import copy
import os

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






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



    def     UpperCamelCase_					(	self )							->       Tuple:
     cva.imshow('''Output-Image'''			,			self.img )
     cva.imshow('''Input-Image'''			,			self.original_image )
     cva.waitKey(5000 )
     cva.destroyAllWindows()


if __name__ == "__main__":
      lowercase_  : Union[str, Any]								=					os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
      lowercase_  : Dict								=					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"]
