"""simple docstring"""






from typing import Mapping

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


lowerCAmelCase__						:						Dict		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						List[str]		  =			{
    '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        snake_case (	__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"t5"
					snake_case__       					=				["past_key_values"]
					snake_case__       					=				{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(       self     :			Any					,lowerCamelCase__     :			Union[str, Any]=32_128					,lowerCamelCase__     :			Optional[Any]=512					,lowerCamelCase__     :			Optional[int]=64					,lowerCamelCase__     :			Any=2_048					,lowerCamelCase__     :			List[Any]=6					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=8					,lowerCamelCase__     :			str=32					,lowerCamelCase__     :			Dict=128					,lowerCamelCase__     :			List[Any]=0.1					,lowerCamelCase__     :			Optional[Any]=1e-6					,lowerCamelCase__     :			str=1.0					,lowerCamelCase__     :			Optional[int]="relu"					,lowerCamelCase__     :			Tuple=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			int=0					,lowerCamelCase__     :			Optional[Any]=1					,**lowerCamelCase__     :			Union[str, Any]					,):
									UpperCAmelCase__    =							vocab_size
									UpperCAmelCase__    =							d_model
									UpperCAmelCase__    =							d_kv
									UpperCAmelCase__    =							d_ff
									UpperCAmelCase__    =							num_layers
									UpperCAmelCase__    =							(
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									UpperCAmelCase__    =							num_heads
									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__    =							use_cache

									UpperCAmelCase__    =							self.feed_forward_proj.split('-'    )
									UpperCAmelCase__    =							act_info[-1]
									UpperCAmelCase__    =							act_info[0] == 'gated'

									if len(lowercase_    ) > 1 and act_info[0] != "gated" or len(lowercase_    ) > 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=lowercase_					,eos_token_id=lowercase_					,is_encoder_decoder=lowercase_					,**lowercase_					,)






class        snake_case (	__UpperCAmelCase       ):




					"""simple docstring"""

					@property
					def 	__lowerCAmelCase		(       self     :			int    ):
									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_(lowercase_					,direction='inputs'    )

									return common_inputs







					@property
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									return 13




"""simple docstring"""






def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return x if y == 0 else greatest_common_divisor(lowerCamelCase      ,    x % y )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return (x * y) // greatest_common_divisor(lowerCamelCase      ,    lowerCamelCase )



def 						a_		(      lowerCamelCase = 2_0 ):
				UpperCAmelCase__    =							1
				for i in range(1      ,    n + 1 ):
								UpperCAmelCase__    =							lcm(lowerCamelCase      ,    lowerCamelCase )
				return g


if __name__ == "__main__":
						print(F"""{solution() = }""")



