

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





class 					__UpperCAmelCase     :



   def __init__(							self							:     Optional[Any],		__A							:     Optional[Any],		__A							:     int,		__A							:     int						):
     if dst_width < 0 or dst_height < 0:
       raise ValueError('''Destination width/height should be > 0'''						)

     UpperCAmelCase       :							Union[str, Any]  = img
     UpperCAmelCase       :							Optional[int]  = img.shape[1]
     UpperCAmelCase       :							Dict  = img.shape[0]
     UpperCAmelCase       :							Dict  = dst_width
     UpperCAmelCase       :							Any  = dst_height

     UpperCAmelCase       :							str  = self.src_w / self.dst_w
     UpperCAmelCase       :							Dict  = self.src_h / self.dst_h

     UpperCAmelCase       :							Dict  = (
         np.ones((self.dst_h, self.dst_w, 3),		np.uinta						) * 2_5_5
     )



   def        __magic_name__    (							self							:     Tuple						):
     for i in range(self.dst_h						):
       for j in range(self.dst_w						):
         UpperCAmelCase       :							List[str]  = self.img[self.get_y(__UpperCAmelCase						)][self.get_x(__UpperCAmelCase						)]



   def        __magic_name__    (							self							:     Dict,		__A							:     int						):
     return int(self.ratio_x * x						)





   def        __magic_name__    (							self							:     Optional[int],		__A							:     int						):
     return int(self.ratio_y * y						)


if __name__ == "__main__":
     _lowerCamelCase   ,    _lowerCamelCase				:   str					   =							8_0_0, 6_0_0
     _lowerCamelCase				:   int					   =							imread("image_data/lena.jpg", 1)
     _lowerCamelCase				:   Any					   =							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()









"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """dpr"""






    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    int=30522				, __UpperCAmelCase				:    Union[str, Any]=768				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    List[str]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    Optional[int]="gelu"				, __UpperCAmelCase				:    Any=0.1				, __UpperCAmelCase				:    Union[str, Any]=0.1				, __UpperCAmelCase				:    str=512				, __UpperCAmelCase				:    List[str]=2				, __UpperCAmelCase				:    Tuple=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    List[str]=0				, __UpperCAmelCase				:    str="absolute"				, __UpperCAmelCase				:    int = 0				, **__UpperCAmelCase				:    Tuple				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			vocab_size
      a						:		Optional[Any]      							=			hidden_size
      a						:		Union[str, Any]      							=			num_hidden_layers
      a						:		Dict      							=			num_attention_heads
      a						:		int      							=			hidden_act
      a						:		Any      							=			intermediate_size
      a						:		Any      							=			hidden_dropout_prob
      a						:		Dict      							=			attention_probs_dropout_prob
      a						:		Any      							=			max_position_embeddings
      a						:		Union[str, Any]      							=			type_vocab_size
      a						:		Optional[Any]      							=			initializer_range
      a						:		Dict      							=			layer_norm_eps
      a						:		int      							=			projection_dim
      a						:		str      							=			position_embedding_type






