



from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:Optional[int]					=						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__					(		__SCREAMING_SNAKE_CASE   ):




				'''simple docstring'''






				__lowerCamelCase      :			Dict =				["pixel_values"]






				def __init__(	self,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = True,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = IMAGENET_DEFAULT_MEAN,		lowerCamelCase__ = IMAGENET_DEFAULT_STD,		**lowerCamelCase__,		):
								super().__init__(**UpperCamelCase__		)
								A			:	Optional[Any]										=       size if size is not None else {'''shortest_edge''': 224}
								A			:	Optional[Any]										=       get_size_dict(UpperCamelCase__,		default_to_square=UpperCamelCase__		)
								A			:	Any										=       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								A			:	str										=       get_size_dict(UpperCamelCase__,		param_name="""crop_size"""		)

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






				def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = PILImageResampling.BICUBIC,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
								A			:	Union[str, Any]										=       get_size_dict(UpperCamelCase__,		default_to_square=UpperCamelCase__		)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
												A			:	List[str]										=       int((256 / 224) * size["""shortest_edge"""]		)
												A			:	Optional[Any]										=       get_resize_output_image_size(UpperCamelCase__,		size=UpperCamelCase__,		default_to_square=UpperCamelCase__		)
												A			:	Optional[Any]										=       {'''height''': output_size[0], '''width''': output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
												raise ValueError(
												    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
								return resize(
								    UpperCamelCase__,		size=(size_dict["""height"""], size_dict["""width"""]),		resample=UpperCamelCase__,		data_format=UpperCamelCase__,		**UpperCamelCase__		)






				def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
								A			:	int										=       get_size_dict(UpperCamelCase__		)
								if "height" not in size or "width" not in size:
												raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
								return center_crop(UpperCamelCase__,		size=(size["""height"""], size["""width"""]),		data_format=UpperCamelCase__,		**UpperCamelCase__		)






				def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
								return rescale(UpperCamelCase__,		scale=UpperCamelCase__,		data_format=UpperCamelCase__,		**UpperCamelCase__		)






				def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
								return normalize(UpperCamelCase__,		mean=UpperCamelCase__,		std=UpperCamelCase__,		data_format=UpperCamelCase__,		**UpperCamelCase__		)






				def       _lowerCAmelCase (	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__ = ChannelDimension.FIRST,		**lowerCamelCase__,		):
								A			:	Optional[Any]										=       do_resize if do_resize is not None else self.do_resize
								A			:	Optional[int]										=       resample if resample is not None else self.resample
								A			:	Dict										=       do_center_crop if do_center_crop is not None else self.do_center_crop
								A			:	Union[str, Any]										=       do_rescale if do_rescale is not None else self.do_rescale
								A			:	Union[str, Any]										=       rescale_factor if rescale_factor is not None else self.rescale_factor
								A			:	List[Any]										=       do_normalize if do_normalize is not None else self.do_normalize
								A			:	List[str]										=       image_mean if image_mean is not None else self.image_mean
								A			:	Any										=       image_std if image_std is not None else self.image_std

								A			:	List[str]										=       size if size is not None else self.size
								A			:	Optional[int]										=       get_size_dict(UpperCamelCase__,		default_to_square=UpperCamelCase__		)
								A			:	List[str]										=       crop_size if crop_size is not None else self.crop_size
								A			:	Tuple										=       get_size_dict(UpperCamelCase__,		param_name="""crop_size"""		)

								A			:	str										=       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."""		)

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

								if do_resize:
												A			:	Optional[int]										=       [self.resize(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__		) for image in images]

								if do_center_crop:
												A			:	List[str]										=       [self.center_crop(UpperCamelCase__,		UpperCamelCase__		) for image in images]

								if do_rescale:
												A			:	Dict										=       [self.rescale(UpperCamelCase__,		UpperCamelCase__		) for image in images]

								if do_normalize:
												A			:	int										=       [self.normalize(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__		) for image in images]

								A			:	int										=       [to_channel_dimension_format(UpperCamelCase__,		UpperCamelCase__		) for image in images]

								A			:	str										=       {'''pixel_values''': images}
								return BatchFeature(data=UpperCamelCase__,		tensor_type=UpperCamelCase__		)







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


