

from __future__ import annotations



class        lowercase		:



 '''simple docstring'''


 def __init__(self			,			__a					)					->   List[Any]:






  """simple docstring"""





  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(__a					) != 0:
   UpperCAmelCase__     							=							len(rows[0]					)
   if cols == 0:
    raise error
   for row in rows:
    if len(__a					) != cols:
     raise error
    for value in row:
     if not isinstance(__a			,			(int, float)					):
      raise error
   UpperCAmelCase__     							=							rows
  else:
   UpperCAmelCase__     							=							[]




 def     UpperCamelCase__				(self					)					->   list[list[int]]:






  """simple docstring"""





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




 @property
 def     UpperCamelCase__				(self					)					->   int:






  """simple docstring"""





  return len(self.rows					)




 @property
 def     UpperCamelCase__				(self					)					->   int:






  """simple docstring"""





  return len(self.rows[0]					)




 @property
 def     UpperCamelCase__				(self					)					->   tuple[int, int]:






  """simple docstring"""





  return (self.num_rows, self.num_columns)




 @property
 def     UpperCamelCase__				(self					)					->   bool:






  """simple docstring"""





  return self.order[0] == self.order[1]




 def     UpperCamelCase__				(self					)					->   Matrix:






  """simple docstring"""





  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(__a					)




 def     UpperCamelCase__				(self					)					->   int:






  """simple docstring"""





  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					)					->   bool:






  """simple docstring"""





  return bool(self.determinant()					)




 def     UpperCamelCase__				(self			,			__a			,			__a					)					->   int:






  """simple docstring"""





  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(__a					).determinant()




 def     UpperCamelCase__				(self			,			__a			,			__a					)					->   int:






  """simple docstring"""





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




 def     UpperCamelCase__				(self					)					->   Matrix:






  """simple docstring"""





  return Matrix(
      [
          [self.get_minor(__a			,			__a					) for column in range(self.num_columns					)]
          for row in range(self.num_rows					)
      ]					)




 def     UpperCamelCase__				(self					)					->   Matrix:






  """simple docstring"""





  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					)					->   Matrix:






  """simple docstring"""





  UpperCAmelCase__     							=							[
      [self.cofactors().rows[column][row] for column in range(self.num_columns					)]
      for row in range(self.num_rows					)
  ]
  return Matrix(__a					)




 def     UpperCamelCase__				(self					)					->   Matrix:






  """simple docstring"""





  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					)					->   str:






  """simple docstring"""





  return str(self.rows					)




 def __str__(self					)					->   str:






  """simple docstring"""





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




 def     UpperCamelCase__				(self			,			__a			,			__a = None					)					->   None:






  """simple docstring"""





  UpperCAmelCase__     							=							TypeError('Row must be a list containing all ints and/or floats'					)
  if not isinstance(__a			,			__a					):
   raise type_error
  for value in row:
   if not isinstance(__a			,			(int, float)					):
    raise type_error
  if len(__a					) != 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(__a					)
  else:
   UpperCAmelCase__     							=							self.rows[0:position] + [row] + self.rows[position:]




 def     UpperCamelCase__				(self			,			__a			,			__a = None					)					->   None:






  """simple docstring"""





  UpperCAmelCase__     							=							TypeError(
      'Column must be a list containing all ints and/or floats'					)
  if not isinstance(__a			,			__a					):
   raise type_error
  for value in column:
   if not isinstance(__a			,			(int, float)					):
    raise type_error
  if len(__a					) != 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					)					->   bool:






  """simple docstring"""





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




 def __ne__(self			,			__a					)					->   bool:






  """simple docstring"""





  return not self == other




 def __neg__(self					)					->   Matrix:






  """simple docstring"""





  return self * -1




 def __add__(self			,			__a					)					->   Matrix:






  """simple docstring"""





  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					)					->   Matrix:






  """simple docstring"""





  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					)					->   Matrix:






  """simple docstring"""





  if isinstance(__a			,			(int, float)					):
   return Matrix(
       [[int(element * other					) for element in row] for row in self.rows]					)
  elif isinstance(__a			,			__a					):
   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(__a			,			__a					) 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					)					->   Matrix:






  """simple docstring"""





  if not isinstance(__a			,			__a					):
   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					)					->   int:






  """simple docstring"""





  return sum(row[i] * column[i] for i in range(len(__a					)					)					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()








import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
     pass

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


a_      :      Union[str, Any]    =     logging.get_logger(__name__)

a_      :      Optional[Any]    =     {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    int      						=					'''align_text_model'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE=3_0_5_2_2		,				SCREAMING_SNAKE_CASE=7_6_8		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=3_0_7_2		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=5_1_2		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=1e-1_2		,				SCREAMING_SNAKE_CASE=0		,				SCREAMING_SNAKE_CASE="absolute"		,				SCREAMING_SNAKE_CASE=True		,				**SCREAMING_SNAKE_CASE		,				) ->			List[str]:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  a__												= vocab_size
  a__												= hidden_size
  a__												= num_hidden_layers
  a__												= num_attention_heads
  a__												= hidden_act
  a__												= intermediate_size
  a__												= hidden_dropout_prob
  a__												= attention_probs_dropout_prob
  a__												= max_position_embeddings
  a__												= type_vocab_size
  a__												= initializer_range
  a__												= layer_norm_eps
  a__												= position_embedding_type
  a__												= use_cache
  a__												= pad_token_id



 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			"PretrainedConfig":
  cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE      )

  a__							,			a__												= cls.get_config_dict(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  # get the text config dict if we are loading from AlignConfig
  if config_dict.get('''model_type'''      ) == "align":
   a__												= config_dict['''text_config''']

  if "model_type" in config_dict and hasattr(cls		,				'''model_type'''      ) and config_dict["model_type"] != cls.model_type:
   logger.warning(
       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."      )

  return cls.from_dict(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    str      						=					'''align_vision_model'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE = 3		,				SCREAMING_SNAKE_CASE = 6_0_0		,				SCREAMING_SNAKE_CASE = 2.0		,				SCREAMING_SNAKE_CASE = 3.1		,				SCREAMING_SNAKE_CASE = 8		,				SCREAMING_SNAKE_CASE = [3, 3, 5, 3, 5, 5, 3]		,				SCREAMING_SNAKE_CASE = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]		,				SCREAMING_SNAKE_CASE = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]		,				SCREAMING_SNAKE_CASE = []		,				SCREAMING_SNAKE_CASE = [1, 2, 2, 2, 1, 2, 1]		,				SCREAMING_SNAKE_CASE = [1, 2, 2, 3, 3, 4, 1]		,				SCREAMING_SNAKE_CASE = [1, 6, 6, 6, 6, 6, 6]		,				SCREAMING_SNAKE_CASE = 0.25		,				SCREAMING_SNAKE_CASE = "swish"		,				SCREAMING_SNAKE_CASE = 2_5_6_0		,				SCREAMING_SNAKE_CASE = "mean"		,				SCREAMING_SNAKE_CASE = 0.02		,				SCREAMING_SNAKE_CASE = 0.0_01		,				SCREAMING_SNAKE_CASE = 0.99		,				SCREAMING_SNAKE_CASE = 0.2		,				**SCREAMING_SNAKE_CASE		,				) ->			str:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  a__												= num_channels
  a__												= image_size
  a__												= width_coefficient
  a__												= depth_coefficient
  a__												= depth_divisor
  a__												= kernel_sizes
  a__												= in_channels
  a__												= out_channels
  a__												= depthwise_padding
  a__												= strides
  a__												= num_block_repeats
  a__												= expand_ratios
  a__												= squeeze_expansion_ratio
  a__												= hidden_act
  a__												= hidden_dim
  a__												= pooling_type
  a__												= initializer_range
  a__												= batch_norm_eps
  a__												= batch_norm_momentum
  a__												= drop_connect_rate
  a__												= sum(SCREAMING_SNAKE_CASE      ) * 4



 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			"PretrainedConfig":
  cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE      )

  a__							,			a__												= cls.get_config_dict(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  # get the vision config dict if we are loading from AlignConfig
  if config_dict.get('''model_type'''      ) == "align":
   a__												= config_dict['''vision_config''']

  if "model_type" in config_dict and hasattr(cls		,				'''model_type'''      ) and config_dict["model_type"] != cls.model_type:
   logger.warning(
       f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
       f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."      )

  return cls.from_dict(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Dict      						=					'''align'''
 _lowercase   :    Optional[Any]      						=					True

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=6_4_0		,				SCREAMING_SNAKE_CASE=1.0		,				SCREAMING_SNAKE_CASE=0.02		,				**SCREAMING_SNAKE_CASE		,				) ->			List[str]:
  super().__init__(**SCREAMING_SNAKE_CASE      )

  if text_config is None:
   a__												= {}
   logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''      )

  if vision_config is None:
   a__												= {}
   logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''      )

  a__												= AlignTextConfig(**SCREAMING_SNAKE_CASE      )
  a__												= AlignVisionConfig(**SCREAMING_SNAKE_CASE      )

  a__												= projection_dim
  a__												= temperature_init_value
  a__												= initializer_range

 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  return cls(text_config=text_config.to_dict()		,				vision_config=vision_config.to_dict()		,				**SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= copy.deepcopy(self.__dict__      )
  a__												= self.text_config.to_dict()
  a__												= self.vision_config.to_dict()
  a__												= self.__class__.model_type
  return output





