




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase       :	Tuple									=     logging.get_logger(__name__)

lowerCamelCase       :	List[str]									=     {
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}







class 						A(		lowerCAmelCase__     ):



 '''simple docstring'''


 UpperCamelCase		     =		"blip_2_vision_model"

 def __init__(       self      :						List[str]		,							A_      :						Optional[Any]=1408		,							A_      :						str=6144		,							A_      :						str=39		,							A_      :						List[Any]=16		,							A_      :						Optional[int]=224		,							A_      :						Any=14		,							A_      :						List[str]="gelu"		,							A_      :						List[str]=0.00001		,							A_      :						Optional[Any]=0.0		,							A_      :						int=1E-10		,							A_      :						List[Any]=True		,							**A_      :						Union[str, Any]		,							)     ->			Dict:

       """simple docstring"""






       super().__init__(**_SCREAMING_SNAKE_CASE						)

       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					patch_size
       lowerCamelCase_	  =					image_size
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					attention_dropout
       lowerCamelCase_	  =					layer_norm_eps
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					qkv_bias





 @classmethod
 def        a__				(       cls      :						Any		,							A_      :						Any		,							**A_      :						int						)     ->			"PretrainedConfig":

       """simple docstring"""






       cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE						)

       lowerCamelCase_  ,       lowerCamelCase_	  =					cls.get_config_dict(_SCREAMING_SNAKE_CASE		,							**_SCREAMING_SNAKE_CASE						)

       # get the vision config dict if we are loading from Blip2Config
       if config_dict.get('model_type'						) == "blip-2":
             lowerCamelCase_	  =					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(_SCREAMING_SNAKE_CASE		,							**_SCREAMING_SNAKE_CASE						)







class 						A(		lowerCAmelCase__     ):



 '''simple docstring'''


 UpperCamelCase		     =		"blip_2_qformer"

 def __init__(       self      :						Any		,							A_      :						Tuple=30522		,							A_      :						str=768		,							A_      :						List[Any]=12		,							A_      :						List[Any]=12		,							A_      :						List[str]=3072		,							A_      :						Dict="gelu"		,							A_      :						List[Any]=0.1		,							A_      :						Union[str, Any]=0.1		,							A_      :						Optional[Any]=512		,							A_      :						Optional[int]=0.02		,							A_      :						int=1E-12		,							A_      :						Optional[Any]=0		,							A_      :						str="absolute"		,							A_      :						Tuple=2		,							A_      :						Optional[int]=1408		,							**A_      :						Optional[Any]		,							)     ->			List[Any]:

       """simple docstring"""






       super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		,							**_SCREAMING_SNAKE_CASE						)

       lowerCamelCase_	  =					vocab_size
       lowerCamelCase_	  =					hidden_size
       lowerCamelCase_	  =					num_hidden_layers
       lowerCamelCase_	  =					num_attention_heads
       lowerCamelCase_	  =					hidden_act
       lowerCamelCase_	  =					intermediate_size
       lowerCamelCase_	  =					hidden_dropout_prob
       lowerCamelCase_	  =					attention_probs_dropout_prob
       lowerCamelCase_	  =					max_position_embeddings
       lowerCamelCase_	  =					initializer_range
       lowerCamelCase_	  =					layer_norm_eps
       lowerCamelCase_	  =					position_embedding_type
       lowerCamelCase_	  =					cross_attention_frequency
       lowerCamelCase_	  =					encoder_hidden_size





 @classmethod
 def        a__				(       cls      :						Dict		,							A_      :						Union[str, Any]		,							**A_      :						Optional[Any]						)     ->			"PretrainedConfig":

       """simple docstring"""






       cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE						)

       lowerCamelCase_  ,       lowerCamelCase_	  =					cls.get_config_dict(_SCREAMING_SNAKE_CASE		,							**_SCREAMING_SNAKE_CASE						)

       # get the qformer config dict if we are loading from Blip2Config
       if config_dict.get('model_type'						) == "blip-2":
             lowerCamelCase_	  =					config_dict['qformer_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(_SCREAMING_SNAKE_CASE		,							**_SCREAMING_SNAKE_CASE						)







class 						A(		lowerCAmelCase__     ):



 '''simple docstring'''


 UpperCamelCase		     =		"blip-2"
 UpperCamelCase		     =		True

 def __init__(       self      :						int		,							A_      :						Any=None		,							A_      :						Tuple=None		,							A_      :						int=None		,							A_      :						Any=32		,							**A_      :						Optional[int]						)     ->			int:

       """simple docstring"""






       super().__init__(**_SCREAMING_SNAKE_CASE						)

       if vision_config is None:
             lowerCamelCase_	  =					{}
             logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'						)

       if qformer_config is None:
             lowerCamelCase_	  =					{}
             logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'						)

       if text_config is None:
             lowerCamelCase_	  =					{}
             logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'						)

       lowerCamelCase_	  =					BlipaVisionConfig(**_SCREAMING_SNAKE_CASE						)
       lowerCamelCase_	  =					BlipaQFormerConfig(**_SCREAMING_SNAKE_CASE						)
       lowerCamelCase_	  =					text_config['model_type'] if 'model_type' in text_config else 'opt'
       lowerCamelCase_	  =					CONFIG_MAPPING[text_model_type](**_SCREAMING_SNAKE_CASE						)

       lowerCamelCase_	  =					self.text_config.tie_word_embeddings
       lowerCamelCase_	  =					self.text_config.is_encoder_decoder

       lowerCamelCase_	  =					num_query_tokens
       lowerCamelCase_	  =					self.vision_config.hidden_size
       lowerCamelCase_	  =					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
       lowerCamelCase_	  =					1.0
       lowerCamelCase_	  =					0.02

 @classmethod
 def        a__				(       cls      :						List[Any]		,							A_      :						Any		,							A_      :						Tuple		,							A_      :						Dict		,							**A_      :						Any		,							)     ->			Any:

       """simple docstring"""






       return cls(
           vision_config=vision_config.to_dict()		,							qformer_config=qformer_config.to_dict()		,							text_config=text_config.to_dict()		,							**_SCREAMING_SNAKE_CASE		,							)





 def        a__				(       self      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






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





import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					lbp.get_neighbors_pixel(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase_	  =					np.zeros((image.shape[0], image.shape[1])  )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0			,							image.shape[0]  ):
            for j in range(0			,							image.shape[1]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



