'''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 )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       ''
         SCREAMING_SNAKE_CASE					:			int    	=       ''
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       []
         SCREAMING_SNAKE_CASE					:			Dict    	=       0
         SCREAMING_SNAKE_CASE					:			Dict    	=       256
         SCREAMING_SNAKE_CASE					:			Any    	=       0
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       0
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       0






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






    def       _UpperCamelCase    (       self )							-> List[Any]:
         plt.hist(self.img.ravel()    ,							256    ,							[0, 256] )






    def       _UpperCamelCase    (       self )							-> Any:
         cva.imshow('Output-Image'    ,							self.img )
         cva.imshow('Input-Image'    ,							self.original_image )
         cva.waitKey(5_000 )
         cva.destroyAllWindows()


if __name__ == "__main__":
 _lowerCAmelCase  :Tuple  				=    os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
 _lowerCAmelCase  :Union[str, Any]  				=    ConstantStretch()
 stretcher.stretch(file_path)
 stretcher.plot_histogram()
 stretcher.show_image()




'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation






@require_torch
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         get_activation('gelu' )
         get_activation('gelu_10' )
         get_activation('gelu_fast' )
         get_activation('gelu_new' )
         get_activation('gelu_python' )
         get_activation('gelu_pytorch_tanh' )
         get_activation('linear' )
         get_activation('mish' )
         get_activation('quick_gelu' )
         get_activation('relu' )
         get_activation('sigmoid' )
         get_activation('silu' )
         get_activation('swish' )
         get_activation('tanh' )
         with self.assertRaises(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a



