






"""simple docstring"""







from __future__ import annotations







class    lowerCamelCase__   :






    def __init__(					self      ,A   ):
          UpperCAmelCase							     =			TypeError(
              """Matrices must be formed from a list of zero or more lists containing at """
              """least one and the same number of values, each of which must be of type """
              """int or float."""   )
          if len(__UpperCamelCase   ) != 0:
                UpperCAmelCase							     =			len(rows[0]   )
                if cols == 0:
                      raise error
                for row in rows:
                      if len(__UpperCamelCase   ) != cols:
                            raise error
                      for value in row:
                            if not isinstance(__UpperCamelCase      ,(int, float)   ):
                                  raise error
                UpperCAmelCase							     =			rows
          else:
                UpperCAmelCase							     =			[]






    def 							_UpperCamelCase				(					self   ):
          return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]






    @property
    def 							_UpperCamelCase				(					self   ):
          return len(self.rows   )






    @property
    def 							_UpperCamelCase				(					self   ):
          return len(self.rows[0]   )






    @property
    def 							_UpperCamelCase				(					self   ):
          return (self.num_rows, self.num_columns)






    @property
    def 							_UpperCamelCase				(					self   ):
          return self.order[0] == self.order[1]






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			[
              [0 if column_num != row_num else 1 for column_num in range(self.num_rows   )]
              for row_num in range(self.num_rows   )
          ]
          return Matrix(__UpperCamelCase   )






    def 							_UpperCamelCase				(					self   ):


          if not self.is_square:
                return 0
          if self.order == (0, 0):
                return 1
          if self.order == (1, 1):
                return int(self.rows[0][0]   )
          if self.order == (2, 2):
                return int(
                    (self.rows[0][0] * self.rows[1][1])
                    - (self.rows[0][1] * self.rows[1][0])   )
          else:
                return sum(
                    self.rows[0][column] * self.cofactors().rows[0][column]
                    for column in range(self.num_columns   )   )






    def 							_UpperCamelCase				(					self   ):
          return bool(self.determinant()   )






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          UpperCAmelCase							     =			[
              [
                  self.rows[other_row][other_column]
                  for other_column in range(self.num_columns   )
                  if other_column != column
              ]
              for other_row in range(self.num_rows   )
              if other_row != row
          ]
          return Matrix(__UpperCamelCase   ).determinant()






    def 							_UpperCamelCase				(					self      ,A      ,A   ):


          if (row + column) % 2 == 0:
                return self.get_minor(__UpperCamelCase      ,__UpperCamelCase   )
          return -1 * self.get_minor(__UpperCamelCase      ,__UpperCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          return Matrix(
              [
                  [self.get_minor(__UpperCamelCase      ,__UpperCamelCase   ) for column in range(self.num_columns   )]
                  for row in range(self.num_rows   )
              ]   )






    def 							_UpperCamelCase				(					self   ):
          return Matrix(
              [
                  [
                      self.minors().rows[row][column]
                      if (row + column) % 2 == 0
                      else self.minors().rows[row][column] * -1
                      for column in range(self.minors().num_columns   )
                  ]
                  for row in range(self.minors().num_rows   )
              ]   )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			[
              [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
              for row in range(self.num_rows   )
          ]
          return Matrix(__UpperCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.determinant()
          if not determinant:
                raise TypeError("""Only matrices with a non-zero determinant have an inverse"""   )
          return self.adjugate() * (1 / determinant)






    def __repr__(					self   ):
          return str(self.rows   )






    def __str__(					self   ):


          if self.num_rows == 0:
                return "[]"
          if self.num_rows == 1:
                return "[[" + ". ".join(str(self.rows[0]   )   ) + "]]"
          return (
              "["
              + "\n ".join(
                  [
                      """[""" + """. """.join([str(__UpperCamelCase   ) for value in row]   ) + """.]"""
                      for row in self.rows
                  ]   )
              + "]"
          )






    def 							_UpperCamelCase				(					self      ,A      ,A = None   ):
          UpperCAmelCase							     =			TypeError("""Row must be a list containing all ints and/or floats"""   )
          if not isinstance(__UpperCamelCase      ,__UpperCamelCase   ):
                raise type_error
          for value in row:
                if not isinstance(__UpperCamelCase      ,(int, float)   ):
                      raise type_error
          if len(__UpperCamelCase   ) != self.num_columns:
                raise ValueError(
                    """Row must be equal in length to the other rows in the matrix"""   )
          if position is None:
                self.rows.append(__UpperCamelCase   )
          else:
                UpperCAmelCase							     =			self.rows[0:position] + [row] + self.rows[position:]






    def 							_UpperCamelCase				(					self      ,A      ,A = None   ):
          UpperCAmelCase							     =			TypeError(
              """Column must be a list containing all ints and/or floats"""   )
          if not isinstance(__UpperCamelCase      ,__UpperCamelCase   ):
                raise type_error
          for value in column:
                if not isinstance(__UpperCamelCase      ,(int, float)   ):
                      raise type_error
          if len(__UpperCamelCase   ) != self.num_rows:
                raise ValueError(
                    """Column must be equal in length to the other columns in the matrix"""   )
          if position is None:
                UpperCAmelCase							     =			[self.rows[i] + [column[i]] for i in range(self.num_rows   )]
          else:
                UpperCAmelCase							     =			[
                    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                    for i in range(self.num_rows   )
                ]






    def __eq__(					self      ,A   ):


          if not isinstance(__UpperCamelCase      ,__UpperCamelCase   ):
                return NotImplemented
          return self.rows == other.rows






    def __ne__(					self      ,A   ):
          return not self == other






    def __neg__(					self   ):
          return self * -1






    def __add__(					self      ,A   ):


          if self.order != other.order:
                raise ValueError("""Addition requires matrices of the same order"""   )
          return Matrix(
              [
                  [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns   )]
                  for i in range(self.num_rows   )
              ]   )






    def __sub__(					self      ,A   ):


          if self.order != other.order:
                raise ValueError("""Subtraction requires matrices of the same order"""   )
          return Matrix(
              [
                  [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns   )]
                  for i in range(self.num_rows   )
              ]   )






    def __mul__(					self      ,A   ):


          if isinstance(__UpperCamelCase      ,(int, float)   ):
                return Matrix(
                    [[int(element * other   ) for element in row] for row in self.rows]   )
          elif isinstance(__UpperCamelCase      ,__UpperCamelCase   ):
                if self.num_columns != other.num_rows:
                      raise ValueError(
                          """The number of columns in the first matrix must """
                          """be equal to the number of rows in the second"""   )
                return Matrix(
                    [
                        [Matrix.dot_product(__UpperCamelCase      ,__UpperCamelCase   ) for column in other.columns()]
                        for row in self.rows
                    ]   )
          else:
                raise TypeError(
                    """A Matrix can only be multiplied by an int, float, or another matrix"""   )






    def __pow__(					self      ,A   ):


          if not isinstance(__UpperCamelCase      ,__UpperCamelCase   ):
                raise TypeError("""A Matrix can only be raised to the power of an int"""   )
          if not self.is_square:
                raise ValueError("""Only square matrices can be raised to a power"""   )
          if other == 0:
                return self.identity()
          if other < 0:
                if self.is_invertable():
                      return self.inverse() ** (-other)
                raise ValueError(
                    """Only invertable matrices can be raised to a negative power"""   )
          UpperCAmelCase							     =			self
          for _ in range(other - 1   ):
                result *= self
          return result






    @classmethod
    def 							_UpperCamelCase				(					cls      ,A      ,A   ):
          return sum(row[i] * column[i] for i in range(len(__UpperCamelCase   )   )   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A									=  logging.get_logger(__name__)

A									=  {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			__A    	=	"""bit"""
			__A    	=	["""preactivation""", """bottleneck"""]
			__A    	=	["""SAME""", """VALID"""]


			def __init__(     self			,					__UpperCamelCase=3			,					__UpperCamelCase=64			,					__UpperCamelCase=[2_56, 5_12, 10_24, 20_48]			,					__UpperCamelCase=[3, 4, 6, 3]			,					__UpperCamelCase="preactivation"			,					__UpperCamelCase="relu"			,					__UpperCamelCase=None			,					__UpperCamelCase=32			,					__UpperCamelCase=0.0			,					__UpperCamelCase=False			,					__UpperCamelCase=32			,					__UpperCamelCase=1			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_   		=			global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
				snake_case_   		=			num_channels
				snake_case_   		=			embedding_size
				snake_case_   		=			hidden_sizes
				snake_case_   		=			depths
				snake_case_   		=			layer_type
				snake_case_   		=			hidden_act
				snake_case_   		=			global_padding
				snake_case_   		=			num_groups
				snake_case_   		=			drop_path_rate
				snake_case_   		=			embedding_dynamic_padding
				snake_case_   		=			output_stride
				snake_case_   		=			width_factor

				snake_case_   		=			['stem'] + [f"""stage{idx}""" for idx in range(1			,					len(__UpperCamelCase				) + 1				)]
				snake_case_		,							snake_case_   		=			get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase			,					out_indices=__UpperCamelCase			,					stage_names=self.stage_names				)
