code: 
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							{
      """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
      # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
  }
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
       __lowerCamelCase										=     'm2m_100'
       __lowerCamelCase										=     ['past_key_values']
       __lowerCamelCase										=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase=128112  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=12  ,	_lowerCAmelCase=4096  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=12  ,	_lowerCAmelCase=4096  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=0.0_5  ,	_lowerCAmelCase=0.0_5  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase="relu"  ,	_lowerCAmelCase=1024  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=0.0  ,	_lowerCAmelCase=0.0_2  ,	_lowerCAmelCase=2  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=1  ,	_lowerCAmelCase=0  ,	_lowerCAmelCase=2  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     str										= vocab_size
            UpperCAmelCase__			:     Tuple										= max_position_embeddings
            UpperCAmelCase__			:     Tuple										= d_model
            UpperCAmelCase__			:     str										= encoder_ffn_dim
            UpperCAmelCase__			:     Optional[int]										= encoder_layers
            UpperCAmelCase__			:     int										= encoder_attention_heads
            UpperCAmelCase__			:     Optional[Any]										= decoder_ffn_dim
            UpperCAmelCase__			:     Optional[Any]										= decoder_layers
            UpperCAmelCase__			:     List[str]										= decoder_attention_heads
            UpperCAmelCase__			:     int										= dropout
            UpperCAmelCase__			:     Any										= attention_dropout
            UpperCAmelCase__			:     Union[str, Any]										= activation_dropout
            UpperCAmelCase__			:     List[Any]										= activation_function
            UpperCAmelCase__			:     Tuple										= init_std
            UpperCAmelCase__			:     Dict										= encoder_layerdrop
            UpperCAmelCase__			:     List[str]										= decoder_layerdrop
            UpperCAmelCase__			:     int										= use_cache
            UpperCAmelCase__			:     List[str]										= encoder_layers
            UpperCAmelCase__			:     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  ,	decoder_start_token_id=_lowerCAmelCase  ,	**_lowerCAmelCase  ,	)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     str										= OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ]		)
  
            if self.use_past:
                 UpperCAmelCase__			:     Optional[Any]										= {0: """batch"""}
                 UpperCAmelCase__			:     int										= {0: """batch""", 1: """past_decoder_sequence + sequence"""}
            else:
                 UpperCAmelCase__			:     Optional[Any]										= {0: """batch""", 1: """decoder_sequence"""}
                 UpperCAmelCase__			:     List[Any]										= {0: """batch""", 1: """decoder_sequence"""}
  
            if self.use_past:
                 self.fill_with_past_key_values_(_lowerCAmelCase  ,	direction="""inputs"""		)
            return common_inputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = 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__			:     Optional[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
            UpperCAmelCase__			:     List[str]										= tokenizer.num_special_tokens_to_add(_lowerCAmelCase		)
            UpperCAmelCase__			:     int										= 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__			:     Any										= [""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
            UpperCAmelCase__			:     Tuple										= dict(tokenizer(_lowerCAmelCase  ,	return_tensors=_lowerCAmelCase		)		)
            return common_inputs
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = -1  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	):
            UpperCAmelCase__			:     List[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__			:     Optional[Any]										= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     Dict										= {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
            UpperCAmelCase__			:     List[str]										= 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__			,		UpperCAmelCase__			:     Union[str, Any]										= common_inputs["""input_ids"""].shape
                 UpperCAmelCase__			:     Union[str, Any]										= common_inputs["""decoder_input_ids"""].shape[1]
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Any										= self.num_attention_heads
                 UpperCAmelCase__			:     List[str]										= (
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 UpperCAmelCase__			:     Tuple										= decoder_seq_length + 3
                 UpperCAmelCase__			:     List[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__			:     str										= []
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 UpperCAmelCase__			,		UpperCAmelCase__			:     Any										= self.num_layers
                 UpperCAmelCase__			:     Tuple										= min(_lowerCAmelCase  ,	_lowerCAmelCase		)
                 UpperCAmelCase__			:     str										= max(_lowerCAmelCase  ,	_lowerCAmelCase		) - min_num_layers
                 UpperCAmelCase__			:     List[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.
                 UpperCAmelCase__			:     Union[str, Any]										= 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
  
  
  
  
       __lowerCamelCase										=     _generate_dummy_inputs_for_default_and_seqaseq_lm
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE__		:       str   							=							[
            """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DinatForImageClassification""",
            """DinatModel""",
            """DinatPreTrainedModel""",
            """DinatBackbone""",
        ]
  
  if TYPE_CHECKING:
        from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  