import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
      from ...processing_utils import ProcessorMixin
      from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_  : int								=					logging.get_logger(__name__)

lowercase_  : int								=					{
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   '''owlvit_text_model'''
    def __init__(	self			,			lowerCAmelCase=49408			,			lowerCAmelCase=512			,			lowerCAmelCase=2048			,			lowerCAmelCase=12			,			lowerCAmelCase=8			,			lowerCAmelCase=16			,			lowerCAmelCase="quick_gelu"			,			lowerCAmelCase=1e-5			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1.0			,			lowerCAmelCase=0			,			lowerCAmelCase=49406			,			lowerCAmelCase=49407			,			**lowerCAmelCase			,			)							->       str:
     super().__init__(pad_token_id=lowerCamelCase_			,			bos_token_id=lowerCamelCase_			,			eos_token_id=lowerCamelCase_			,			**lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: Optional[int]=   vocab_size
     SCREAMING_SNAKE_CASE__: Dict=   hidden_size
     SCREAMING_SNAKE_CASE__: Optional[Any]=   intermediate_size
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   num_hidden_layers
     SCREAMING_SNAKE_CASE__: int=   num_attention_heads
     SCREAMING_SNAKE_CASE__: List[str]=   max_position_embeddings
     SCREAMING_SNAKE_CASE__: Any=   hidden_act
     SCREAMING_SNAKE_CASE__: int=   layer_norm_eps
     SCREAMING_SNAKE_CASE__: List[Any]=   attention_dropout
     SCREAMING_SNAKE_CASE__: List[str]=   initializer_range
     SCREAMING_SNAKE_CASE__: Tuple=   initializer_factor



    @classmethod
    def     UpperCamelCase_					(	cls			,			lowerCAmelCase			,			**lowerCAmelCase )							->       "PretrainedConfig":
     cls._set_token_in_kwargs(lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: List[Any]=   cls.get_config_dict(lowerCamelCase_			,			**lowerCamelCase_ )

     # get the text config dict if we are loading from OwlViTConfig
     if config_dict.get('''model_type''' ) == "owlvit":
      SCREAMING_SNAKE_CASE__: Optional[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(lowerCamelCase_			,			**lowerCamelCase_ )






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   '''owlvit_vision_model'''
    def __init__(	self			,			lowerCAmelCase=768			,			lowerCAmelCase=3072			,			lowerCAmelCase=12			,			lowerCAmelCase=12			,			lowerCAmelCase=3			,			lowerCAmelCase=768			,			lowerCAmelCase=32			,			lowerCAmelCase="quick_gelu"			,			lowerCAmelCase=1e-5			,			lowerCAmelCase=0.0			,			lowerCAmelCase=0.02			,			lowerCAmelCase=1.0			,			**lowerCAmelCase			,			)							->       List[str]:
     super().__init__(**lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: Optional[int]=   hidden_size
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   intermediate_size
     SCREAMING_SNAKE_CASE__: Dict=   num_hidden_layers
     SCREAMING_SNAKE_CASE__: Tuple=   num_attention_heads
     SCREAMING_SNAKE_CASE__: str=   num_channels
     SCREAMING_SNAKE_CASE__: Optional[int]=   image_size
     SCREAMING_SNAKE_CASE__: Dict=   patch_size
     SCREAMING_SNAKE_CASE__: str=   hidden_act
     SCREAMING_SNAKE_CASE__: int=   layer_norm_eps
     SCREAMING_SNAKE_CASE__: Optional[Any]=   attention_dropout
     SCREAMING_SNAKE_CASE__: List[Any]=   initializer_range
     SCREAMING_SNAKE_CASE__: Optional[int]=   initializer_factor



    @classmethod
    def     UpperCamelCase_					(	cls			,			lowerCAmelCase			,			**lowerCAmelCase )							->       "PretrainedConfig":
     cls._set_token_in_kwargs(lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: str=   cls.get_config_dict(lowerCamelCase_			,			**lowerCamelCase_ )

     # get the vision config dict if we are loading from OwlViTConfig
     if config_dict.get('''model_type''' ) == "owlvit":
      SCREAMING_SNAKE_CASE__: List[str]=   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(lowerCamelCase_			,			**lowerCamelCase_ )






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   '''owlvit'''
    __a						=   True
    def __init__(	self			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase=512			,			lowerCAmelCase=2.6592			,			lowerCAmelCase=True			,			**lowerCAmelCase			,			)							->       Union[str, Any]:
     super().__init__(**lowerCamelCase_ )

     if text_config is None:
      SCREAMING_SNAKE_CASE__: Any=   {}
      logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )

     if vision_config is None:
      SCREAMING_SNAKE_CASE__: List[Any]=   {}
      logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )

     SCREAMING_SNAKE_CASE__: List[Any]=   OwlViTTextConfig(**lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__: Any=   OwlViTVisionConfig(**lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: Dict=   projection_dim
     SCREAMING_SNAKE_CASE__: Dict=   logit_scale_init_value
     SCREAMING_SNAKE_CASE__: str=   return_dict
     SCREAMING_SNAKE_CASE__: List[Any]=   1.0
    @classmethod
    def     UpperCamelCase_					(	cls			,			lowerCAmelCase			,			**lowerCAmelCase )							->       "PretrainedConfig":
     cls._set_token_in_kwargs(lowerCamelCase_ )

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   cls.get_config_dict(lowerCamelCase_			,			**lowerCamelCase_ )

     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(lowerCamelCase_			,			**lowerCamelCase_ )
    @classmethod
    def     UpperCamelCase_					(	cls			,			lowerCAmelCase			,			lowerCAmelCase			,			**lowerCAmelCase )							->       str:
     SCREAMING_SNAKE_CASE__: int=   {}
     SCREAMING_SNAKE_CASE__: Any=   text_config
     SCREAMING_SNAKE_CASE__: str=   vision_config

     return cls.from_dict(lowerCamelCase_			,			**lowerCamelCase_ )



    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: List[Any]=   copy.deepcopy(self.__dict__ )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.text_config.to_dict()
     SCREAMING_SNAKE_CASE__: Any=   self.vision_config.to_dict()
     SCREAMING_SNAKE_CASE__: int=   self.__class__.model_type
     return output






class 		_lowerCamelCase		( UpperCamelCase_     ):
    @property
    def     UpperCamelCase_					(	self )							->       Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
         ] )
    @property
    def     UpperCamelCase_					(	self )							->       Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('''logits_per_image''', {0: '''batch'''}),
             ('''logits_per_text''', {0: '''batch'''}),
             ('''text_embeds''', {0: '''batch'''}),
             ('''image_embeds''', {0: '''batch'''}),
         ] )
    @property
    def     UpperCamelCase_					(	self )							->       float:
     return 1e-4
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase = -1			,			lowerCAmelCase = -1			,			lowerCAmelCase = None			,			)							->       Mapping[str, Any]:
     SCREAMING_SNAKE_CASE__: str=   super().generate_dummy_inputs(
         processor.tokenizer			,			batch_size=lowerCamelCase_			,			seq_length=lowerCamelCase_			,			framework=lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__: Any=   super().generate_dummy_inputs(
         processor.image_processor			,			batch_size=lowerCamelCase_			,			framework=lowerCamelCase_ )
     return {**text_input_dict, **image_input_dict}



    @property
    def     UpperCamelCase_					(	self )							->       int:
     return 14


'''simple docstring'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''simple docstring'''
   assert np.shape(_A     )[0] == np.shape(_A     )[1]
   # Ensure proper dimensionality.
   assert np.shape(_A     )[0] == np.shape(_A     )[0]
   # Ensure inputs are either both complex or both real
   assert np.iscomplexobj(_A     ) == np.iscomplexobj(_A     )
   UpperCAmelCase__				:					int 					=	np.iscomplexobj(_A     )
   if is_complex:
      # Ensure complex input_matrix is Hermitian
      assert np.array_equal(_A	,      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__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

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

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

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

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

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()




