


"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__lowerCamelCase      :int	    =		logging.get_logger(__name__)


class 			A__	(						__lowercase):


					"""simple docstring"""
					snake_case__	:					int													=['''pixel_values''']

					def __init__(	self:						str     ,		__a:						bool = True     ,		__a:						Dict[str, int] = None     ,		__a:						float = None     ,		__a:						PILImageResampling = PILImageResampling.BILINEAR     ,		__a:						bool = True     ,		__a:						Union[int, float] = 1 / 255     ,		__a:						bool = True     ,		__a:						Optional[Union[float, List[float]]] = None     ,		__a:						Optional[Union[float, List[float]]] = None     ,		**__a:						Optional[int]     ,		)->    None:
											super().__init__(**__a	)
											lowerCamelCase					:					Optional[Any]        =	size if size is not None else {"""shortest_edge""": 384}
											lowerCamelCase					:					List[str]        =	get_size_dict(__a     ,		default_to_square=__a	)

											lowerCamelCase					:					Tuple        =	do_resize
											lowerCamelCase					:					Dict        =	size
											# Default value set here for backwards compatibility where the value in config is None
											lowerCamelCase					:					List[Any]        =	crop_pct if crop_pct is not None else 224 / 256
											lowerCamelCase					:					Dict        =	resample
											lowerCamelCase					:					Any        =	do_rescale
											lowerCamelCase					:					str        =	rescale_factor
											lowerCamelCase					:					Optional[Any]        =	do_normalize
											lowerCamelCase					:					int        =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowerCamelCase					:					int        =	image_std if image_std is not None else IMAGENET_STANDARD_STD

					def 		a__						(	self:						Dict     ,		__a:						np.ndarray     ,		__a:						Dict[str, int]     ,		__a:						float     ,		__a:						PILImageResampling = PILImageResampling.BICUBIC     ,		__a:						Optional[Union[str, ChannelDimension]] = None     ,		**__a:						Optional[Any]     ,		)->    np.ndarray:
											lowerCamelCase					:					List[Any]        =	get_size_dict(__a     ,		default_to_square=__a	)
											if "shortest_edge" not in size:
																	raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
											lowerCamelCase					:					Optional[Any]        =	size["""shortest_edge"""]

											if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	lowerCamelCase					:					Union[str, Any]        =	int(shortest_edge / crop_pct	)
																	lowerCamelCase					:					str        =	get_resize_output_image_size(__a     ,		size=__a     ,		default_to_square=__a	)
																	lowerCamelCase					:					Optional[int]        =	resize(image=__a     ,		size=__a     ,		resample=__a     ,		data_format=__a     ,		**__a	)
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=__a     ,		size=(shortest_edge, shortest_edge)     ,		data_format=__a     ,		**__a	)
											else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    __a     ,		size=(shortest_edge, shortest_edge)     ,		resample=__a     ,		data_format=__a     ,		**__a	)

					def 		a__						(	self:						List[Any]     ,		__a:						np.ndarray     ,		__a:						Union[int, float]     ,		__a:						Optional[Union[str, ChannelDimension]] = None     ,		**__a:						Tuple     ,		)->    Union[str, Any]:
											return rescale(__a     ,		scale=__a     ,		data_format=__a     ,		**__a	)

					def 		a__						(	self:						Dict     ,		__a:						np.ndarray     ,		__a:						Union[float, List[float]]     ,		__a:						Union[float, List[float]]     ,		__a:						Optional[Union[str, ChannelDimension]] = None     ,		**__a:						List[Any]     ,		)->    np.ndarray:
											return normalize(__a     ,		mean=__a     ,		std=__a     ,		data_format=__a     ,		**__a	)







					def 		a__						(	self:						Dict     ,		__a:						ImageInput     ,		__a:						bool = None     ,		__a:						Dict[str, int] = None     ,		__a:						float = None     ,		__a:						PILImageResampling = 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:						Optional[Union[str, TensorType]] = None     ,		__a:						ChannelDimension = ChannelDimension.FIRST     ,		**__a:						str     ,		)->    PIL.Image.Image:
											lowerCamelCase					:					Dict        =	do_resize if do_resize is not None else self.do_resize
											lowerCamelCase					:					List[str]        =	crop_pct if crop_pct is not None else self.crop_pct
											lowerCamelCase					:					Optional[int]        =	resample if resample is not None else self.resample
											lowerCamelCase					:					Union[str, Any]        =	do_rescale if do_rescale is not None else self.do_rescale
											lowerCamelCase					:					List[str]        =	rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCamelCase					:					Optional[Any]        =	do_normalize if do_normalize is not None else self.do_normalize
											lowerCamelCase					:					int        =	image_mean if image_mean is not None else self.image_mean
											lowerCamelCase					:					Tuple        =	image_std if image_std is not None else self.image_std

											lowerCamelCase					:					Tuple        =	size if size is not None else self.size
											lowerCamelCase					:					Any        =	get_size_dict(__a     ,		default_to_square=__a	)

											lowerCamelCase					:					Any        =	make_list_of_images(__a	)

											if not valid_images(__a	):
																	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 or resample is None:
																	raise ValueError("""Size and resample must be specified if do_resize is True."""	)

											if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("""crop_pct must be specified if size < 384."""	)

											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."""	)

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

											if do_resize:
																	lowerCamelCase					:					Tuple        =	[self.resize(image=__a     ,		size=__a     ,		crop_pct=__a     ,		resample=__a	) for image in images]

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

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

											lowerCamelCase					:					Dict        =	[to_channel_dimension_format(__a     ,		__a	) for image in images]

											lowerCamelCase					:					int        =	{"""pixel_values""": images}
											return BatchFeature(data=__a     ,		tensor_type=__a	)








"""simple docstring"""


from __future__ import annotations

__lowerCamelCase      :int	    =		10




def   snake_case		(     UpperCamelCase__       :						list[int]    )   ->      list[int]:
      lowerCamelCase					:					int        =	1
      lowerCamelCase					:					Union[str, Any]        =	max(UpperCamelCase__    )
      while placement <= max_digit:
            # declare and initialize empty buckets
            lowerCamelCase					:					list[list]        =	[[] for _ in range(UpperCamelCase__    )]
            # split list_of_ints between the buckets
            for i in list_of_ints:
                  lowerCamelCase					:					Any        =	int((i / placement) % RADIX    )
                  buckets[tmp].append(UpperCamelCase__    )
            # put each buckets' contents into list_of_ints
            lowerCamelCase					:					Dict        =	0
            for b in range(UpperCamelCase__    ):
                  for i in buckets[b]:
                        lowerCamelCase					:					List[str]        =	i
                        a += 1
        # move to next
            placement *= RADIX
      return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()




