






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


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Dict       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       if dst_width < 0 or dst_height < 0:
         raise ValueError("Destination width/height should be > 0"   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     img
       SCREAMING_SNAKE_CASE							:   Any						    =     img.shape[1]
       SCREAMING_SNAKE_CASE							:   Any						    =     img.shape[0]
       SCREAMING_SNAKE_CASE							:   Tuple						    =     dst_width
       SCREAMING_SNAKE_CASE							:   str						    =     dst_height

       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.src_w / self.dst_w
       SCREAMING_SNAKE_CASE							:   int						    =     self.src_h / self.dst_h

       SCREAMING_SNAKE_CASE							:   str						    =     (
           np.ones((self.dst_h, self.dst_w, 3)       ,			np.uinta   ) * 255
       )


     def 					_A							(					self							:						List[Any]   ):
       for i in range(self.dst_h   ):
         for j in range(self.dst_w   ):
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.img[self.get_y(UpperCAmelCase_   )][self.get_x(UpperCAmelCase_   )]


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						int   ):
       return int(self.ratio_x * x   )






     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						int   ):
       return int(self.ratio_y * y   )


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









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Any  					=    'table-transformer'
   _lowercase     :       List[Any]  					=    ['past_key_values']
   _lowercase     :       Union[str, Any]  					=    {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(				self	:    List[Any]			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Any=3			,						lowerCamelCase__	:    Any=1_0_0			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[Any]=2_0_4_8			,						lowerCamelCase__	:    List[str]=8			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[str]=2_0_4_8			,						lowerCamelCase__	:    Optional[int]=8			,						lowerCamelCase__	:    Union[str, Any]=0.0			,						lowerCamelCase__	:    Any=0.0			,						lowerCamelCase__	:    str=True			,						lowerCamelCase__	:    Optional[Any]="relu"			,						lowerCamelCase__	:    Optional[int]=2_5_6			,						lowerCamelCase__	:    List[str]=0.1			,						lowerCamelCase__	:    str=0.0			,						lowerCamelCase__	:    Optional[int]=0.0			,						lowerCamelCase__	:    Dict=0.02			,						lowerCamelCase__	:    Union[str, Any]=1.0			,						lowerCamelCase__	:    Union[str, Any]=False			,						lowerCamelCase__	:    Optional[int]="sine"			,						lowerCamelCase__	:    str="resnet50"			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    Any=1			,						lowerCamelCase__	:    int=5			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    List[str]=1			,						lowerCamelCase__	:    Union[str, Any]=1			,						lowerCamelCase__	:    Optional[Any]=5			,						lowerCamelCase__	:    Tuple=2			,						lowerCamelCase__	:    List[str]=0.1			,						**lowerCamelCase__	:    List[str]			,						) ->			Dict:
         """simple docstring"""





         if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
                     A_							       =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
               elif isinstance(lowerCamelCase__			,						lowerCamelCase__       ):
                     A_							       =		backbone_config.get('''model_type'''       )
                     A_							       =		CONFIG_MAPPING[backbone_model_type]
                     A_							       =		config_class.from_dict(lowerCamelCase__       )
               # set timm attributes to None
               A_       ,A_       ,A_							       =		None, None, None

         A_							       =		use_timm_backbone
         A_							       =		backbone_config
         A_							       =		num_channels
         A_							       =		num_queries
         A_							       =		d_model
         A_							       =		encoder_ffn_dim
         A_							       =		encoder_layers
         A_							       =		encoder_attention_heads
         A_							       =		decoder_ffn_dim
         A_							       =		decoder_layers
         A_							       =		decoder_attention_heads
         A_							       =		dropout
         A_							       =		attention_dropout
         A_							       =		activation_dropout
         A_							       =		activation_function
         A_							       =		init_std
         A_							       =		init_xavier_std
         A_							       =		encoder_layerdrop
         A_							       =		decoder_layerdrop
         A_							       =		encoder_layers
         A_							       =		auxiliary_loss
         A_							       =		position_embedding_type
         A_							       =		backbone
         A_							       =		use_pretrained_backbone
         A_							       =		dilation
         # Hungarian matcher
         A_							       =		class_cost
         A_							       =		bbox_cost
         A_							       =		giou_cost
         # Loss coefficients
         A_							       =		mask_loss_coefficient
         A_							       =		dice_loss_coefficient
         A_							       =		bbox_loss_coefficient
         A_							       =		giou_loss_coefficient
         A_							       =		eos_coefficient
         super().__init__(is_encoder_decoder=lowerCamelCase__			,						**lowerCamelCase__       )

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         return self.encoder_attention_heads





   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			int:
         """simple docstring"""





         return self.d_model



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Tuple  					=    version.parse('1.11'			)

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			float:
         """simple docstring"""





         return 1e-5





   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         return 1_2





