"""simple docstring"""





import warnings

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




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["image_processor", "tokenizer"]
				a			=						"ChineseCLIPImageProcessor"
				a			=						("BertTokenizer", "BertTokenizerFast")





				def __init__(      self							:							Dict      ,    _A							:							Tuple=None      ,    _A							:							List[Any]=None      ,    **_A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""      ,    _A      ,    )
																		_SCREAMING_SNAKE_CASE		:						str			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						int			  =    image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError("""You need to specify an `image_processor`.""")
											if tokenizer is None:
																		raise ValueError("""You need to specify a `tokenizer`.""")

											super().__init__(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.image_processor





				def __call__(      self							:							Optional[int]      ,    _A							:							Optional[Any]=None      ,    _A							:							Any=None      ,    _A							:							Tuple=None      ,    **_A							:							int):



											"""simple docstring"""







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

											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(_A      ,    return_tensors=_A      ,    **_A)

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processor(_A      ,    return_tensors=_A      ,    **_A)

											if text is not None and images is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_A)      ,    tensor_type=_A)





				def 		_lowerCAmelCase		(      self							:							str      ,    *_A							:							Any      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.batch_decode(*_A      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    *_A							:							List[Any]      ,    **_A							:							Any):



											"""simple docstring"""







											return self.tokenizer.decode(*_A      ,    **_A)





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





				@property
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,    _A      ,    )
											return self.image_processor_class



"""simple docstring"""





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




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if dst_width < 0 or dst_height < 0:
																		raise ValueError("""Destination width/height should be > 0""")

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			lowerCAmelCase_     =	imread('''image_data/lena.jpg''', 1)
			lowerCAmelCase_     =	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()


