



"""simple docstring"""

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


lowercase__       =	logging.get_logger(__name__)

lowercase__       =	{
    "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__       (		lowerCAmelCase__   ):
						_lowerCAmelCase    						=       "canine"



						def __init__(self    ,  _lowercase=768    ,  _lowercase=12    ,  _lowercase=12    ,  _lowercase=3072    ,  _lowercase="gelu"    ,  _lowercase=0.1    ,  _lowercase=0.1    ,  _lowercase=16384    ,  _lowercase=16    ,  _lowercase=0.02    ,  _lowercase=1e-12    ,  _lowercase=0    ,  _lowercase=0xe_000    ,  _lowercase=0xe_001    ,  _lowercase=4    ,  _lowercase=4    ,  _lowercase=8    ,  _lowercase=16384    ,  _lowercase=128    ,  **_lowercase    ,  ):





													'''simple docstring'''

													super().__init__(pad_token_id=_lowerCamelCase    ,  bos_token_id=_lowerCamelCase    ,  eos_token_id=_lowerCamelCase    ,  **_lowerCamelCase				)

													__a		:		Dict						=							max_position_embeddings
													__a		:		Tuple						=							hidden_size
													__a		:		Optional[int]						=							num_hidden_layers
													__a		:		List[str]						=							num_attention_heads
													__a		:		Any						=							intermediate_size
													__a		:		Dict						=							hidden_act
													__a		:		int						=							hidden_dropout_prob
													__a		:		List[Any]						=							attention_probs_dropout_prob
													__a		:		Union[str, Any]						=							initializer_range
													__a		:		str						=							type_vocab_size
													__a		:		Dict						=							layer_norm_eps

													# Character config:
													__a		:		Tuple						=							downsampling_rate
													__a		:		Any						=							upsampling_kernel_size
													__a		:		str						=							num_hash_functions
													__a		:		List[str]						=							num_hash_buckets
													__a		:		List[Any]						=							local_transformer_stride




"""simple docstring"""
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""    )

      UpperCAmelCase__			:							Dict							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"
      UpperCAmelCase__			:							Any							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"

      UpperCAmelCase__			:							Optional[Any]							=    max(len(lowerCAmelCase    )			,	len(lowerCAmelCase    )    )

      return "0b" + "".join(
          str(int(char_a != char_b    )    )
          for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase    )			,	b_binary.zfill(lowerCAmelCase    )    )    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

