





"""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


a		:				Tuple  			=    logging.get_logger(__name__)


if is_vision_available():
		import PIL


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      int         =["""pixel_values"""]






							def __init__(     self   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = PILImageResampling.BICUBIC   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = 1 / 255   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = True   ,      **lowerCAmelCase__   ,      )			->				None:
								super().__init__(**lowerCAmelCase__   )
								a   :	List[Any]			=       size if size is not None else {"shortest_edge": 224}
								a   :	Optional[Any]			=       get_size_dict(lowerCAmelCase__   ,      default_to_square=lowerCAmelCase__   )
								a   :	Any			=       crop_size if crop_size is not None else {"height": 224, "width": 224}
								a   :	str			=       get_size_dict(lowerCAmelCase__   ,      default_to_square=lowerCAmelCase__   ,      param_name="crop_size"   )

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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = PILImageResampling.BICUBIC   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								a   :	List[str]			=       get_size_dict(lowerCAmelCase__   ,      default_to_square=lowerCAmelCase__   )
								if "shortest_edge" not in size:
									raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
								a   :	Optional[Any]			=       get_resize_output_image_size(lowerCAmelCase__   ,      size=size["shortest_edge"]   ,      default_to_square=lowerCAmelCase__   )
								return resize(lowerCAmelCase__   ,      size=lowerCAmelCase__   ,      resample=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								a   :	Tuple			=       get_size_dict(lowerCAmelCase__   )
								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(lowerCAmelCase__   ,      size=(size["height"], size["width"])   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Any:
								return rescale(lowerCAmelCase__   ,      scale=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								return normalize(lowerCAmelCase__   ,      mean=lowerCAmelCase__   ,      std=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = ChannelDimension.FIRST   ,      **lowerCAmelCase__   ,      )			->				PIL.Image.Image:
								a   :	int			=       do_resize if do_resize is not None else self.do_resize
								a   :	Union[str, Any]			=       size if size is not None else self.size
								a   :	int			=       get_size_dict(lowerCAmelCase__   ,      param_name="size"   ,      default_to_square=lowerCAmelCase__   )
								a   :	Union[str, Any]			=       resample if resample is not None else self.resample
								a   :	Optional[int]			=       do_center_crop if do_center_crop is not None else self.do_center_crop
								a   :	str			=       crop_size if crop_size is not None else self.crop_size
								a   :	Optional[int]			=       get_size_dict(lowerCAmelCase__   ,      param_name="crop_size"   ,      default_to_square=lowerCAmelCase__   )
								a   :	Tuple			=       do_rescale if do_rescale is not None else self.do_rescale
								a   :	List[Any]			=       rescale_factor if rescale_factor is not None else self.rescale_factor
								a   :	str			=       do_normalize if do_normalize is not None else self.do_normalize
								a   :	Dict			=       image_mean if image_mean is not None else self.image_mean
								a   :	Optional[Any]			=       image_std if image_std is not None else self.image_std
								a   :	str			=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								a   :	str			=       make_list_of_images(lowerCAmelCase__   )

								if not valid_images(lowerCAmelCase__   ):
									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:
									a   :	Any			=       [convert_to_rgb(lowerCAmelCase__   ) for image in images]

								# All transformations expect numpy arrays.
								a   :	int			=       [to_numpy_array(lowerCAmelCase__   ) for image in images]

								if do_resize:
									a   :	Dict			=       [self.resize(image=lowerCAmelCase__   ,      size=lowerCAmelCase__   ,      resample=lowerCAmelCase__   ) for image in images]

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

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

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

								a   :	List[str]			=       [to_channel_dimension_format(lowerCAmelCase__   ,      lowerCAmelCase__   ) for image in images]

								a   :	int			=       {"pixel_values": images}
								return BatchFeature(data=lowerCAmelCase__   ,      tensor_type=lowerCAmelCase__   )









"""simple docstring"""




from random import randint
from tempfile import TemporaryFile

import numpy as np






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->Dict:






	'''simple docstring'''






	a   :	List[str]			=       0
	if start < end:
		a   :	Tuple			=       randint(_lowercase					, _lowercase      )
		a   :	List[str]			=       a[end]
		a   :	str			=       a[pivot]
		a   :	Optional[int]			=       temp

		a, a   :	Dict			=       _in_place_partition(_lowercase					, _lowercase					, _lowercase      )
		count += _in_place_quick_sort(_lowercase					, _lowercase					, p - 1      )
		count += _in_place_quick_sort(_lowercase					, p + 1					, _lowercase      )
	return count






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					str					, _lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       0
	a   :	List[Any]			=       randint(_lowercase					, _lowercase      )
	a   :	int			=       a[end]
	a   :	List[str]			=       a[pivot]
	a   :	Tuple			=       temp
	a   :	Union[str, Any]			=       start - 1
	for index in range(_lowercase					, _lowercase      ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			a   :	List[str]			=       new_pivot_index + 1
			a   :	Optional[int]			=       a[new_pivot_index]
			a   :	Union[str, Any]			=       a[index]
			a   :	List[Any]			=       temp

	a   :	Tuple			=       a[new_pivot_index + 1]
	a   :	str			=       a[end]
	a   :	Dict			=       temp
	return new_pivot_index + 1, count


a		:				int  			=    TemporaryFile()
a		:				Tuple  			=    100  # 1000 elements are to be sorted


a			,				a		:				int  			=    0, 1  # mean and standard deviation
a		:				List[Any]  			=    np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a		:				int  			=    np.load(outfile)
a		:				Tuple  			=    len(M) - 1
a		:				Union[str, Any]  			=    _in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)


