code: 
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  UpperCamelCase_	:    Tuple  					=       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      __lowercase	(	__snake_case					):
  	_A   			=      "longt5"
  	_A   			=      ["past_key_values"]
  	_A   			=      {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
  
  
  
  
  
  
  	def __init__(self							:   Union[str, Any]			,  snake_case							:   Tuple=3_2128			,  snake_case							:   Optional[int]=512			,  snake_case							:   str=64			,  snake_case							:   int=2048			,  snake_case							:   List[Any]=6			,  snake_case							:   Dict=None			,  snake_case							:   List[str]=8			,  snake_case							:   Union[str, Any]=127			,  snake_case							:   Optional[Any]=16			,  snake_case							:   Union[str, Any]=32			,  snake_case							:   Optional[int]=128			,  snake_case							:   Any=0.1			,  snake_case							:   Any=1e-6			,  snake_case							:   Optional[Any]=1.0			,  snake_case							:   List[Any]="relu"			,  snake_case							:   Any=True			,  snake_case							:   Tuple="local"			,  snake_case							:   int=True			,  snake_case							:   str=0			,  snake_case							:   Any=1			,  **snake_case							:   Any			,  )   -> Optional[Any]:
  							_lowercase					:  Optional[int]    			=      vocab_size
  							_lowercase					:  Tuple    			=      d_model
  							_lowercase					:  int    			=      d_kv
  							_lowercase					:  Tuple    			=      d_ff
  							_lowercase					:  Optional[Any]    			=      num_layers
  							# default = symmetry
  							_lowercase					:  Union[str, Any]    			=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
  							_lowercase					:  Optional[Any]    			=      num_heads
  							_lowercase					:  Any    			=      local_radius
  							_lowercase					:  Optional[Any]    			=      global_block_size
  							_lowercase					:  int    			=      relative_attention_num_buckets
  							_lowercase					:  List[str]    			=      relative_attention_max_distance
  							_lowercase					:  Optional[int]    			=      dropout_rate
  							_lowercase					:  List[str]    			=      layer_norm_epsilon
  							_lowercase					:  Dict    			=      initializer_factor
  							_lowercase					:  Any    			=      feed_forward_proj
  							_lowercase					:  Any    			=      encoder_attention_type
  							_lowercase					:  List[str]    			=      use_cache
  
  							_lowercase					:  int    			=      self.feed_forward_proj.split("-"						)
  							_lowercase					:  Optional[int]    			=      act_info[-1]
  							_lowercase					:  List[str]    			=      act_info[0] == "gated"
  
  							if len(snake_case						) > 1 and act_info[0] != "gated" or len(snake_case						) > 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":
  													_lowercase					:  Optional[int]    			=      "gelu_new"
  
  							super().__init__(
  							    pad_token_id=snake_case			,  eos_token_id=snake_case			,  is_encoder_decoder=snake_case			,  **snake_case			,  )
  
  
  
  
  class      __lowercase	(	__snake_case					):
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   Optional[int]						)   -> Mapping[str, Mapping[int, str]]:
  							_lowercase					:  str    			=      {
  							    "input_ids": {0: "batch", 1: "encoder_sequence"},
  							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
  							}
  							if self.use_past:
  													_lowercase					:  Any    			=      "past_encoder_sequence + sequence"
  													_lowercase					:  Tuple    			=      {0: "batch"}
  													_lowercase					:  Tuple    			=      {0: "batch", 1: "past_decoder_sequence + sequence"}
  							else:
  													_lowercase					:  int    			=      {0: "batch", 1: "decoder_sequence"}
  													_lowercase					:  Optional[Any]    			=      {0: "batch", 1: "decoder_sequence"}
  
  							if self.use_past:
  													self.fill_with_past_key_values_(snake_case			,  direction="inputs"						)
  
  							return common_inputs
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   List[str]						)   -> int:
  							return 13
  
  
  
  
  
style_context: 
  import os
  
  
  
  def 		UpperCamelCase    (					)      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Tuple    			=      0
  						_lowercase					:  List[Any]    			=      0
  						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
  												_lowercase					:  Any    			=      str(file.readlines()[0]     )
  												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )
  
  						name.sort()
  						for i in range(len(_UpperCAmelCase     )     ):
  												for j in name[i]:
  																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
  												total_sum += (i + 1) * temp_sum
  												_lowercase					:  Tuple    			=      0
  						return total_sum
  
  
  if __name__ == "__main__":
  							print(solution())
  
  
  
  
  