code: 
  
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE__		:       List[str]   							=							logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__		:       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 							UpperCAmelCase_     (		UpperCamelCase__		):
       __lowerCamelCase										=     """longt5"""
       __lowerCamelCase										=     ["""past_key_values"""]
       __lowerCamelCase										=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase=32128  ,	_lowerCAmelCase=512  ,	_lowerCAmelCase=64  ,	_lowerCAmelCase=2048  ,	_lowerCAmelCase=6  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=8  ,	_lowerCAmelCase=127  ,	_lowerCAmelCase=16  ,	_lowerCAmelCase=32  ,	_lowerCAmelCase=128  ,	_lowerCAmelCase=0.1  ,	_lowerCAmelCase=1e-6  ,	_lowerCAmelCase=1.0  ,	_lowerCAmelCase="relu"  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase="local"  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=0  ,	_lowerCAmelCase=1  ,	**_lowerCAmelCase  ,	):
            UpperCAmelCase__			:     Dict										= vocab_size
            UpperCAmelCase__			:     Tuple										= d_model
            UpperCAmelCase__			:     Any										= d_kv
            UpperCAmelCase__			:     Tuple										= d_ff
            UpperCAmelCase__			:     Optional[Any]										= num_layers
            # default = symmetry
            UpperCAmelCase__			:     List[str]										= num_decoder_layers if num_decoder_layers is not None else self.num_layers
            UpperCAmelCase__			:     List[str]										= num_heads
            UpperCAmelCase__			:     Optional[Any]										= local_radius
            UpperCAmelCase__			:     List[Any]										= global_block_size
            UpperCAmelCase__			:     Dict										= relative_attention_num_buckets
            UpperCAmelCase__			:     int										= relative_attention_max_distance
            UpperCAmelCase__			:     Tuple										= dropout_rate
            UpperCAmelCase__			:     Optional[Any]										= layer_norm_epsilon
            UpperCAmelCase__			:     int										= initializer_factor
            UpperCAmelCase__			:     Union[str, Any]										= feed_forward_proj
            UpperCAmelCase__			:     int										= encoder_attention_type
            UpperCAmelCase__			:     Any										= use_cache
  
            UpperCAmelCase__			:     Optional[int]										= self.feed_forward_proj.split("""-"""		)
            UpperCAmelCase__			:     str										= act_info[-1]
            UpperCAmelCase__			:     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":
                 UpperCAmelCase__			:     Optional[int]										= "gelu_new"
  
            super().__init__(
                pad_token_id=lowerCAmelCase__  ,	eos_token_id=lowerCAmelCase__  ,	is_encoder_decoder=lowerCAmelCase__  ,	**lowerCAmelCase__  ,	)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		UpperCamelCase__		):
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     List[Any]										= {
                "input_ids": {0: "batch", 1: "encoder_sequence"},
                "attention_mask": {0: "batch", 1: "encoder_sequence"},
            }
            if self.use_past:
                 UpperCAmelCase__			:     List[Any]										= "past_encoder_sequence + sequence"
                 UpperCAmelCase__			:     str										= {0: "batch"}
                 UpperCAmelCase__			:     List[str]										= {0: "batch", 1: "past_decoder_sequence + sequence"}
            else:
                 UpperCAmelCase__			:     Union[str, Any]										= {0: "batch", 1: "decoder_sequence"}
                 UpperCAmelCase__			:     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      __UpperCAmelCase     (							self		):
            return 13
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            warnings.warn(
                """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use ViltImageProcessor instead."""  ,	_lowerCAmelCase  ,	)
            super().__init__(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
  
  
  