code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  A_			  =    logging.get_logger(__name__)
  
  A_			  =    {
      "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       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    int     	=			"longt5"
    __lowerCamelCase       :    Optional[Any]     	=			["past_key_values"]
    __lowerCamelCase       :    Optional[int]     	=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(      self:		Optional[Any]				,			UpperCamelCase_:		Optional[Any]=3_2128				,			UpperCamelCase_:		Optional[Any]=512				,			UpperCamelCase_:		Tuple=64				,			UpperCamelCase_:		str=2048				,			UpperCamelCase_:		Union[str, Any]=6				,			UpperCamelCase_:		Tuple=None				,			UpperCamelCase_:		Dict=8				,			UpperCamelCase_:		Union[str, Any]=127				,			UpperCamelCase_:		Dict=16				,			UpperCamelCase_:		Optional[int]=32				,			UpperCamelCase_:		Union[str, Any]=128				,			UpperCamelCase_:		str=0.1				,			UpperCamelCase_:		Tuple=1e-6				,			UpperCamelCase_:		Any=1.0				,			UpperCamelCase_:		List[Any]="relu"				,			UpperCamelCase_:		int=True				,			UpperCamelCase_:		Union[str, Any]="local"				,			UpperCamelCase_:		Tuple=True				,			UpperCamelCase_:		List[str]=0				,			UpperCamelCase_:		Dict=1				,			**UpperCamelCase_:		Optional[Any]				,			):
       UpperCamelCase_     =vocab_size
       UpperCamelCase_     =d_model
       UpperCamelCase_     =d_kv
       UpperCamelCase_     =d_ff
       UpperCamelCase_     =num_layers
       # default = symmetry
       UpperCamelCase_     =num_decoder_layers if num_decoder_layers is not None else self.num_layers
       UpperCamelCase_     =num_heads
       UpperCamelCase_     =local_radius
       UpperCamelCase_     =global_block_size
       UpperCamelCase_     =relative_attention_num_buckets
       UpperCamelCase_     =relative_attention_max_distance
       UpperCamelCase_     =dropout_rate
       UpperCamelCase_     =layer_norm_epsilon
       UpperCamelCase_     =initializer_factor
       UpperCamelCase_     =feed_forward_proj
       UpperCamelCase_     =encoder_attention_type
       UpperCamelCase_     =use_cache
  
       UpperCamelCase_     =self.feed_forward_proj.split("-"					)
       UpperCamelCase_     =act_info[-1]
       UpperCamelCase_     =act_info[0] == "gated"
  
       if len(UpperCamelCase_					) > 1 and act_info[0] != "gated" or len(UpperCamelCase_					) > 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_     ="gelu_new"
  
       super().__init__(
           pad_token_id=UpperCamelCase_				,			eos_token_id=UpperCamelCase_				,			is_encoder_decoder=UpperCamelCase_				,			**UpperCamelCase_				,			)
  
  
  
  
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    @property
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       UpperCamelCase_     ={
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
          UpperCamelCase_     ="past_encoder_sequence + sequence"
          UpperCamelCase_     ={0: "batch"}
          UpperCamelCase_     ={0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          UpperCamelCase_     ={0: "batch", 1: "decoder_sequence"}
          UpperCamelCase_     ={0: "batch", 1: "decoder_sequence"}
  
       if self.use_past:
          self.fill_with_past_key_values_(UpperCamelCase_				,			direction="inputs"					)
  
       return common_inputs
  
  
  
  
  
    @property
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return 13
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from .hash_table import HashTable
  from .number_theory.prime_numbers import is_prime, next_prime
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		List[Any]				,			*UpperCamelCase_:		Dict				,			**UpperCamelCase_:		Optional[int]					):
       super().__init__(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Optional[int]				,			UpperCamelCase_:		Optional[int]					):
       UpperCamelCase_     =(
           next_prime(value % self.size_table					)
           if not is_prime(value % self.size_table					)
           else value % self.size_table
       )  # gt = bigger than
       return next_prime_gt - (data % next_prime_gt)
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		List[str]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Tuple					):
       return (increment * self.__hash_function_a(UpperCamelCase_				,			UpperCamelCase_					)) % self.size_table
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		str=None					):
       UpperCamelCase_     =1
       UpperCamelCase_     =self.hash_function(UpperCamelCase_					)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          UpperCamelCase_     =(
              self.__hash_double_function(UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_					)
              if self.balanced_factor() >= self.lim_charge
              else None
          )
          if new_key is None:
             break
          else:
             i += 1
  
       return new_key
  