






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


snake_case__   :						Any													=   logging.get_logger(__name__)

snake_case__   :						List[Any]													=   {
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """instructblip_vision_model"""




  def __init__(  self		,		_UpperCAmelCase=1408		,		_UpperCAmelCase=6144		,		_UpperCAmelCase=39		,		_UpperCAmelCase=16		,		_UpperCAmelCase=224		,		_UpperCAmelCase=14		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=1e-6		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=1e-10		,		_UpperCAmelCase=True		,		**_UpperCAmelCase		,		)      ->			Any:
     super().__init__(**a_			)

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




  @classmethod
  def  _UpperCAmelCase  (  cls		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Any:
     cls._set_token_in_kwargs(a_			)

     UpperCamelCase_		,   UpperCamelCase_						=  cls.get_config_dict(a_		,		**a_			)

     # get the vision config dict if we are loading from InstructBlipConfig
     if config_dict.get('model_type'			) == "instructblip":
        UpperCamelCase_						=  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(a_		,		**a_			)




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """instructblip_qformer"""




  def __init__(  self		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=0		,		_UpperCAmelCase="absolute"		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1408		,		**_UpperCAmelCase		,		)      ->			Any:
     super().__init__(pad_token_id=a_		,		**a_			)

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




  @classmethod
  def  _UpperCAmelCase  (  cls		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Optional[int]:
     cls._set_token_in_kwargs(a_			)

     UpperCamelCase_		,   UpperCamelCase_						=  cls.get_config_dict(a_		,		**a_			)

     # get the qformer config dict if we are loading from InstructBlipConfig
     if config_dict.get('model_type'			) == "instructblip":
        UpperCamelCase_						=  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(a_		,		**a_			)




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """instructblip"""
  A_						       = True




  def __init__(  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=32		,		**_UpperCAmelCase			)      ->			Union[str, Any]:
     super().__init__(**a_			)

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

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

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

     UpperCamelCase_						=  InstructBlipVisionConfig(**a_			)
     UpperCamelCase_						=  InstructBlipQFormerConfig(**a_			)
     UpperCamelCase_						=  text_config['model_type'] if 'model_type' in text_config else 'opt'
     UpperCamelCase_						=  CONFIG_MAPPING[text_model_type](**a_			)

     UpperCamelCase_						=  self.text_config.tie_word_embeddings
     UpperCamelCase_						=  self.text_config.is_encoder_decoder

     UpperCamelCase_						=  num_query_tokens
     UpperCamelCase_						=  self.vision_config.hidden_size
     UpperCamelCase_						=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     UpperCamelCase_						=  1.0
     UpperCamelCase_						=  0.0_2




  @classmethod
  def  _UpperCAmelCase  (  cls		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		**_UpperCAmelCase		,		)      ->			Optional[Any]:
     return cls(
         vision_config=vision_config.to_dict()		,		qformer_config=qformer_config.to_dict()		,		text_config=text_config.to_dict()		,		**a_		,		)




  def  _UpperCAmelCase  (  self			)      ->			int:
     UpperCamelCase_						=  copy.deepcopy(self.__dict__			)
     UpperCamelCase_						=  self.vision_config.to_dict()
     UpperCamelCase_						=  self.qformer_config.to_dict()
     UpperCamelCase_						=  self.text_config.to_dict()
     UpperCamelCase_						=  self.__class__.model_type
     return output










from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  k_size // 2
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				lowerCAmelCase__		  =  1 / (2 * pi * sigma) * exp(-(square(snake_case__					) + square(snake_case__					)) / (2 * square(snake_case__					))					)
				return g







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.shape[0], image.shape[1]
				# dst image height and width
				lowerCAmelCase__		  =  height - k_size + 1
				lowerCAmelCase__		  =  width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				lowerCAmelCase__		  =  zeros((dst_height * dst_width, k_size * k_size)					)
				lowerCAmelCase__		  =  0
				for i, j in product(range(snake_case__					)    ,			range(snake_case__					)					):
								lowerCAmelCase__		  =  ravel(image[i : i + k_size, j : j + k_size]					)
								lowerCAmelCase__		  =  window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


if __name__ == "__main__":
	# read original image
	_lowerCAmelCase	:   List[str]    =   imread(R"../image_data/lena.jpg")
	# turn image in gray scale value
	_lowerCAmelCase	:   Optional[Any]    =   cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	_lowerCAmelCase	:   Optional[int]    =   gaussian_filter(gray, 3, sigma=1)
	_lowerCAmelCase	:   Tuple    =   gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow("gaussian filter with 3x3 mask", gaussianaxa)
	imshow("gaussian filter with 5x5 mask", gaussianaxa)
	waitKey()
