



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_   :str        =      logging.get_logger(__name__)

a_   :Dict        =      {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}




class 					snake_case__						(       UpperCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	'align_text_model'






     def __init__(       self  :  Dict, _snake_case  :  List[str]=3_0_5_2_2, _snake_case  :  str=7_6_8, _snake_case  :  int=1_2, _snake_case  :  List[str]=1_2, _snake_case  :  Any=3_0_7_2, _snake_case  :  Any="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  int=0.1, _snake_case  :  Dict=5_1_2, _snake_case  :  Dict=2, _snake_case  :  Tuple=0.0_2, _snake_case  :  int=1e-12, _snake_case  :  Dict=0, _snake_case  :  Optional[int]="absolute", _snake_case  :  str=True, **_snake_case  :  List[Any], )   ->Dict:
            super().__init__(**__UpperCAmelCase					)

            snake_case__							:	Any   	=	vocab_size
            snake_case__							:	List[str]   	=	hidden_size
            snake_case__							:	Dict   	=	num_hidden_layers
            snake_case__							:	str   	=	num_attention_heads
            snake_case__							:	int   	=	hidden_act
            snake_case__							:	Tuple   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_dropout_prob
            snake_case__							:	Any   	=	attention_probs_dropout_prob
            snake_case__							:	str   	=	max_position_embeddings
            snake_case__							:	Optional[int]   	=	type_vocab_size
            snake_case__							:	Tuple   	=	initializer_range
            snake_case__							:	int   	=	layer_norm_eps
            snake_case__							:	Optional[Any]   	=	position_embedding_type
            snake_case__							:	Optional[Any]   	=	use_cache
            snake_case__							:	str   	=	pad_token_id






     @classmethod
     def    lowercase_   (       cls  :  Tuple, _snake_case  :  Union[str, os.PathLike], **_snake_case  :  int					)   ->"PretrainedConfig":
            cls._set_token_in_kwargs(__UpperCAmelCase					)

            snake_case__	,				snake_case__							:	List[str]   	=	cls.get_config_dict(__UpperCAmelCase, **__UpperCAmelCase					)

            # get the text config dict if we are loading from AlignConfig
            if config_dict.get('model_type'					) == "align":
                   snake_case__							:	Dict   	=	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(__UpperCAmelCase, **__UpperCAmelCase					)




class 					snake_case__						(       UpperCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	'align_vision_model'






     def __init__(       self  :  Any, _snake_case  :  int = 3, _snake_case  :  int = 6_0_0, _snake_case  :  float = 2.0, _snake_case  :  float = 3.1, _snake_case  :  int = 8, _snake_case  :  List[int] = [3, 3, 5, 3, 5, 5, 3], _snake_case  :  List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2], _snake_case  :  List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0], _snake_case  :  List[int] = [], _snake_case  :  List[int] = [1, 2, 2, 2, 1, 2, 1], _snake_case  :  List[int] = [1, 2, 2, 3, 3, 4, 1], _snake_case  :  List[int] = [1, 6, 6, 6, 6, 6, 6], _snake_case  :  float = 0.2_5, _snake_case  :  str = "swish", _snake_case  :  int = 2_5_6_0, _snake_case  :  str = "mean", _snake_case  :  float = 0.0_2, _snake_case  :  float = 0.0_0_1, _snake_case  :  float = 0.9_9, _snake_case  :  float = 0.2, **_snake_case  :  List[Any], )   ->Union[str, Any]:
            super().__init__(**__UpperCAmelCase					)

            snake_case__							:	int   	=	num_channels
            snake_case__							:	Union[str, Any]   	=	image_size
            snake_case__							:	Tuple   	=	width_coefficient
            snake_case__							:	Optional[int]   	=	depth_coefficient
            snake_case__							:	Dict   	=	depth_divisor
            snake_case__							:	Dict   	=	kernel_sizes
            snake_case__							:	Union[str, Any]   	=	in_channels
            snake_case__							:	Any   	=	out_channels
            snake_case__							:	Optional[Any]   	=	depthwise_padding
            snake_case__							:	Union[str, Any]   	=	strides
            snake_case__							:	Any   	=	num_block_repeats
            snake_case__							:	List[Any]   	=	expand_ratios
            snake_case__							:	List[str]   	=	squeeze_expansion_ratio
            snake_case__							:	Tuple   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dim
            snake_case__							:	List[Any]   	=	pooling_type
            snake_case__							:	List[str]   	=	initializer_range
            snake_case__							:	Optional[Any]   	=	batch_norm_eps
            snake_case__							:	Dict   	=	batch_norm_momentum
            snake_case__							:	Optional[int]   	=	drop_connect_rate
            snake_case__							:	List[str]   	=	sum(__UpperCAmelCase					) * 4






     @classmethod
     def    lowercase_   (       cls  :  List[str], _snake_case  :  Union[str, os.PathLike], **_snake_case  :  Any					)   ->"PretrainedConfig":
            cls._set_token_in_kwargs(__UpperCAmelCase					)

            snake_case__	,				snake_case__							:	Tuple   	=	cls.get_config_dict(__UpperCAmelCase, **__UpperCAmelCase					)

            # get the vision config dict if we are loading from AlignConfig
            if config_dict.get('model_type'					) == "align":
                   snake_case__							:	Union[str, Any]   	=	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(__UpperCAmelCase, **__UpperCAmelCase					)




class 					snake_case__						(       UpperCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	'align'
     _SCREAMING_SNAKE_CASE						=	True






     def __init__(       self  :  str, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=None, _snake_case  :  Tuple=6_4_0, _snake_case  :  int=1.0, _snake_case  :  List[str]=0.0_2, **_snake_case  :  Optional[int], )   ->Any:
            super().__init__(**__UpperCAmelCase					)

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

            if vision_config is None:
                   snake_case__							:	List[str]   	=	{}
                   logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'					)

            snake_case__							:	Any   	=	AlignTextConfig(**__UpperCAmelCase					)
            snake_case__							:	Optional[Any]   	=	AlignVisionConfig(**__UpperCAmelCase					)

            snake_case__							:	Optional[Any]   	=	projection_dim
            snake_case__							:	int   	=	temperature_init_value
            snake_case__							:	Optional[int]   	=	initializer_range






     @classmethod
     def    lowercase_   (       cls  :  int, _snake_case  :  AlignTextConfig, _snake_case  :  AlignVisionConfig, **_snake_case  :  int					)   ->Dict:
            return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **__UpperCAmelCase					)






     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            snake_case__							:	int   	=	copy.deepcopy(self.__dict__					)
            snake_case__							:	Any   	=	self.text_config.to_dict()
            snake_case__							:	List[Any]   	=	self.vision_config.to_dict()
            snake_case__							:	List[str]   	=	self.__class__.model_type
            return output







from __future__ import annotations





class      A							:


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[list[int]]		)						->				List[str]:
         """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(__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 			lowercase_      (self     :						Any		)						->				list[list[int]]:
         """simple docstring"""



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


   @property
   def 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         return len(self.rows		)


   @property
   def 			lowercase_      (self     :						Union[str, Any]		)						->				int:
         """simple docstring"""



         return len(self.rows[0]		)


   @property
   def 			lowercase_      (self     :						List[Any]		)						->				tuple[int, int]:
         """simple docstring"""



         return (self.num_rows, self.num_columns)


   @property
   def 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Any		)						->				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(__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				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 			lowercase_      (self     :						Tuple		)						->				bool:
         """simple docstring"""



         return bool(self.determinant()		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				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(__UpperCAmelCase		).determinant()


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				int:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Matrix:
         """simple docstring"""



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


   def 			lowercase_      (self     :						List[str]		)						->				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 			lowercase_      (self     :						Optional[Any]		)						->				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(__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[Any]		)						->				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     :						Dict		)						->				str:
         """simple docstring"""



         return str(self.rows		)


   def __str__(self     :						Optional[Any]		)						->				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(__UpperCAmelCase		) for value in row]		) + ".]"
                     for row in self.rows
                 ]		)
             + "]"
         )


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         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 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						int | None = None		)						->				None:
         """simple docstring"""



         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     :						Any       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



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


   def __ne__(self     :						int       ,  __UpperCAmelCase     :						object		)						->				bool:
         """simple docstring"""



         return not self == other


   def __neg__(self     :						Dict		)						->				Matrix:
         """simple docstring"""



         return self * -1


   def __add__(self     :						Dict       ,  __UpperCAmelCase     :						Matrix		)						->				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     :						Optional[Any]       ,  __UpperCAmelCase     :						Matrix		)						->				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     :						Tuple       ,  __UpperCAmelCase     :						Matrix | int | float		)						->				Matrix:
         """simple docstring"""



         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     :						List[Any]       ,  __UpperCAmelCase     :						int		)						->				Matrix:
         """simple docstring"""



         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 			lowercase_      (cls     :						Dict       ,  __UpperCAmelCase     :						list[int]       ,  __UpperCAmelCase     :						list[int]		)						->				int:
         """simple docstring"""



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


if __name__ == "__main__":
      import doctest

      doctest.testmod()



