
'''simple docstring'''
import copy
from typing import Dict, List, Optional

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


__lowerCAmelCase												=	{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__lowerCAmelCase												=	logging.get_logger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"mask2former"
							lowerCAmelCase_ 						=						["swin"]
							lowerCAmelCase_ 						=						{"hidden_size": "hidden_dim"}

							def __init__(self  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 256  ,  UpperCAmelCase = 256  ,  UpperCAmelCase = 256  ,  UpperCAmelCase = 1024  ,  UpperCAmelCase = "relu"  ,  UpperCAmelCase = 6  ,  UpperCAmelCase = 10  ,  UpperCAmelCase = 8  ,  UpperCAmelCase = 0.0  ,  UpperCAmelCase = 2048  ,  UpperCAmelCase = False  ,  UpperCAmelCase = False  ,  UpperCAmelCase = 4  ,  UpperCAmelCase = 255  ,  UpperCAmelCase = 100  ,  UpperCAmelCase = 0.1  ,  UpperCAmelCase = 2.0  ,  UpperCAmelCase = 5.0  ,  UpperCAmelCase = 5.0  ,  UpperCAmelCase = 12544  ,  UpperCAmelCase = 3.0  ,  UpperCAmelCase = 0.75  ,  UpperCAmelCase = 0.02  ,  UpperCAmelCase = 1.0  ,  UpperCAmelCase = True  ,  UpperCAmelCase = [4, 8, 16, 32]  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       List[str]:
														if backbone_config is None:
																					logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""  )
																					_snake_case       =  CONFIG_MAPPING["""swin"""](
																					    image_size=224  ,  in_channels=3  ,  patch_size=4  ,  embed_dim=96  ,  depths=[2, 2, 18, 2]  ,  num_heads=[3, 6, 12, 24]  ,  window_size=7  ,  drop_path_rate=0.3  ,  use_absolute_embeddings=UpperCAmelCase  ,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  ,  )

														if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  backbone_config.pop("""model_type"""  )
																					_snake_case       =  CONFIG_MAPPING[backbone_model_type]
																					_snake_case       =  config_class.from_dict(UpperCAmelCase  )

														# verify that the backbone is supported
														if backbone_config.model_type not in self.backbones_supported:
																					logger.warning_once(
																					    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
																					    f"""Supported model types: {",".join(self.backbones_supported  )}"""  )

														_snake_case       =  backbone_config
														_snake_case       =  feature_size
														_snake_case       =  mask_feature_size
														_snake_case       =  hidden_dim
														_snake_case       =  encoder_feedforward_dim
														_snake_case       =  activation_function
														_snake_case       =  encoder_layers
														_snake_case       =  decoder_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  dropout
														_snake_case       =  dim_feedforward
														_snake_case       =  pre_norm
														_snake_case       =  enforce_input_projection
														_snake_case       =  common_stride
														_snake_case       =  ignore_value
														_snake_case       =  num_queries
														_snake_case       =  no_object_weight
														_snake_case       =  class_weight
														_snake_case       =  mask_weight
														_snake_case       =  dice_weight
														_snake_case       =  train_num_points
														_snake_case       =  oversample_ratio
														_snake_case       =  importance_sample_ratio
														_snake_case       =  init_std
														_snake_case       =  init_xavier_std
														_snake_case       =  use_auxiliary_loss
														_snake_case       =  feature_strides
														_snake_case       =  output_auxiliary_logits
														_snake_case       =  decoder_layers

														super().__init__(**UpperCAmelCase  )

							@classmethod
							def 	lowercase							(cls  ,  UpperCAmelCase  ,  **UpperCAmelCase  )					->       Any:
														return cls(
														    backbone_config=UpperCAmelCase  ,  **UpperCAmelCase  ,  )





							def 	lowercase							(self  )					->       Dict[str, any]:
														_snake_case       =  copy.deepcopy(self.__dict__  )
														_snake_case       =  self.backbone_config.to_dict()
														_snake_case       =  self.__class__.model_type
														return output

'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														if dst_width < 0 or dst_height < 0:
																					raise ValueError("""Destination width/height should be > 0"""  )

														_snake_case       =  img
														_snake_case       =  img.shape[1]
														_snake_case       =  img.shape[0]
														_snake_case       =  dst_width
														_snake_case       =  dst_height

														_snake_case       =  self.src_w / self.dst_w
														_snake_case       =  self.src_h / self.dst_h

														_snake_case       =  _snake_case       =  (
														    np.ones((self.dst_h, self.dst_w, 3)  ,  np.uinta  ) * 255
														)

							def 	lowercase							(self  )					->       List[Any]:
														for i in range(self.dst_h  ):
																					for j in range(self.dst_w  ):
																												_snake_case       =  self.img[self.get_y(UpperCAmelCase  )][self.get_x(UpperCAmelCase  )]

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														return int(self.ratio_x * x  )





							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														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()