





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a__			:    List[str]			      =		logging.get_logger(__name__)

a__			:    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      :	List[Any]  	=      """longt5"""
  __lowerCAmelCase      :	Any  	=      ["""past_key_values"""]
  __lowerCAmelCase      :	Optional[Any]  	=      {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


  def __init__(       self   ,__snake_case=3_2_1_2_8   ,__snake_case=5_1_2   ,__snake_case=6_4   ,__snake_case=2_0_4_8   ,__snake_case=6   ,__snake_case=None   ,__snake_case=8   ,__snake_case=1_2_7   ,__snake_case=1_6   ,__snake_case=3_2   ,__snake_case=1_2_8   ,__snake_case=0.1   ,__snake_case=1E-6   ,__snake_case=1.0   ,__snake_case="relu"   ,__snake_case=True   ,__snake_case="local"   ,__snake_case=True   ,__snake_case=0   ,__snake_case=1   ,**__snake_case   ,):



       """simple docstring"""





       A_					      =					vocab_size
       A_					      =					d_model
       A_					      =					d_kv
       A_					      =					d_ff
       A_					      =					num_layers
       # default = symmetry
       A_					      =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
       A_					      =					num_heads
       A_					      =					local_radius
       A_					      =					global_block_size
       A_					      =					relative_attention_num_buckets
       A_					      =					relative_attention_max_distance
       A_					      =					dropout_rate
       A_					      =					layer_norm_epsilon
       A_					      =					initializer_factor
       A_					      =					feed_forward_proj
       A_					      =					encoder_attention_type
       A_					      =					use_cache

       A_					      =					self.feed_forward_proj.split('''-'''   )
       A_					      =					act_info[-1]
       A_					      =					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_					      =					"gelu_new"

       super().__init__(
           pad_token_id=lowerCAmelCase__   ,eos_token_id=lowerCAmelCase__   ,is_encoder_decoder=lowerCAmelCase__   ,**lowerCAmelCase__   ,)





class UpperCAmelCase_							(   UpperCamelCase__   ):


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					{
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
            A_					      =					"past_encoder_sequence + sequence"
            A_					      =					{0: "batch"}
            A_					      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
            A_					      =					{0: "batch", 1: "decoder_sequence"}
            A_					      =					{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   ):



       """simple docstring"""





       return 1_3






from __future__ import annotations

def     UpperCAmelCase_						(						_UpperCAmelCase       :list[int | float]							)  ->							int | float:
     '''simple docstring'''


     if len(_UpperCAmelCase							) == 0:
          raise ValueError('''find_max() arg is an empty sequence'''							)
     A_					      =					nums[0]
     for x in nums:
          if x > max_num:
               A_					      =					x
     return max_num


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



