






"""simple docstring"""



import copy
import os
from typing import Union

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


_lowerCAmelCase	:	Any				=      logging.get_logger(__name__)

_lowerCAmelCase	:	List[str]				=      {
    """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__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''bridgetower_vision_model'''
   def __init__(		self      :	Optional[int]							,			snake_case__      :	str=7_6_8							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	List[str]=3							,			snake_case__      :	Tuple=1_6							,			snake_case__      :	Tuple=2_8_8							,			snake_case__      :	int=1							,			snake_case__      :	str=1e-05							,			snake_case__      :	Any=False							,			snake_case__      :	Dict=True							,			snake_case__      :	Dict=False							,			**snake_case__      :	Tuple							,			):


        '''simple docstring'''
        super().__init__(**snake_case__							)
        UpperCAmelCase__						:    str							=							hidden_size
        UpperCAmelCase__						:    str							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_channels
        UpperCAmelCase__						:    str							=							patch_size
        UpperCAmelCase__						:    List[str]							=							image_size
        UpperCAmelCase__						:    Optional[int]							=							initializer_factor
        UpperCAmelCase__						:    List[Any]							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							stop_gradient
        UpperCAmelCase__						:    Tuple							=							share_layernorm
        UpperCAmelCase__						:    Dict							=							remove_last_layer




   @classmethod
   def       __a							(		cls      :	int							,			snake_case__      :	Union[str, os.PathLike]							,			**snake_case__      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							cls.get_config_dict(snake_case__							,			**snake_case__							)

        if config_dict.get("model_type"							) == "bridgetower":
             UpperCAmelCase__						:    Union[str, Any]							=							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(snake_case__							,			**snake_case__							)




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


        '''simple docstring'''
        super().__init__(**snake_case__							)

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




   @classmethod
   def       __a							(		cls      :	str							,			snake_case__      :	Union[str, os.PathLike]							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							cls.get_config_dict(snake_case__							,			**snake_case__							)

        if config_dict.get("model_type"							) == "bridgetower":
             UpperCAmelCase__						:    List[Any]							=							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(snake_case__							,			**snake_case__							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''bridgetower'''
   def __init__(		self      :	Optional[int]							,			snake_case__      :	Dict=True							,			snake_case__      :	Tuple="gelu"							,			snake_case__      :	List[Any]=7_6_8							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[str]=1e-05							,			snake_case__      :	str=False							,			snake_case__      :	List[Any]="add"							,			snake_case__      :	str=1_2							,			snake_case__      :	Any=6							,			snake_case__      :	List[str]=False							,			snake_case__      :	Union[str, Any]=False							,			snake_case__      :	Union[str, Any]=None							,			snake_case__      :	List[Any]=None							,			**snake_case__      :	str							,			):


        '''simple docstring'''
        # TODO: remove this once the Hub files are updated.
        UpperCAmelCase__						:    Any							=							kwargs.pop("text_config_dict"							,			snake_case__							)
        UpperCAmelCase__						:    Any							=							kwargs.pop("vision_config_dict"							,			snake_case__							)

        super().__init__(**snake_case__							)
        UpperCAmelCase__						:    Any							=							share_cross_modal_transformer_layers
        UpperCAmelCase__						:    List[Any]							=							hidden_act
        UpperCAmelCase__						:    List[Any]							=							hidden_size
        UpperCAmelCase__						:    List[str]							=							initializer_factor
        UpperCAmelCase__						:    Union[str, Any]							=							layer_norm_eps
        UpperCAmelCase__						:    Union[str, Any]							=							share_link_tower_layers
        UpperCAmelCase__						:    List[Any]							=							link_tower_type
        UpperCAmelCase__						:    List[Any]							=							num_attention_heads
        UpperCAmelCase__						:    int							=							num_hidden_layers
        UpperCAmelCase__						:    Dict							=							tie_word_embeddings
        UpperCAmelCase__						:    List[Any]							=							init_layernorm_from_vision_encoder

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

        if vision_config is None:
             UpperCAmelCase__						:    List[str]							=							{}
             logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."							)

        UpperCAmelCase__						:    List[Any]							=							BridgeTowerTextConfig(**snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							BridgeTowerVisionConfig(**snake_case__							)
   @classmethod
   def       __a							(		cls      :	Dict							,			snake_case__      :	BridgeTowerTextConfig							,			snake_case__      :	BridgeTowerVisionConfig							,			**snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        return cls(text_config=text_config.to_dict()							,			vision_config=vision_config.to_dict()							,			**snake_case__							)




   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							copy.deepcopy(self.__dict__							)
        UpperCAmelCase__						:    int							=							self.text_config.to_dict()
        UpperCAmelCase__						:    Optional[Any]							=							self.vision_config.to_dict()
        UpperCAmelCase__						:    List[Any]							=							self.__class__.model_type
        return output










"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(snake_case			,					input_matrix.conj().T				)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max				) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


