

"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase  :      int							      =		logging.get_logger(__name__)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				int   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Dict[str, int]] = None   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				bool = True   ,lowercase_			:				bool = True   ,lowercase_			:				Union[int, float] = 1 / 2_5_5   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,**lowercase_			:				Dict   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						str  				=	get_size_dict(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						List[str]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_   ,param_name='''crop_size'''		)

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







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BILINEAR   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						lowerCAmelCase__	:						Optional[int]  				=	get_size_dict(lowercase_		)

						if "shortest_edge" in size:
							lowerCAmelCase__	:						Dict  				=	get_resize_output_image_size(lowercase_   ,size=size['''shortest_edge''']   ,default_to_square=lowercase_		)
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
						elif "height" in size and "width" in size:
							lowerCAmelCase__	:						Dict  				=	(size['''height'''], size['''width'''])
						else:
							raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'		)
						return resize(lowercase_   ,size=lowercase_   ,resample=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Any  				=	get_size_dict(lowercase_		)
						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(lowercase_   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				np.ndarray   ,lowercase_			:				float   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]		):
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[int]   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				ImageInput   ,lowercase_			:				Optional[bool] = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				int = None   ,lowercase_			:				Optional[bool] = None   ,lowercase_			:				Optional[float] = None   ,lowercase_			:				Optional[bool] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				Union[str, ChannelDimension] = ChannelDimension.FIRST   ,**lowercase_			:				Union[str, Any]   ,):
						lowerCAmelCase__	:						int  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						Any  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Tuple  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Optional[int]  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						int  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						List[Any]  				=	image_std if image_std is not None else self.image_std

						lowerCAmelCase__	:						str  				=	size if size is not None else self.size
						lowerCAmelCase__	:						str  				=	get_size_dict(lowercase_		)

						if not is_batched(lowercase_		):
							lowerCAmelCase__	:						Dict  				=	[images]

						if not valid_images(lowercase_		):
							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.'''		)

						# All transformations expect numpy arrays.
						lowerCAmelCase__	:						List[Any]  				=	[to_numpy_array(lowercase_		) for image in images]

						if do_resize:
							lowerCAmelCase__	:						Union[str, Any]  				=	[self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		) for image in images]

						if do_center_crop:
							lowerCAmelCase__	:						int  				=	[self.center_crop(image=lowercase_   ,size=lowercase_		) for image in images]

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

						if do_normalize:
							lowerCAmelCase__	:						Any  				=	[self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		) for image in images]

						lowerCAmelCase__	:						int  				=	[to_channel_dimension_format(lowercase_   ,lowercase_		) for image in images]

						lowerCAmelCase__	:						Optional[Any]  				=	{'''pixel_values''': images}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)







"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(A_						,     A_ )
	# must be in larger
	else:
		return quick_select(A_						,     index - (m + count) )




