






import copy
import os

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




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:List[str]          =						""""""
            __snake_case			:Optional[int]          =						""""""
            __snake_case			:Union[str, Any]          =						[]
            __snake_case			:Any          =						0
            __snake_case			:Optional[Any]          =						2_56
            __snake_case			:str          =						0
            __snake_case			:Optional[int]          =						0
            __snake_case			:Optional[Any]          =						0
            __snake_case			:int          =						0



       def 					__lowercase						(							self			,						a__    ) ->      Any:






            '''simple docstring'''





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






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





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


if __name__ == "__main__":
     lowerCamelCase__							=     os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
     lowerCamelCase__							=     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


lowerCamelCase__							=     {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CanineForMultipleChoice""",
         """CanineForQuestionAnswering""",
         """CanineForSequenceClassification""",
         """CanineForTokenClassification""",
         """CanineLayer""",
         """CanineModel""",
         """CaninePreTrainedModel""",
         """load_tf_weights_in_canine""",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

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


