import copy
import os

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



class     _A   :






  def __init__(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      ''''''
       UpperCamelCase__										=      ''''''
       UpperCamelCase__										=      []
       UpperCamelCase__										=      0
       UpperCamelCase__										=      256
       UpperCamelCase__										=      0
       UpperCamelCase__										=      0
       UpperCamelCase__										=      0
       UpperCamelCase__										=      0






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





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






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       plt.hist(self.img.ravel()   ,				256   ,				[0, 256]	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       cva.imshow('''Output-Image'''   ,				self.img	)
       cva.imshow('''Input-Image'''   ,				self.original_image	)
       cva.waitKey(5000	)
       cva.destroyAllWindows()


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




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__magic_name__								=logging.get_logger(__name__)



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





       warnings.warn(
           '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
           ''' use BeitImageProcessor instead.'''   ,				SCREAMING_SNAKE_CASE_   ,				)
       super().__init__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)



