





"""simple docstring"""


import copy
import os

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



class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							Union[str, Any]						)		->					List[str]:
        _UpperCamelCase   :						Optional[Any]    =							""
        _UpperCamelCase   :						Union[str, Any]    =							""
        _UpperCamelCase   :						Dict    =							[]
        _UpperCamelCase   :						Optional[Any]    =							0
        _UpperCamelCase   :						Dict    =							256
        _UpperCamelCase   :						Union[str, Any]    =							0
        _UpperCamelCase   :						Dict    =							0
        _UpperCamelCase   :						Optional[Any]    =							0
        _UpperCamelCase   :						Union[str, Any]    =							0

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					Union[str, Any]:
        plt.hist(self.img.ravel()       , 256       , [0, 256]						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Tuple:
        cva.imshow("Output-Image"       , self.img						)
        cva.imshow("Input-Image"       , self.original_image						)
        cva.waitKey(5000						)
        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()







"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__				:Dict						  =       "AutoImageProcessor"
  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "AutoTokenizer"

  def __init__(		self :							Dict       , __a :							Tuple       , __a :							str						)		->					Optional[Any]:
        super().__init__(__a       , __a						)
        _UpperCamelCase   :						Tuple    =							self.image_processor

  def __call__(		self :							Tuple       , __a :							Tuple=None       , __a :							str=None       , __a :							Any=None       , **__a :							Optional[Any]						)		->					Union[str, Any]:

        if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none."						)

        if text is not None:
              _UpperCamelCase   :						List[str]    =							self.tokenizer(__a       , return_tensors=__a       , **__a						)

        if images is not None:
              _UpperCamelCase   :						str    =							self.image_processor(__a       , return_tensors=__a       , **__a						)

        if text is not None and images is not None:
              _UpperCamelCase   :						Optional[int]    =							image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a						)       , tensor_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , *__a :							Optional[int]       , **__a :							Optional[int]						)		->					List[Any]:
        return self.tokenizer.batch_decode(*__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							Any       , **__a :							List[Any]						)		->					Tuple:
        return self.tokenizer.decode(*__a       , **__a						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        return ["input_ids", "attention_mask", "pixel_values"]
