






"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase__          = logging.get_logger(__name__)


if is_vision_available():
					import PIL


class 		__lowerCamelCase				( _a			):





		'''simple docstring'''






		a_	:				Dict            =							["""pixel_values"""]





		def __init__(   self							:	List[str]						,			a_							:	bool = True						,			a_							:	Dict[str, int] = None						,			a_							:	PILImageResampling = PILImageResampling.BICUBIC						,			a_							:	bool = True						,			a_							:	Dict[str, int] = None						,			a_							:	bool = True						,			a_							:	Union[int, float] = 1 / 2_55						,			a_							:	bool = True						,			a_							:	Optional[Union[float, List[float]]] = None						,			a_							:	Optional[Union[float, List[float]]] = None						,			a_							:	bool = True						,			**a_							:	Optional[Any]						,			):
				super().__init__(**__UpperCAmelCase	)
				lowerCAmelCase_   :							Dict									=			size if size is not None else {"shortest_edge": 2_24}
				lowerCAmelCase_   :							Dict									=			get_size_dict(__UpperCAmelCase						,			default_to_square=__UpperCAmelCase	)
				lowerCAmelCase_   :							Union[str, Any]									=			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
				lowerCAmelCase_   :							Optional[int]									=			get_size_dict(__UpperCAmelCase						,			default_to_square=__UpperCAmelCase						,			param_name="crop_size"	)

				lowerCAmelCase_   :							int									=			do_resize
				lowerCAmelCase_   :							List[Any]									=			size
				lowerCAmelCase_   :							Dict									=			resample
				lowerCAmelCase_   :							List[str]									=			do_center_crop
				lowerCAmelCase_   :							Any									=			crop_size
				lowerCAmelCase_   :							Dict									=			do_rescale
				lowerCAmelCase_   :							Optional[int]									=			rescale_factor
				lowerCAmelCase_   :							Tuple									=			do_normalize
				lowerCAmelCase_   :							int									=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				lowerCAmelCase_   :							Dict									=			image_std if image_std is not None else OPENAI_CLIP_STD
				lowerCAmelCase_   :							Union[str, Any]									=			do_convert_rgb





		def    lowerCamelCase       (   self							:	List[Any]						,			a_							:	np.ndarray						,			a_							:	Dict[str, int]						,			a_							:	PILImageResampling = PILImageResampling.BICUBIC						,			a_							:	Optional[Union[str, ChannelDimension]] = None						,			**a_							:	List[Any]						,			):
				lowerCAmelCase_   :							Optional[Any]									=			get_size_dict(__UpperCAmelCase						,			default_to_square=__UpperCAmelCase	)
				if "shortest_edge" not in size:
						raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
				lowerCAmelCase_   :							Any									=			get_resize_output_image_size(__UpperCAmelCase						,			size=size["shortest_edge"]						,			default_to_square=__UpperCAmelCase	)
				return resize(__UpperCAmelCase						,			size=__UpperCAmelCase						,			resample=__UpperCAmelCase						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase	)





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	np.ndarray						,			a_							:	Dict[str, int]						,			a_							:	Optional[Union[str, ChannelDimension]] = None						,			**a_							:	Union[str, Any]						,			):
				lowerCAmelCase_   :							Tuple									=			get_size_dict(__UpperCAmelCase	)
				if "height" not in size or "width" not in size:
						raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
				return center_crop(__UpperCAmelCase						,			size=(size["height"], size["width"])						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase	)





		def    lowerCamelCase       (   self							:	int						,			a_							:	np.ndarray						,			a_							:	Union[int, float]						,			a_							:	Optional[Union[str, ChannelDimension]] = None						,			**a_							:	List[Any]						,			):
				return rescale(__UpperCAmelCase						,			scale=__UpperCAmelCase						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase	)





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	np.ndarray						,			a_							:	Union[float, List[float]]						,			a_							:	Union[float, List[float]]						,			a_							:	Optional[Union[str, ChannelDimension]] = None						,			**a_							:	List[str]						,			):
				return normalize(__UpperCAmelCase						,			mean=__UpperCAmelCase						,			std=__UpperCAmelCase						,			data_format=__UpperCAmelCase						,			**__UpperCAmelCase	)





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	ImageInput						,			a_							:	bool = None						,			a_							:	Dict[str, int] = None						,			a_							:	PILImageResampling = None						,			a_							:	bool = None						,			a_							:	int = None						,			a_							:	bool = None						,			a_							:	float = None						,			a_							:	bool = None						,			a_							:	Optional[Union[float, List[float]]] = None						,			a_							:	Optional[Union[float, List[float]]] = None						,			a_							:	bool = None						,			a_							:	Optional[Union[str, TensorType]] = None						,			a_							:	Optional[ChannelDimension] = ChannelDimension.FIRST						,			**a_							:	Union[str, Any]						,			):
				lowerCAmelCase_   :							Optional[int]									=			do_resize if do_resize is not None else self.do_resize
				lowerCAmelCase_   :							Union[str, Any]									=			size if size is not None else self.size
				lowerCAmelCase_   :							Any									=			get_size_dict(__UpperCAmelCase						,			param_name="size"						,			default_to_square=__UpperCAmelCase	)
				lowerCAmelCase_   :							Dict									=			resample if resample is not None else self.resample
				lowerCAmelCase_   :							Optional[Any]									=			do_center_crop if do_center_crop is not None else self.do_center_crop
				lowerCAmelCase_   :							Any									=			crop_size if crop_size is not None else self.crop_size
				lowerCAmelCase_   :							int									=			get_size_dict(__UpperCAmelCase						,			param_name="crop_size"						,			default_to_square=__UpperCAmelCase	)
				lowerCAmelCase_   :							Optional[int]									=			do_rescale if do_rescale is not None else self.do_rescale
				lowerCAmelCase_   :							Optional[Any]									=			rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCAmelCase_   :							Optional[int]									=			do_normalize if do_normalize is not None else self.do_normalize
				lowerCAmelCase_   :							Optional[Any]									=			image_mean if image_mean is not None else self.image_mean
				lowerCAmelCase_   :							Union[str, Any]									=			image_std if image_std is not None else self.image_std
				lowerCAmelCase_   :							Tuple									=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				lowerCAmelCase_   :							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_resize and size is None:
						raise ValueError("Size must be specified if do_resize is True."	)

				if do_center_crop and crop_size is None:
						raise ValueError("Crop size must be specified if do_center_crop is True."	)

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

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError("Image mean and std must be specified if do_normalize is True."	)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						lowerCAmelCase_   :							Union[str, Any]									=			[convert_to_rgb(__UpperCAmelCase	) for image in images]

				# All transformations expect numpy arrays.
				lowerCAmelCase_   :							List[str]									=			[to_numpy_array(__UpperCAmelCase	) for image in images]

				if do_resize:
						lowerCAmelCase_   :							int									=			[self.resize(image=__UpperCAmelCase						,			size=__UpperCAmelCase						,			resample=__UpperCAmelCase	) for image in images]

				if do_center_crop:
						lowerCAmelCase_   :							List[str]									=			[self.center_crop(image=__UpperCAmelCase						,			size=__UpperCAmelCase	) for image in images]

				if do_rescale:
						lowerCAmelCase_   :							List[str]									=			[self.rescale(image=__UpperCAmelCase						,			scale=__UpperCAmelCase	) for image in images]

				if do_normalize:
						lowerCAmelCase_   :							List[str]									=			[self.normalize(image=__UpperCAmelCase						,			mean=__UpperCAmelCase						,			std=__UpperCAmelCase	) for image in images]

				lowerCAmelCase_   :							int									=			[to_channel_dimension_format(__UpperCAmelCase						,			__UpperCAmelCase	) for image in images]

				lowerCAmelCase_   :							List[Any]									=			{"pixel_values": images}
				return BatchFeature(data=__UpperCAmelCase						,			tensor_type=__UpperCAmelCase	)




"""simple docstring"""







class 						_A  :







    """simple docstring"""




    def __init__(					self				:    int				, __UpperCAmelCase				:    int):
      a						:		Tuple      							=			size
      a						:		Dict      							=			[0] * size
      a						:		Optional[int]      							=			[0] * size






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    int):
      return index | (index + 1)






    @staticmethod
    def 	__snake_case							(					__UpperCAmelCase				:    int):
      return (index & (index + 1)) - 1






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int):
      a						:		Union[str, Any]      							=			value
      while index < self.size:
        a						:		Dict      							=			self.get_prev(__UpperCAmelCase) + 1
        if current_left_border == index:
          a						:		Optional[int]      							=			value
        else:
          a						:		Any      							=			max(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
        a						:		Optional[int]      							=			self.get_next(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int				, __UpperCAmelCase				:    int):
      right -= 1  # Because of right is exclusive
      a						:		List[str]      							=			0
      while left <= right:
        a						:		Dict      							=			self.get_prev(__UpperCAmelCase)
        if left <= current_left:
          a						:		Optional[int]      							=			max(__UpperCAmelCase				, self.tree[right])
          a						:		Optional[Any]      							=			current_left
        else:
          a						:		List[str]      							=			max(__UpperCAmelCase				, self.arr[right])
          right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()






