
import copy
import os
from typing import Union

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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}





class     lowerCamelCase      (       SCREAMING_SNAKE_CASE__					):
	UpperCamelCase_  :					str    		=	'bridgetower_vision_model'







	def __init__(     self      :int   ,     lowercase      :str=7_6_8   ,     lowercase      :Optional[int]=1_2   ,     lowercase      :Tuple=3   ,     lowercase      :Dict=1_6   ,     lowercase      :Tuple=2_8_8   ,     lowercase      :int=1   ,     lowercase      :int=1e-05   ,     lowercase      :List[str]=False   ,     lowercase      :int=True   ,     lowercase      :Any=False   ,     **lowercase      :Union[str, Any]   ,     )		->      Any:







				"""simple docstring"""






				super().__init__(**lowercase			)
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= patch_size
				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= initializer_factor
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= stop_gradient
				SCREAMING_SNAKE_CASE     						= share_layernorm
				SCREAMING_SNAKE_CASE     						= remove_last_layer







	@classmethod
	def  snake_case__     (     cls      :Any   ,     lowercase      :Union[str, Any]   ,     **lowercase      :Tuple			)		->      "PretrainedConfig":







				"""simple docstring"""






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

				if config_dict.get('''model_type'''			) == "bridgetower":
							SCREAMING_SNAKE_CASE     						= 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(lowercase   ,     **lowercase			)





class     lowerCamelCase      (       SCREAMING_SNAKE_CASE__					):
	UpperCamelCase_  :					Dict    		=	'bridgetower_text_model'







	def __init__(     self      :List[Any]   ,     lowercase      :Dict=5_0_2_6_5   ,     lowercase      :Tuple=7_6_8   ,     lowercase      :Any=1_2   ,     lowercase      :str=1_2   ,     lowercase      :Any=1   ,     lowercase      :str=3_0_7_2   ,     lowercase      :Dict="gelu"   ,     lowercase      :int=0.1   ,     lowercase      :Optional[Any]=0.1   ,     lowercase      :Tuple=5_1_4   ,     lowercase      :Optional[int]=1   ,     lowercase      :int=1e-05   ,     lowercase      :Union[str, Any]=1   ,     lowercase      :Optional[Any]=0   ,     lowercase      :Any=2   ,     lowercase      :Any="absolute"   ,     lowercase      :Union[str, Any]=True   ,     **lowercase      :Optional[int]   ,     )		->      Optional[Any]:







				"""simple docstring"""






				super().__init__(**lowercase			)

				SCREAMING_SNAKE_CASE     						= vocab_size
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= initializer_factor
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= max_position_embeddings
				SCREAMING_SNAKE_CASE     						= type_vocab_size
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= position_embedding_type
				SCREAMING_SNAKE_CASE     						= use_cache
				SCREAMING_SNAKE_CASE     						= pad_token_id
				SCREAMING_SNAKE_CASE     						= bos_token_id
				SCREAMING_SNAKE_CASE     						= eos_token_id







	@classmethod
	def  snake_case__     (     cls      :str   ,     lowercase      :Optional[Any]   ,     **lowercase      :Union[str, Any]			)		->      "PretrainedConfig":







				"""simple docstring"""






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

				if config_dict.get('''model_type'''			) == "bridgetower":
							SCREAMING_SNAKE_CASE     						= 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(lowercase   ,     **lowercase			)





class     lowerCamelCase      (       SCREAMING_SNAKE_CASE__					):
	UpperCamelCase_  :					List[str]    		=	'bridgetower'







	def __init__(     self      :Optional[int]   ,     lowercase      :Optional[Any]=True   ,     lowercase      :Tuple="gelu"   ,     lowercase      :Union[str, Any]=7_6_8   ,     lowercase      :Union[str, Any]=1   ,     lowercase      :Any=1e-05   ,     lowercase      :Tuple=False   ,     lowercase      :str="add"   ,     lowercase      :str=1_2   ,     lowercase      :Optional[Any]=6   ,     lowercase      :List[Any]=False   ,     lowercase      :Any=False   ,     lowercase      :Optional[Any]=None   ,     lowercase      :List[Any]=None   ,     **lowercase      :Tuple   ,     )		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= kwargs.pop('''text_config_dict'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''vision_config_dict'''   ,     lowercase			)

				super().__init__(**lowercase			)
				SCREAMING_SNAKE_CASE     						= share_cross_modal_transformer_layers
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= initializer_factor
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= share_link_tower_layers
				SCREAMING_SNAKE_CASE     						= link_tower_type
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= tie_word_embeddings
				SCREAMING_SNAKE_CASE     						= init_layernorm_from_vision_encoder

				if text_config is None:
							SCREAMING_SNAKE_CASE     						= {}
							logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'''			)

				if vision_config is None:
							SCREAMING_SNAKE_CASE     						= {}
							logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''			)

				SCREAMING_SNAKE_CASE     						= BridgeTowerTextConfig(**lowercase			)
				SCREAMING_SNAKE_CASE     						= BridgeTowerVisionConfig(**lowercase			)







	@classmethod
	def  snake_case__     (     cls      :List[str]   ,     lowercase      :Optional[Any]   ,     lowercase      :List[str]   ,     **lowercase      :Optional[int]			)		->      Optional[int]:







				"""simple docstring"""






				return cls(text_config=text_config.to_dict()   ,     vision_config=vision_config.to_dict()   ,     **lowercase			)







	def  snake_case__     (     self      :Dict			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= copy.deepcopy(self.__dict__			)
				SCREAMING_SNAKE_CASE     						= self.text_config.to_dict()
				SCREAMING_SNAKE_CASE     						= self.vision_config.to_dict()
				SCREAMING_SNAKE_CASE     						= self.__class__.model_type
				return output





from __future__ import annotations

from decimal import Decimal

from numpy import array

def lowercase    (  SCREAMING_SNAKE_CASE )			->							list[list[float]]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(SCREAMING_SNAKE_CASE ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
						# Calculate the determinant of the matrix
						SCREAMING_SNAKE_CASE_								=						float(
						    d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
						if determinant == 0:
									raise ValueError('This matrix has no inverse.' )

						# Creates a copy of the matrix with swapped positions of the elements
						SCREAMING_SNAKE_CASE_								=						[[0.0, 0.0], [0.0, 0.0]]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						matrix[1][1], matrix[0][0]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						-matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(SCREAMING_SNAKE_CASE ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(SCREAMING_SNAKE_CASE ) == 3
			    and len(matrix[0] ) == 3
			    and len(matrix[1] ) == 3
			    and len(matrix[2] ) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						SCREAMING_SNAKE_CASE_								=						float(
						    (
						        (d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
						        + (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
						        + (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
						    )
						    - (
						        (d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
						        + (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
						        + (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
						    ) )
						if determinant == 0:
									raise ValueError('This matrix has no inverse.' )

						# Creating cofactor matrix
						SCREAMING_SNAKE_CASE_								=						[
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						    [d(0.0 ), d(0.0 ), d(0.0 )],
						]
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][1] ) * d(matrix[2][2] )) - (
						    d(matrix[1][2] ) * d(matrix[2][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][0] ) * d(matrix[2][1] )) - (
						    d(matrix[1][1] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[2][2] )) - (
						    d(matrix[0][2] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][1] ) * d(matrix[1][2] )) - (
						    d(matrix[0][2] ) * d(matrix[1][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[1][1] )) - (
						    d(matrix[0][1] ) * d(matrix[1][0] )
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												SCREAMING_SNAKE_CASE_								=						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE )

        # Calculate the inverse of the matrix
						return [[float(d(SCREAMING_SNAKE_CASE ) ) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
