
"""simple docstring"""






from typing import Mapping

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


lowerCamelCase__ :       Union[str, Any]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Dict						     =			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """umt5"""
	UpperCamelCase				=  ["""past_key_values"""]





	def __init__(						self    :Any		, lowerCamelCase_    :Union[str, Any]=25_01_12		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :List[Any]=64		, lowerCamelCase_    :str=10_24		, lowerCamelCase_    :List[str]=8		, lowerCamelCase_    :Dict=None		, lowerCamelCase_    :Optional[int]=6		, lowerCamelCase_    :int=32		, lowerCamelCase_    :Dict=1_28		, lowerCamelCase_    :List[Any]=0.1		, lowerCamelCase_    :List[Any]=1E-6		, lowerCamelCase_    :List[str]=1.0		, lowerCamelCase_    :Union[str, Any]="gated-gelu"		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Tuple="T5Tokenizer"		, lowerCamelCase_    :List[str]=True		, lowerCamelCase_    :int=0		, lowerCamelCase_    :int=1		, lowerCamelCase_    :int=0		, **lowerCamelCase_    :Optional[Any]		, )							->			List[str]:







			'''simple docstring'''




			super().__init__(
			    is_encoder_decoder=lowerCamelCase_		, tokenizer_class=lowerCamelCase_		, tie_word_embeddings=lowerCamelCase_		, pad_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, decoder_start_token_id=lowerCamelCase_		, **lowerCamelCase_		, )
			SCREAMING_SNAKE_CASE       :     List[str]				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= d_model
			SCREAMING_SNAKE_CASE       :     List[Any]				= d_kv
			SCREAMING_SNAKE_CASE       :     Optional[int]				= d_ff
			SCREAMING_SNAKE_CASE       :     List[str]				= num_layers
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
			    num_decoder_layers if num_decoder_layers is not None else self.num_layers
			)  # default = symmetry
			SCREAMING_SNAKE_CASE       :     Dict				= num_heads
			SCREAMING_SNAKE_CASE       :     int				= relative_attention_num_buckets
			SCREAMING_SNAKE_CASE       :     str				= relative_attention_max_distance
			SCREAMING_SNAKE_CASE       :     str				= dropout_rate
			SCREAMING_SNAKE_CASE       :     int				= layer_norm_epsilon
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= initializer_factor
			SCREAMING_SNAKE_CASE       :     List[str]				= feed_forward_proj
			SCREAMING_SNAKE_CASE       :     List[str]				= use_cache

			SCREAMING_SNAKE_CASE       :     Any				= self.feed_forward_proj.split('''-'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= act_info[-1]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 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\''''					)

			if feed_forward_proj == "gated-gelu":
					SCREAMING_SNAKE_CASE       :     Any				= '''gelu_new'''





	@property
	def   __lowerCAmelCase (						self    :Any					)							->			Any:







			'''simple docstring'''




			return self.d_model





	@property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			return self.num_heads







	@property
	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			return self.num_layers





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def   __lowerCAmelCase (						self    :int					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {
			    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
			    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
			}
			if self.use_past:
					SCREAMING_SNAKE_CASE       :     int				= '''past_encoder_sequence + sequence'''
					SCREAMING_SNAKE_CASE       :     str				= {0: '''batch'''}
					SCREAMING_SNAKE_CASE       :     List[Any]				= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
			else:
					SCREAMING_SNAKE_CASE       :     Tuple				= {0: '''batch''', 1: '''decoder_sequence'''}
					SCREAMING_SNAKE_CASE       :     List[str]				= {0: '''batch''', 1: '''decoder_sequence'''}

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

			return common_inputs





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			int:







			'''simple docstring'''




			return 13







	@property
	def   __lowerCAmelCase (						self    :List[str]					)							->			float:







			'''simple docstring'''




			return 5E-4







"""simple docstring"""






from __future__ import annotations

from fractions import Fraction





def __A						(   a_ :							int		,		a_ :							int     )->							bool:




		'''simple docstring'''






		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)





def __A						(   a_ :							int     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
		SCREAMING_SNAKE_CASE       :     List[str]				= 11
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= int('''1''' + '''0''' * digit_len     )
		for num in range(a_		,		a_     ):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(a_		,		a_     ):
										solutions.append(F"{num}/{den}"     )
						den += 1
				num += 1
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10
		return solutions





def __A						(   a_ :							int = 2     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 1.0
		for fraction in fraction_list(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= Fraction(a_     )
				result *= frac.denominator / frac.numerator
		return int(a_     )


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





