



'''simple docstring'''





import copy
import os

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







class     UpperCAmelCase_       :
    """simple docstring"""







    def __init__(    self					:     Dict						):
          snake_case__  :    Optional[int]    						=      """"""
          snake_case__  :    List[Any]    						=      """"""
          snake_case__  :    List[str]    						=      []
          snake_case__  :    str    						=      0
          snake_case__  :    Union[str, Any]    						=      256
          snake_case__  :    Union[str, Any]    						=      0
          snake_case__  :    Any    						=      0
          snake_case__  :    List[Any]    						=      0
          snake_case__  :    Optional[Any]    						=      0




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    Dict    						=      cva.imread(snake_case_ ,     0						)
          snake_case__  :    str    						=      copy.deepcopy(self.img						)
          snake_case__						,				snake_case__						,				snake_case__  :    List[str]    						=      plt.hist(self.img.ravel() ,     256 ,     [0, 256] ,     label="""x"""						)
          snake_case__  :    str    						=      np.sum(snake_case_						)
          for i in range(len(snake_case_						)						):
                snake_case__  :    str    						=      x[i] / self.k
                self.sk += prk
                snake_case__  :    int    						=      (self.L - 1) * self.sk
                if self.rem != 0:
                      snake_case__  :    List[Any]    						=      int(last % last						)
                snake_case__  :    Tuple    						=      int(last + 1 if self.rem >= 0.5 else last						)
                self.last_list.append(snake_case_						)
                snake_case__  :    Tuple    						=      int(np.ma.count(self.img						) / self.img[1].size						)
                snake_case__  :    int    						=      self.img[1].size
          for i in range(self.number_of_cols						):
                for j in range(self.number_of_rows						):
                      snake_case__  :    Tuple    						=      self.img[j][i]
                      if num != self.last_list[num]:
                            snake_case__  :    str    						=      self.last_list[num]
          cva.imwrite("""output_data/output.jpg""" ,     self.img						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          plt.hist(self.img.ravel() ,     256 ,     [0, 256]						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          cva.imshow("""Output-Image""" ,     self.img						)
          cva.imshow("""Input-Image""" ,     self.original_image						)
          cva.waitKey(5_000						)
          cva.destroyAllWindows()


if __name__ == "__main__":
       __a          =		os.path.join(os.path.basename(__file__), "image_data/input.jpg")
       __a          =		ConstantStretch()
       stretcher.stretch(file_path)
       stretcher.plot_histogram()
       stretcher.show_image()





'''simple docstring'''





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


__a          =		logging.get_logger(__name__)

__a          =		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"glpn"




    def __init__(    self					:     Optional[Any] ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Dict=4 ,     snake_case_					:     List[Any]=[2, 2, 2, 2] ,     snake_case_					:     int=[8, 4, 2, 1] ,     snake_case_					:     List[str]=[32, 64, 160, 256] ,     snake_case_					:     Tuple=[7, 3, 3, 3] ,     snake_case_					:     List[Any]=[4, 2, 2, 2] ,     snake_case_					:     Tuple=[1, 2, 5, 8] ,     snake_case_					:     List[str]=[4, 4, 4, 4] ,     snake_case_					:     Optional[int]="gelu" ,     snake_case_					:     Dict=0.0 ,     snake_case_					:     Union[str, Any]=0.0 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Any=1E-6 ,     snake_case_					:     Dict=64 ,     snake_case_					:     Tuple=10 ,     snake_case_					:     List[Any]=-1 ,     **snake_case_					:     Optional[Any] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    Optional[Any]    						=      num_channels
          snake_case__  :    Dict    						=      num_encoder_blocks
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Union[str, Any]    						=      sr_ratios
          snake_case__  :    Tuple    						=      hidden_sizes
          snake_case__  :    Optional[Any]    						=      patch_sizes
          snake_case__  :    int    						=      strides
          snake_case__  :    List[Any]    						=      mlp_ratios
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      hidden_dropout_prob
          snake_case__  :    Optional[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      initializer_range
          snake_case__  :    List[str]    						=      drop_path_rate
          snake_case__  :    int    						=      layer_norm_eps
          snake_case__  :    Tuple    						=      decoder_hidden_size
          snake_case__  :    List[Any]    						=      max_depth
          snake_case__  :    Dict    						=      head_in_index
