



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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


if is_vision_available():
					import PIL


class      __lowerCAmelCase     (		snake_case_      ):
					"""simple docstring"""






					A__				:		Optional[Any]			       =    ["""pixel_values"""]



					def __init__(     self					:       Tuple				,					_snake_case					:       List[Any] = True				,					_snake_case					:       Optional[Any] = None				,					_snake_case					:       List[str] = PILImageResampling.BICUBIC				,					_snake_case					:       List[Any] = True				,					_snake_case					:       Union[str, Any] = None				,					_snake_case					:       Optional[Any] = True				,					_snake_case					:       List[Any] = 1 / 2_55				,					_snake_case					:       List[str] = True				,					_snake_case					:       Any = None				,					_snake_case					:       List[Any] = None				,					_snake_case					:       Union[str, Any] = True				,					**_snake_case					:       Dict				,					):






								"""simple docstring"""


								super().__init__(**_snake_case					)
								A__       =    size if size is not None else {'shortest_edge': 2_24}
								A__       =    get_size_dict(_snake_case				,					default_to_square=_snake_case					)
								A__       =    crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								A__       =    get_size_dict(_snake_case				,					default_to_square=_snake_case				,					param_name='crop_size'					)

								A__       =    do_resize
								A__       =    size
								A__       =    resample
								A__       =    do_center_crop
								A__       =    crop_size
								A__       =    do_rescale
								A__       =    rescale_factor
								A__       =    do_normalize
								A__       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								A__       =    image_std if image_std is not None else OPENAI_CLIP_STD
								A__       =    do_convert_rgb



					def     _a							(     self					:       Dict				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       str				,					_snake_case					:       List[Any] = PILImageResampling.BICUBIC				,					_snake_case					:       Optional[Any] = None				,					**_snake_case					:       List[Any]				,					):






								"""simple docstring"""


								A__       =    get_size_dict(_snake_case				,					default_to_square=_snake_case					)
								if "shortest_edge" not in size:
											raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
								A__       =    get_resize_output_image_size(_snake_case				,					size=size['shortest_edge']				,					default_to_square=_snake_case					)
								return resize(_snake_case				,					size=_snake_case				,					resample=_snake_case				,					data_format=_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       str = None				,					**_snake_case					:       List[str]				,					):






								"""simple docstring"""


								A__       =    get_size_dict(_snake_case					)
								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(_snake_case				,					size=(size['height'], size['width'])				,					data_format=_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Tuple				,					_snake_case					:       Tuple				,					_snake_case					:       int				,					_snake_case					:       List[str] = None				,					**_snake_case					:       Optional[Any]				,					):






								"""simple docstring"""


								return rescale(_snake_case				,					scale=_snake_case				,					data_format=_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       Any				,					_snake_case					:       List[Any]				,					_snake_case					:       List[str]				,					_snake_case					:       int = None				,					**_snake_case					:       Tuple				,					):






								"""simple docstring"""


								return normalize(_snake_case				,					mean=_snake_case				,					std=_snake_case				,					data_format=_snake_case				,					**_snake_case					)



					def     _a							(     self					:       str				,					_snake_case					:       Optional[int]				,					_snake_case					:       List[str] = None				,					_snake_case					:       Tuple = None				,					_snake_case					:       Tuple = None				,					_snake_case					:       Any = None				,					_snake_case					:       int = None				,					_snake_case					:       Dict = None				,					_snake_case					:       str = None				,					_snake_case					:       Dict = None				,					_snake_case					:       List[str] = None				,					_snake_case					:       Dict = None				,					_snake_case					:       Tuple = None				,					_snake_case					:       List[Any] = None				,					_snake_case					:       str = ChannelDimension.FIRST				,					**_snake_case					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    do_resize if do_resize is not None else self.do_resize
								A__       =    size if size is not None else self.size
								A__       =    get_size_dict(_snake_case				,					param_name='size'				,					default_to_square=_snake_case					)
								A__       =    resample if resample is not None else self.resample
								A__       =    do_center_crop if do_center_crop is not None else self.do_center_crop
								A__       =    crop_size if crop_size is not None else self.crop_size
								A__       =    get_size_dict(_snake_case				,					param_name='crop_size'				,					default_to_square=_snake_case					)
								A__       =    do_rescale if do_rescale is not None else self.do_rescale
								A__       =    rescale_factor if rescale_factor is not None else self.rescale_factor
								A__       =    do_normalize if do_normalize is not None else self.do_normalize
								A__       =    image_mean if image_mean is not None else self.image_mean
								A__       =    image_std if image_std is not None else self.image_std
								A__       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								A__       =    make_list_of_images(_snake_case					)

								if not valid_images(_snake_case					):
											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__       =    [convert_to_rgb(_snake_case					) for image in images]

								# All transformations expect numpy arrays.
								A__       =    [to_numpy_array(_snake_case					) for image in images]

								if do_resize:
											A__       =    [self.resize(image=_snake_case				,					size=_snake_case				,					resample=_snake_case					) for image in images]

								if do_center_crop:
											A__       =    [self.center_crop(image=_snake_case				,					size=_snake_case					) for image in images]

								if do_rescale:
											A__       =    [self.rescale(image=_snake_case				,					scale=_snake_case					) for image in images]

								if do_normalize:
											A__       =    [self.normalize(image=_snake_case				,					mean=_snake_case				,					std=_snake_case					) for image in images]

								A__       =    [to_channel_dimension_format(_snake_case				,					_snake_case					) for image in images]

								A__       =    {'pixel_values': images}
								return BatchFeature(data=_snake_case				,					tensor_type=_snake_case					)









def A						(       __UpperCamelCase					)						->      bool:
			return number & 1 == 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




