
'''simple docstring'''
import copy
import os

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






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    Any     			=						""
     snake_case_ :    Optional[int]     			=						""
     snake_case_ :    List[str]     			=						[]
     snake_case_ :    Any     			=						0
     snake_case_ :    Tuple     			=						2_56
     snake_case_ :    Optional[Any]     			=						0
     snake_case_ :    List[str]     			=						0
     snake_case_ :    int     			=						0
     snake_case_ :    str     			=						0





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





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





 def        snake_case__		(    self):
     cva.imshow("Output-Image" ,       self.img)
     cva.imshow("Input-Image" ,       self.original_image)
     cva.waitKey(50_00)
     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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




