



import copy
import os

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



class       UpperCAmelCase__:

   '''simple docstring'''




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




         """simple docstring"""

         lowercase__     =  ''
         lowercase__     =  ''
         lowercase__     =  []
         lowercase__     =  0
         lowercase__     =  2_56
         lowercase__     =  0
         lowercase__     =  0
         lowercase__     =  0
         lowercase__     =  0


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			Dict)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  cva.imread(lowerCAmelCase			,				0)
         lowercase__     =  copy.deepcopy(self.img)
         lowercase__,	lowercase__,	lowercase__     =  plt.hist(self.img.ravel()			,				2_56			,				[0, 2_56]			,				label='x')
         lowercase__     =  np.sum(lowerCAmelCase)
         for i in range(len(lowerCAmelCase)):
               lowercase__     =  x[i] / self.k
               self.sk += prk
               lowercase__     =  (self.L - 1) * self.sk
               if self.rem != 0:
                     lowercase__     =  int(last % last)
               lowercase__     =  int(last + 1 if self.rem >= 0.5 else last)
               self.last_list.append(lowerCAmelCase)
               lowercase__     =  int(np.ma.count(self.img) / self.img[1].size)
               lowercase__     =  self.img[1].size
         for i in range(self.number_of_cols):
               for j in range(self.number_of_rows):
                     lowercase__     =  self.img[j][i]
                     if num != self.last_list[num]:
                           lowercase__     =  self.last_list[num]
         cva.imwrite('output_data/output.jpg'			,				self.img)


   def 			UpperCAmelCase		(						self  :			List[str])	->				str:




         """simple docstring"""

         plt.hist(self.img.ravel()			,				2_56			,				[0, 2_56])



   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         cva.imshow('Output-Image'			,				self.img)
         cva.imshow('Input-Image'			,				self.original_image)
         cva.waitKey(50_00)
         cva.destroyAllWindows()


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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Any   	=			{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Optional[int]   	=			["ReformerTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Union[str, Any]   	=			["ReformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			[
           "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ReformerAttention",
           "ReformerForMaskedLM",
           "ReformerForQuestionAnswering",
           "ReformerForSequenceClassification",
           "ReformerLayer",
           "ReformerModel",
           "ReformerModelWithLMHead",
           "ReformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer import ReformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer_fast import ReformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_reformer import (
                  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ReformerAttention,
                  ReformerForMaskedLM,
                  ReformerForQuestionAnswering,
                  ReformerForSequenceClassification,
                  ReformerLayer,
                  ReformerModel,
                  ReformerModelWithLMHead,
                  ReformerPreTrainedModel,
              )

else:
       import sys

       a__      :				Tuple   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
