




import copy
import os

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




class    __lowerCAmelCase       :






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






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






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






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         cva.imshow("Output-Image"       ,					self.img				)
         cva.imshow("Input-Image"       ,					self.original_image				)
         cva.waitKey(50_00				)
         cva.destroyAllWindows()


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









from typing import TYPE_CHECKING

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


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "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

     _UpperCAmelCase	:						Optional[Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



