

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


UpperCAmelCase__ :     Optional[int]				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     int				    =    {
    """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        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       ="""canine"""


  def __init__(			self    : Tuple				,				UpperCAmelCase__    : int=7_6_8				,				UpperCAmelCase__    : Optional[Any]=1_2				,				UpperCAmelCase__    : List[Any]=1_2				,				UpperCAmelCase__    : Any=3_0_7_2				,				UpperCAmelCase__    : str="gelu"				,				UpperCAmelCase__    : List[Any]=0.1				,				UpperCAmelCase__    : str=0.1				,				UpperCAmelCase__    : Tuple=1_6_3_8_4				,				UpperCAmelCase__    : Any=1_6				,				UpperCAmelCase__    : Optional[Any]=0.02				,				UpperCAmelCase__    : Optional[int]=1e-12				,				UpperCAmelCase__    : Optional[int]=0				,				UpperCAmelCase__    : Optional[Any]=0XE0_00				,				UpperCAmelCase__    : List[str]=0XE0_01				,				UpperCAmelCase__    : List[Any]=4				,				UpperCAmelCase__    : Tuple=4				,				UpperCAmelCase__    : List[Any]=8				,				UpperCAmelCase__    : List[str]=1_6_3_8_4				,				UpperCAmelCase__    : Optional[int]=1_2_8				,				**UpperCAmelCase__    : Any				,				)					->Optional[Any]:
        """simple docstring"""







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

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

        # Character config:
        SCREAMING_SNAKE_CASE :      Any       =   downsampling_rate
        SCREAMING_SNAKE_CASE :      Dict       =   upsampling_kernel_size
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   num_hash_functions
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   num_hash_buckets
        SCREAMING_SNAKE_CASE :      List[str]       =   local_transformer_stride







from __future__ import annotations

import numpy as np






def 					__lowercase      (     _A			)     ->						tuple[np.ndarray, np.ndarray]:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   np.shape(_A			)
      if rows != columns:
            SCREAMING_SNAKE_CASE :      int       =   (
                """'table' has to be of square shaped array but got a """
                F"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(_A			)

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.zeros((rows, columns)			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.zeros((rows, columns)			)
      for i in range(_A			):
            for j in range(_A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
            for j in range(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      List[str]       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   table[i][j] - total
      return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()




