


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowerCamelCase						=		logging.get_logger(__name__)





@add_end_docstrings(_lowercase)
class  _a							(    _lowercase):






	"""simple docstring"""




	def __init__( self						:		Union[str, Any]		, *_SCREAMING_SNAKE_CASE						:		List[Any]		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	List[Any]:
			super().__init__(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			self.check_model_type(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[str]=None		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	List[str]:
			lowerCAmelCase__       :      Optional[Any]   					= {}, {}
			if padding is not None:
					lowerCAmelCase__       :      str   					= padding
			if truncation is not None:
					lowerCAmelCase__       :      Tuple   					= truncation
			if top_k is not None:
					lowerCAmelCase__       :      List[str]   					= top_k
			return preprocess_params, {}, postprocess_params




	def __call__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Union["Image.Image", str]		, _SCREAMING_SNAKE_CASE						:		str = None		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	Dict:
			if isinstance(_SCREAMING_SNAKE_CASE		, (Image.Image, str)		) and isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		):
					lowerCAmelCase__       :      List[str]   					= {'''image''': image, '''question''': question}
			else:
					lowerCAmelCase__       :      Any   					= image
			lowerCAmelCase__       :      Tuple   					= super().__call__(_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			return results




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		int=False		, _SCREAMING_SNAKE_CASE						:		List[str]=False		)->	Any:
			lowerCAmelCase__       :      Tuple   					= load_image(inputs['''image''']		)
			lowerCAmelCase__       :      Dict   					= self.tokenizer(
			    inputs['''question''']		, return_tensors=self.framework		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      str   					= self.image_processor(images=_SCREAMING_SNAKE_CASE		, return_tensors=self.framework		)
			model_inputs.update(_SCREAMING_SNAKE_CASE		)
			return model_inputs




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Any:
			lowerCAmelCase__       :      List[str]   					= self.model(**_SCREAMING_SNAKE_CASE		)
			return model_outputs




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Dict=5		)->	Optional[int]:
			if top_k > self.model.config.num_labels:
					lowerCAmelCase__       :      Any   					= self.model.config.num_labels

			if self.framework == "pt":
					lowerCAmelCase__       :      Optional[int]   					= model_outputs.logits.sigmoid()[0]
					lowerCAmelCase__       :      List[Any]   					= probs.topk(_SCREAMING_SNAKE_CASE		)
			else:
					raise ValueError(F'Unsupported framework: {self.framework}'		)

			lowerCAmelCase__       :      List[Any]   					= scores.tolist()
			lowerCAmelCase__       :      Any   					= ids.tolist()
			return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)]








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 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






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

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

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


if __name__ == "__main__":
		# read original image
		lowerCamelCase						=		imread(R'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowerCamelCase						=		cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowerCamelCase						=		gaussian_filter(gray, 3, sigma=1)
		lowerCamelCase						=		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()




