
from typing import Mapping

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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class 					_SCREAMING_SNAKE_CASE  (      UpperCamelCase_ ):
      _A						:		List[Any]    	=     """t5"""
      _A						:		List[str]    	=     ["""past_key_values"""]
      _A						:		Any    	=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
      def __init__(			self		,	lowerCamelCase=3_21_28		,	lowerCamelCase=5_12		,	lowerCamelCase=64		,	lowerCamelCase=20_48		,	lowerCamelCase=6		,	lowerCamelCase=None		,	lowerCamelCase=8		,	lowerCamelCase=32		,	lowerCamelCase=1_28		,	lowerCamelCase=0.1		,	lowerCamelCase=1e-6		,	lowerCamelCase=1.0		,	lowerCamelCase="relu"		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=0		,	lowerCamelCase=1		,	**lowerCamelCase		,	):
          snake_case__						=				vocab_size
          snake_case__						=				d_model
          snake_case__						=				d_kv
          snake_case__						=				d_ff
          snake_case__						=				num_layers
          snake_case__						=				(
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          snake_case__						=				num_heads
          snake_case__						=				relative_attention_num_buckets
          snake_case__						=				relative_attention_max_distance
          snake_case__						=				dropout_rate
          snake_case__						=				layer_norm_epsilon
          snake_case__						=				initializer_factor
          snake_case__						=				feed_forward_proj
          snake_case__						=				use_cache

          snake_case__						=				self.feed_forward_proj.split("-" )
          snake_case__						=				act_info[-1]
          snake_case__						=				act_info[0] == """gated"""

          if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 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":
              snake_case__						=				"""gelu_new"""

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





class 					_SCREAMING_SNAKE_CASE  (      UpperCamelCase_ ):
      @property
      def A_			(			self ):
          snake_case__						=				{
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
              snake_case__						=				"""past_encoder_sequence + sequence"""
              snake_case__						=				{0: """batch"""}
              snake_case__						=				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
              snake_case__						=				{0: """batch""", 1: """decoder_sequence"""}
              snake_case__						=				{0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
              self.fill_with_past_key_values_(_a		,	direction="inputs" )

          return common_inputs



      @property
      def A_			(			self ):
          return 13





from __future__ import annotations

from fractions import Fraction





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[]
    snake_case__						=				11
    snake_case__						=				int("1" + "0" * digit_len							)
    for num in range(__lowerCAmelCase    ,		__lowerCAmelCase							):
        while den <= 99:
            if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                if is_digit_cancelling(__lowerCAmelCase    ,		__lowerCAmelCase							):
                    solutions.append(F"""{num}/{den}"""							)
            den += 1
        num += 1
        snake_case__						=				10
    return solutions





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = 2							):
    snake_case__						=				1.0
    for fraction in fraction_list(__lowerCAmelCase							):
        snake_case__						=				Fraction(__lowerCAmelCase							)
        result *= frac.denominator / frac.numerator
    return int(__lowerCAmelCase							)


if __name__ == "__main__":
     print(solution())



