


import copy

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


__A :  Union[str, Any]    							=				logging.get_logger(__name__)






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase       			= 'upernet'

    def __init__(       self   :  Any   ,						lowerCamelCase   :  Dict=None   ,						lowerCamelCase   :  Union[str, Any]=5_12   ,						lowerCamelCase   :  Optional[Any]=0.02   ,						lowerCamelCase   :  List[str]=[1, 2, 3, 6]   ,						lowerCamelCase   :  Union[str, Any]=True   ,						lowerCamelCase   :  Tuple=0.4   ,						lowerCamelCase   :  Tuple=3_84   ,						lowerCamelCase   :  Optional[Any]=2_56   ,						lowerCamelCase   :  Union[str, Any]=1   ,						lowerCamelCase   :  List[Any]=False   ,						lowerCamelCase   :  int=2_55   ,						**lowerCamelCase   :  List[str]   ,						)       ->  Any:
      super().__init__(**lowerCamelCase		)

      if backbone_config is None:
        logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
        lowerCAmelCase_      :				str      =       CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		)
      elif isinstance(lowerCamelCase   ,						lowerCamelCase		):
        lowerCAmelCase_      :				str      =       backbone_config.get("""model_type"""		)
        lowerCAmelCase_      :				List[Any]      =       CONFIG_MAPPING[backbone_model_type]
        lowerCAmelCase_      :				Optional[Any]      =       config_class.from_dict(lowerCamelCase		)

      lowerCAmelCase_      :				str      =       backbone_config
      lowerCAmelCase_      :				int      =       hidden_size
      lowerCAmelCase_      :				Optional[Any]      =       initializer_range
      lowerCAmelCase_      :				Optional[Any]      =       pool_scales
      lowerCAmelCase_      :				Optional[int]      =       use_auxiliary_head
      lowerCAmelCase_      :				int      =       auxiliary_loss_weight
      lowerCAmelCase_      :				Dict      =       auxiliary_in_channels
      lowerCAmelCase_      :				List[Any]      =       auxiliary_channels
      lowerCAmelCase_      :				int      =       auxiliary_num_convs
      lowerCAmelCase_      :				str      =       auxiliary_concat_input
      lowerCAmelCase_      :				Union[str, Any]      =       loss_ignore_index





    def __lowercase       (       self   :  Any		)       ->  Optional[int]:
      lowerCAmelCase_      :				int      =       copy.deepcopy(self.__dict__		)
      lowerCAmelCase_      :				Optional[int]      =       self.backbone_config.to_dict()
      lowerCAmelCase_      :				List[str]      =       self.__class__.model_type
      return output







'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       np.ndarray							,   A__ :       int							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       cva.getAffineTransform(A__							,   A__ )
  return cva.warpAffine(A__							,   A__							,   (rows, cols) )


if __name__ == "__main__":
  # read original image
  __A :  Dict    							=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __A :  List[Any]    							=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __A							,			__A :  Dict    							=				gray_img.shape

  # set different points to rotate image
  __A :  List[str]    							=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __A :  Tuple    							=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __A :  List[Any]    							=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __A :  Optional[Any]    							=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __A :  Optional[Any]    							=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __A :  Dict    							=				plt.figure(1)
  __A :  Optional[Any]    							=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
  plt.show()



