code: 
  
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase			:			List[str]	  = logging.get_logger(__name__)
  
  UpperCAmelCase			:			Tuple	  = {
      """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
      """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
      """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
      """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
  }
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      List[Any]        =    """longt5"""
        _lowercase       :      Any        =    ["""past_key_values"""]
        _lowercase       :      Optional[Any]        =    {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=3_2_1_2_8	,   lowerCAmelCase__=5_1_2	,   lowerCAmelCase__=6_4	,   lowerCAmelCase__=2_0_4_8	,   lowerCAmelCase__=6	,   lowerCAmelCase__=None	,   lowerCAmelCase__=8	,   lowerCAmelCase__=1_2_7	,   lowerCAmelCase__=1_6	,   lowerCAmelCase__=3_2	,   lowerCAmelCase__=1_2_8	,   lowerCAmelCase__=0.1	,   lowerCAmelCase__=1E-6	,   lowerCAmelCase__=1.0	,   lowerCAmelCase__="relu"	,   lowerCAmelCase__=True	,   lowerCAmelCase__="local"	,   lowerCAmelCase__=True	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   **lowerCAmelCase__	,   )     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =vocab_size
          a__			:      Tuple            =d_model
          a__			:      Any            =d_kv
          a__			:      Tuple            =d_ff
          a__			:      Optional[Any]            =num_layers
          # default = symmetry
          a__			:      List[str]            =num_decoder_layers if num_decoder_layers is not None else self.num_layers
          a__			:      List[str]            =num_heads
          a__			:      Optional[Any]            =local_radius
          a__			:      List[Any]            =global_block_size
          a__			:      Dict            =relative_attention_num_buckets
          a__			:      int            =relative_attention_max_distance
          a__			:      Tuple            =dropout_rate
          a__			:      Optional[Any]            =layer_norm_epsilon
          a__			:      int            =initializer_factor
          a__			:      Union[str, Any]            =feed_forward_proj
          a__			:      int            =encoder_attention_type
          a__			:      Any            =use_cache
  
          a__			:      Optional[int]            =self.feed_forward_proj.split("-"							)
          a__			:      str            =act_info[-1]
          a__			:      Union[str, Any]            =act_info[0] == "gated"
  
          if len(lowerCAmelCase__							) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__							) > 2:
            raise ValueError(
                F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                "'gated-gelu' or 'relu'"							)
  
          # for backwards compatibility
          if feed_forward_proj == "gated-gelu":
            a__			:      Optional[int]            ="gelu_new"
  
          super().__init__(
              pad_token_id=lowerCAmelCase__	,   eos_token_id=lowerCAmelCase__	,   is_encoder_decoder=lowerCAmelCase__	,   **lowerCAmelCase__	,   )
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Mapping[str, Mapping[int, str]]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            ={
              "input_ids": {0: "batch", 1: "encoder_sequence"},
              "attention_mask": {0: "batch", 1: "encoder_sequence"},
          }
          if self.use_past:
            a__			:      List[Any]            ="past_encoder_sequence + sequence"
            a__			:      str            ={0: "batch"}
            a__			:      List[str]            ={0: "batch", 1: "past_decoder_sequence + sequence"}
          else:
            a__			:      Union[str, Any]            ={0: "batch", 1: "decoder_sequence"}
            a__			:      Optional[int]            ={0: "batch", 1: "decoder_sequence"}
  
          if self.use_past:
            self.fill_with_past_key_values_(lowerCAmelCase__	,   direction="inputs"							)
  
          return common_inputs
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          return 1_3
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_detr import DetrImageProcessor
  
  
  UpperCAmelCase			:			List[Any]	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          warnings.warn(
              "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use DetrImageProcessor instead."	,   lowerCAmelCase__	,   )
          super().__init__(*lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  