


import copy
import os

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



class 	__lowerCamelCase			:







       def __init__(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			''
            __UpperCamelCase =			''
            __UpperCamelCase =			[]
            __UpperCamelCase =			0
            __UpperCamelCase =			256
            __UpperCamelCase =			0
            __UpperCamelCase =			0
            __UpperCamelCase =			0
            __UpperCamelCase =			0







       def 		snake_case_	(			self: List[Any],A_: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			cva.imread(A_,0					)
            __UpperCamelCase =			copy.deepcopy(self.img					)
            __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			plt.hist(self.img.ravel(),256,[0, 256],label='x'					)
            __UpperCamelCase =			np.sum(A_					)
            for i in range(len(A_					)					):
                 __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(A_					)
                 __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 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




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


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






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case   							=						{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


