

'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							Any    		=make_list_of_images(UpperCAmelCase__   )

										if not valid_images(UpperCAmelCase__   ):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )






"""simple docstring"""






from copy import deepcopy





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if arr is None and size is not None:
                  lowercase								=      size
                  lowercase								=      [0] * size
            elif arr is not None:
                  self.init(__lowerCAmelCase		)
            else:
                  raise ValueError("""Either arr or size must be specified"""		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      deepcopy(__lowerCAmelCase		)
            for i in range(1						,			self.size		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        self.tree[j] += self.tree[i]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.tree[:]
            for i in range(self.size - 1						,			0						,			-1		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        arr[j] -= arr[i]
            return arr

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index + (index & (-index))

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index - (index & (-index))

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if index == 0:
                  self.tree[0] += value
                  return
            while index < self.size:
                  self.tree[index] += value
                  lowercase								=      self.next_(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            self.add(__lowerCAmelCase						,			value - self.get(__lowerCAmelCase		)		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if right == 0:
                  return 0
            lowercase								=      self.tree[0]
            right -= 1  # make right inclusive
            while right > 0:
                  result += self.tree[right]
                  lowercase								=      self.prev(__lowerCAmelCase		)
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.prefix(__lowerCAmelCase		) - self.prefix(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.query(__lowerCAmelCase						,			index + 1		)







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            value -= self.tree[0]
            if value < 0:
                  return -1

            lowercase								=      1  # Largest power of 2 <= size
            while j * 2 < self.size:
                  j *= 2

            lowercase								=      0

            while j > 0:
                  if i + j < self.size and self.tree[i + j] <= value:
                        value -= self.tree[i + j]
                        i += j
                  j //= 2
            return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()



