

'''simple docstring'''







import copy
import os
from typing import Union

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


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_text_model"
    lowercase						       =		["past_key_values"]
    lowercase						       =		{
        "hidden_size": "hidden_size",
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }



    def __init__(       self		,					UpperCamelCase__=50244		,					UpperCamelCase__=768		,					UpperCamelCase__=64		,					UpperCamelCase__=2048		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					UpperCamelCase__=0.1		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=1.0		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=0		,					UpperCamelCase__=False		,					UpperCamelCase__=0		,					UpperCamelCase__=1		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   d_kv
           A_						       =   d_ff
           A_						       =   num_layers
           A_						       =   num_heads
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   dropout_rate
           A_						       =   layer_norm_epsilon
           A_						       =   initializer_factor
           A_						       =   use_cache

           A_						       =   eos_token_id
           A_						       =   decoder_start_token_id

           # for backwards compatibility
           A_						       =   dense_act_fn

           super().__init__(
               pad_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					decoder_start_token_id=UpperCamelCase__		,					tie_word_embeddings=UpperCamelCase__		,					is_decoder=UpperCamelCase__		,					**UpperCamelCase__		,					)







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  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(UpperCamelCase__		,					**UpperCamelCase__							)


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_vision_model"



    def __init__(       self		,					UpperCamelCase__=768		,					UpperCamelCase__=768		,					UpperCamelCase__=2048		,					UpperCamelCase__=64		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=1e-1_0		,					UpperCamelCase__=1.0		,					UpperCamelCase__=4096		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					**UpperCamelCase__		,					)    ->   int:





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

           A_						       =   hidden_size
           A_						       =   patch_embed_hidden_size
           A_						       =   d_ff
           A_						       =   dropout_rate
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   initializer_range
           A_						       =   initializer_factor
           A_						       =   attention_dropout
           A_						       =   layer_norm_eps
           A_						       =   dense_act_fn
           A_						       =   seq_len
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   d_kv







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__							)

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""							) == "pix2struct":
                  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(UpperCamelCase__		,					**UpperCamelCase__							)


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct"
    lowercase						       =		True



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=1.0		,					UpperCamelCase__=0.02		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   str:





           '''simple docstring'''
           super().__init__(tie_word_embeddings=UpperCamelCase__		,					is_encoder_decoder=UpperCamelCase__		,					**UpperCamelCase__							)

           if text_config is None:
                  A_						       =   {}
                  logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""							)

           if vision_config is None:
                  A_						       =   {}
                  logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""							)

           A_						       =   PixaStructTextConfig(**UpperCamelCase__							)
           A_						       =   PixaStructVisionConfig(**UpperCamelCase__							)

           A_						       =   self.text_config.decoder_start_token_id
           A_						       =   self.text_config.pad_token_id
           A_						       =   self.text_config.eos_token_id

           A_						       =   initializer_factor
           A_						       =   initializer_range

           A_						       =   self.initializer_range
           A_						       =   self.initializer_range

           A_						       =   is_vqa



    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   Optional[int]:





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







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   copy.deepcopy(self.__dict__							)
           A_						       =   self.text_config.to_dict()
           A_						       =   self.vision_config.to_dict()
           A_						       =   self.__class__.model_type
           return output



'''simple docstring'''







import numpy as np
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = 1e-12,							UpperCAmelCase__ = 1_00,							)      ->      tuple[float, np.ndarray]:
       assert np.shape(UpperCAmelCase__					)[0] == np.shape(UpperCAmelCase__					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase__					)[0] == np.shape(UpperCAmelCase__					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase__					) == np.iscomplexobj(UpperCAmelCase__					)
       A_						       =   np.iscomplexobj(UpperCAmelCase__					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase__,							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.

       A_						       =   False
       A_						       =   0
       A_						       =   0
       A_						       =   1e12

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

              # Check convergence.
              A_						       =   np.abs(lambda_ - lambda_previous					) / lambda_
              iterations += 1

              if error <= error_tol or iterations >= max_iterations:
                     A_						       =   True

              A_						       =   lambda_

       if is_complex:
              A_						       =   np.real(lambda_					)

       return lambda_, vector
def    UpperCAmelCase__     (   )      ->      None:
       A_						       =   np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
       A_						       =   np.array([41, 4, 20]					)
       A_						       =   real_input_matrix.astype(np.complexaaa					)
       A_						       =   np.triu(1j * complex_input_matrix,							1					)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       A_						       =   np.array([41, 4, 20]					).astype(np.complexaaa					)

       for problem_type in ["real", "complex"]:
              if problem_type == "real":
                     A_						       =   real_input_matrix
                     A_						       =   real_vector
              elif problem_type == "complex":
                     A_						       =   complex_input_matrix
                     A_						       =   complex_vector

              # Our implementation.
              A_       ,      A_						       =   power_iteration(UpperCAmelCase__,							UpperCAmelCase__					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              A_       ,      A_						       =   np.linalg.eigh(UpperCAmelCase__					)
              # Last eigenvalue is the maximum one.
              A_						       =   eigen_values[-1]
              # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
              A_						       =   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(UpperCAmelCase__					) - np.abs(UpperCAmelCase__					)					) <= 1e-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()
