




"""simple docstring"""



import copy
import os

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




class      UpperCAmelCase_  :




    def __init__(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     A__	=    ""
     A__	=    ""
     A__	=    []
     A__	=    0
     A__	=    256
     A__	=    0
     A__	=    0
     A__	=    0
     A__	=    0




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Dict				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    cva.imread(snake_case_     ,			0				)
     A__	=    copy.deepcopy(self.img				)
     A__,				A__,				A__	=    plt.hist(self.img.ravel()     ,			256     ,			[0, 256]     ,			label="x"				)
     A__	=    np.sum(snake_case_				)
     for i in range(len(snake_case_				)				):
      A__	=    x[i] / self.k
      self.sk += prk
      A__	=    (self.L - 1) * self.sk
      if self.rem != 0:
       A__	=    int(last % last				)
      A__	=    int(last + 1 if self.rem >= 0.5 else last				)
      self.last_list.append(snake_case_				)
      A__	=    int(np.ma.count(self.img				) / self.img[1].size				)
      A__	=    self.img[1].size
     for i in range(self.number_of_cols				):
      for j in range(self.number_of_rows				):
       A__	=    self.img[j][i]
       if num != self.last_list[num]:
        A__	=    self.last_list[num]
     cva.imwrite("output_data/output.jpg"     ,			self.img				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




     '''simple docstring'''

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




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




     '''simple docstring'''

     cva.imshow("Output-Image"     ,			self.img				)
     cva.imshow("Input-Image"     ,			self.original_image				)
     cva.waitKey(5_000				)
     cva.destroyAllWindows()


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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

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





