

from typing import Mapping

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


_lowerCamelCase								=logging.get_logger(__name__)

_lowerCamelCase								={
    """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 		a_	(							snake_case__					):



      """simple docstring"""







      __UpperCAmelCase      		=				"longt5"
      __UpperCAmelCase      		=				["past_key_values"]
      __UpperCAmelCase      		=				{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
      def __init__(				self					:							Tuple      ,snake_case					:							List[Any]=32128      ,snake_case					:							Dict=512      ,snake_case					:							Union[str, Any]=64      ,snake_case					:							List[Any]=2048      ,snake_case					:							Any=6      ,snake_case					:							List[str]=None      ,snake_case					:							Union[str, Any]=8      ,snake_case					:							int=127      ,snake_case					:							Any=16      ,snake_case					:							List[str]=32      ,snake_case					:							Tuple=128      ,snake_case					:							Optional[Any]=0.1      ,snake_case					:							Optional[int]=1e-6      ,snake_case					:							int=1.0      ,snake_case					:							Optional[int]="relu"      ,snake_case					:							Dict=True      ,snake_case					:							Union[str, Any]="local"      ,snake_case					:							str=True      ,snake_case					:							int=0      ,snake_case					:							Dict=1      ,**snake_case					:							Dict      ,):
           SCREAMING_SNAKE_CASE										=       vocab_size
           SCREAMING_SNAKE_CASE										=       d_model
           SCREAMING_SNAKE_CASE										=       d_kv
           SCREAMING_SNAKE_CASE										=       d_ff
           SCREAMING_SNAKE_CASE										=       num_layers
           # default = symmetry
           SCREAMING_SNAKE_CASE										=       num_decoder_layers if num_decoder_layers is not None else self.num_layers
           SCREAMING_SNAKE_CASE										=       num_heads
           SCREAMING_SNAKE_CASE										=       local_radius
           SCREAMING_SNAKE_CASE										=       global_block_size
           SCREAMING_SNAKE_CASE										=       relative_attention_num_buckets
           SCREAMING_SNAKE_CASE										=       relative_attention_max_distance
           SCREAMING_SNAKE_CASE										=       dropout_rate
           SCREAMING_SNAKE_CASE										=       layer_norm_epsilon
           SCREAMING_SNAKE_CASE										=       initializer_factor
           SCREAMING_SNAKE_CASE										=       feed_forward_proj
           SCREAMING_SNAKE_CASE										=       encoder_attention_type
           SCREAMING_SNAKE_CASE										=       use_cache

           SCREAMING_SNAKE_CASE										=       self.feed_forward_proj.split('-'    )
           SCREAMING_SNAKE_CASE										=       act_info[-1]
           SCREAMING_SNAKE_CASE										=       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":
                SCREAMING_SNAKE_CASE										=       "gelu_new"

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







class 		a_	(							snake_case__					):



      """simple docstring"""







      @property
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=       {
               "input_ids": {0: "batch", 1: "encoder_sequence"},
               "attention_mask": {0: "batch", 1: "encoder_sequence"},
           }
           if self.use_past:
                SCREAMING_SNAKE_CASE										=       "past_encoder_sequence + sequence"
                SCREAMING_SNAKE_CASE										=       {0: "batch"}
                SCREAMING_SNAKE_CASE										=       {0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
                SCREAMING_SNAKE_CASE										=       {0: "batch", 1: "decoder_sequence"}
                SCREAMING_SNAKE_CASE										=       {0: "batch", 1: "decoder_sequence"}

           if self.use_past:
                self.fill_with_past_key_values_(snake_case      ,direction='inputs'    )

           return common_inputs


      @property
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           return 13




def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     SCREAMING_SNAKE_CASE										=1

     while power > 0:
          if power & 1:
               SCREAMING_SNAKE_CASE										=(result * base) % mod
          SCREAMING_SNAKE_CASE										=power >> 1
          SCREAMING_SNAKE_CASE										=(base * base) % mod

     return result







def       snake_case__    (       ):





     """simple docstring"""







     print(modular_exponential(3,   200,   13      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()

