

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__UpperCamelCase					:		Optional[Any]	  =						{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "canine"
							def __init__(     self:      Any       ,	UpperCamelCase:      Dict=7_68       ,	UpperCamelCase:      str=12       ,	UpperCamelCase:      Any=12       ,	UpperCamelCase:      Tuple=30_72       ,	UpperCamelCase:      Dict="gelu"       ,	UpperCamelCase:      List[Any]=0.1       ,	UpperCamelCase:      Union[str, Any]=0.1       ,	UpperCamelCase:      Optional[Any]=1_63_84       ,	UpperCamelCase:      Tuple=16       ,	UpperCamelCase:      int=0.02       ,	UpperCamelCase:      Dict=1e-12       ,	UpperCamelCase:      Tuple=0       ,	UpperCamelCase:      Dict=0XE0_00       ,	UpperCamelCase:      Dict=0XE0_01       ,	UpperCamelCase:      Any=4       ,	UpperCamelCase:      Optional[Any]=4       ,	UpperCamelCase:      List[str]=8       ,	UpperCamelCase:      int=1_63_84       ,	UpperCamelCase:      List[str]=1_28       ,	**UpperCamelCase:      Union[str, Any]       ,	)		->		Tuple:
											super().__init__(pad_token_id=UpperCamelCase       ,	bos_token_id=UpperCamelCase       ,	eos_token_id=UpperCamelCase       ,	**UpperCamelCase						)

											snake_case__									=     max_position_embeddings
											snake_case__									=     hidden_size
											snake_case__									=     num_hidden_layers
											snake_case__									=     num_attention_heads
											snake_case__									=     intermediate_size
											snake_case__									=     hidden_act
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     initializer_range
											snake_case__									=     type_vocab_size
											snake_case__									=     layer_norm_eps

											# Character config:
											snake_case__									=     downsampling_rate
											snake_case__									=     upsampling_kernel_size
											snake_case__									=     num_hash_functions
											snake_case__									=     num_hash_buckets
											snake_case__									=     local_transformer_stride












from __future__ import annotations
def      a_ (      _A  )			->			bool:







				"""simple docstring"""
				return len(set(_A  )  ) == len(_A  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




