to_add(_a						)
      UpperCamelCase__      :       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
      UpperCamelCase__      :       Union[str, Any]	    =  [" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
      UpperCamelCase__      :       List[Any]	    =  dict(tokenizer(_a							,       return_tensors=_a						)						)
      return common_inputs







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Optional[int] = -1							,       SCREAMING_SNAKE_CASE :       Optional[Any] = -1							,       SCREAMING_SNAKE_CASE :       List[str] = False							,       SCREAMING_SNAKE_CASE :       str = None							,       ):


      '''simple docstring'''






      if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__      :       Any	    =  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               _a							,       batch_size=_a							,       seq_length=_a							,       is_pair=_a							,       framework=_a						)

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

      return common_inputs







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Tuple							,       SCREAMING_SNAKE_CASE :       Dict						):


      '''simple docstring'''






      if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__      :       str	    =  super()._flatten_past_key_values_(_a							,       _a							,       _a							,       _a						)
      else:
           UpperCamelCase__      :       Any	    =  super(_a							,       self						)._flatten_past_key_values_(
               _a							,       _a							,       _a							,       _a						)







 @property
 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      return 1e-4







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Dict    =logging.get_logger(__name__)





class       __a  (   A__	):
 _lowerCAmelCase	:	Optional[int]				=			'''timm_backbone'''







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=3							,       SCREAMING_SNAKE_CASE :       List[Any]=True							,       SCREAMING_SNAKE_CASE :       Dict=True							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  backbone
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       str	    =  features_only
      UpperCamelCase__      :       Dict	    =  use_pretrained_backbone
      UpperCamelCase__      :       Tuple	    =  True
      UpperCamelCase__      :       List[Any]	    =  out_indices if out_indices is not None else (-1,)