




"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        SCREAMING_SNAKE_CASE__   (						lowercase__      ):
   snake_case			   =       "ClapFeatureExtractor"
   snake_case			   =       ("RobertaTokenizer", "RobertaTokenizerFast")
   def __init__(       self			:				Tuple   ,	SCREAMING_SNAKE_CASE_			:				List[str]   ,	SCREAMING_SNAKE_CASE_			:				Tuple							):
          super().__init__(UpperCAmelCase__   ,	UpperCAmelCase__							)
   def __call__(       self			:				Tuple   ,	SCREAMING_SNAKE_CASE_			:				str=None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Any]=None   ,	SCREAMING_SNAKE_CASE_			:				str=None   ,	**SCREAMING_SNAKE_CASE_			:				Union[str, Any]							):
          lowerCamelCase__          =						kwargs.pop("""sampling_rate"""   ,	UpperCAmelCase__							)

          if text is None and audios is None:
                 raise ValueError("""You have to specify either text or audios. Both cannot be none."""							)

          if text is not None:
                 lowerCamelCase__          =						self.tokenizer(UpperCAmelCase__   ,	return_tensors=UpperCAmelCase__   ,	**UpperCAmelCase__							)

          if audios is not None:
                 lowerCamelCase__          =						self.feature_extractor(
                     UpperCAmelCase__   ,	sampling_rate=UpperCAmelCase__   ,	return_tensors=UpperCAmelCase__   ,	**UpperCAmelCase__							)

          if text is not None and audios is not None:
                 lowerCamelCase__          =						audio_features.input_features
                 return encoding
          elif text is not None:
                 return encoding
          else:
                 return BatchEncoding(data=dict(**UpperCAmelCase__							)   ,	tensor_type=UpperCAmelCase__							)
   def    __UpperCAmelCase       (       self			:				Tuple   ,	*SCREAMING_SNAKE_CASE_			:				Union[str, Any]   ,	**SCREAMING_SNAKE_CASE_			:				Tuple							):
          return self.tokenizer.batch_decode(*UpperCAmelCase__   ,	**UpperCAmelCase__							)
   def    __UpperCAmelCase       (       self			:				List[Any]   ,	*SCREAMING_SNAKE_CASE_			:				str   ,	**SCREAMING_SNAKE_CASE_			:				Union[str, Any]							):
          return self.tokenizer.decode(*UpperCAmelCase__   ,	**UpperCAmelCase__							)





   @property
   def    __UpperCAmelCase       (       self			:				List[str]							):
          lowerCamelCase__          =						self.tokenizer.model_input_names
          lowerCamelCase__          =						self.feature_extractor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)









'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class 		__SCREAMING_SNAKE_CASE							:







				def __init__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										if dst_width < 0 or dst_height < 0:
																raise ValueError('''Destination width/height should be > 0'''   )

										lowercase :							Union[str, Any]    		=img
										lowercase :							Union[str, Any]    		=img.shape[1]
										lowercase :							str    		=img.shape[0]
										lowercase :							Union[str, Any]    		=dst_width
										lowercase :							str    		=dst_height

										lowercase :							str    		=self.src_w / self.dst_w
										lowercase :							Optional[Any]    		=self.src_h / self.dst_h

										lowercase :							int    		=(
										    np.ones((self.dst_h, self.dst_w, 3)		,					np.uinta   ) * 255
										)







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										for i in range(self.dst_h   ):
																for j in range(self.dst_w   ):
																						lowercase :							List[Any]    		=self.img[self.get_y(UpperCAmelCase__   )][self.get_x(UpperCAmelCase__   )]







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_x * x   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return int(self.ratio_y * y   )


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       800, 600
							UpperCamelCase_    						=       imread("""image_data/lena.jpg""", 1)
							UpperCamelCase_    						=       NearestNeighbour(im, dst_w, dst_h)
							n.process()

							imshow(
							    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
							)
							waitKey(0)
							destroyAllWindows()





