


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, 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__       :	Tuple     				=logging.get_logger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		["""pixel_values"""]






							def __init__(	self       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = PIL.Image.NEAREST       ,		lowerCAmelCase__ = False       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = 1 / 2_5_5       ,		lowerCAmelCase__ = False       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = True       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			int       						= size if size is not None else {'height': 3_4_6, 'width': 3_4_6}
										SCREAMING_SNAKE_CASE_						:			List[str]       						= get_size_dict(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			int       						= crop_size if crop_size is not None else {'height': 2_8_9, 'width': 2_8_9}
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= get_size_dict(lowerCAmelCase__       ,		param_name='crop_size'    )

										SCREAMING_SNAKE_CASE_						:			Tuple       						= do_resize
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= size
										SCREAMING_SNAKE_CASE_						:			int       						= resample
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= do_center_crop
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= crop_size
										SCREAMING_SNAKE_CASE_						:			Any       						= do_rescale
										SCREAMING_SNAKE_CASE_						:			Tuple       						= rescale_factor
										SCREAMING_SNAKE_CASE_						:			str       						= rescale_offset
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= do_normalize
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										SCREAMING_SNAKE_CASE_						:			Tuple       						= image_std if image_std is not None else IMAGENET_STANDARD_STD
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= include_top






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = PIL.Image.NEAREST       ,		lowerCAmelCase__ = None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= get_size_dict(lowerCAmelCase__    )
										if "height" not in size or "width" not in size:
													raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
										return resize(
										    lowerCAmelCase__       ,		size=(size['height'], size['width'])       ,		resample=lowerCAmelCase__       ,		data_format=lowerCAmelCase__       ,		**lowerCAmelCase__    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= get_size_dict(lowerCAmelCase__    )
										if "height" not in size or "width" not in size:
													raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
										return center_crop(lowerCAmelCase__       ,		size=(size['height'], size['width'])       ,		data_format=lowerCAmelCase__       ,		**lowerCAmelCase__    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										if offset:
													SCREAMING_SNAKE_CASE_						:			Any       						= (image - 127.5) * scale
													if data_format is not None:
																SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= to_channel_dimension_format(lowerCAmelCase__       ,		lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= rescaled_image.astype(np.floataa    )
										else:
													SCREAMING_SNAKE_CASE_						:			Any       						= rescale(lowerCAmelCase__       ,		scale=lowerCAmelCase__       ,		data_format=lowerCAmelCase__       ,		**lowerCAmelCase__    )
										return rescaled_image






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__ = None       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	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__ = None       ,		lowerCAmelCase__ = ChannelDimension.FIRST       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= do_resize if do_resize is not None else self.do_resize
										SCREAMING_SNAKE_CASE_						:			Any       						= resample if resample is not None else self.resample
										SCREAMING_SNAKE_CASE_						:			str       						= do_center_crop if do_center_crop is not None else self.do_center_crop
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= do_rescale if do_rescale is not None else self.do_rescale
										SCREAMING_SNAKE_CASE_						:			Tuple       						= rescale_factor if rescale_factor is not None else self.rescale_factor
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= rescale_offset if rescale_offset is not None else self.rescale_offset
										SCREAMING_SNAKE_CASE_						:			Dict       						= do_normalize if do_normalize is not None else self.do_normalize
										SCREAMING_SNAKE_CASE_						:			Dict       						= image_mean if image_mean is not None else self.image_mean
										SCREAMING_SNAKE_CASE_						:			Any       						= image_std if image_std is not None else self.image_std
										SCREAMING_SNAKE_CASE_						:			Dict       						= include_top if include_top is not None else self.include_top

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= size if size is not None else self.size
										SCREAMING_SNAKE_CASE_						:			str       						= get_size_dict(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= crop_size if crop_size is not None else self.crop_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= get_size_dict(lowerCAmelCase__       ,		param_name='crop_size'    )

										SCREAMING_SNAKE_CASE_						:			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 or resample is None:
													raise ValueError('Size and resample 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.
										SCREAMING_SNAKE_CASE_						:			Any       						= [to_numpy_array(lowerCAmelCase__    ) for image in images]

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

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

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

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

										if include_top:
													SCREAMING_SNAKE_CASE_						:			str       						= [self.normalize(image=lowerCAmelCase__       ,		mean=[0, 0, 0]       ,		std=lowerCAmelCase__    ) for image in images]

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

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= {'pixel_values': images}
										return BatchFeature(data=lowerCAmelCase__       ,		tensor_type=lowerCAmelCase__    )









from __future__ import annotations






def 		a__       (							A__							):
			if len(A__							) == 0:
						raise ValueError('find_max() arg is an empty sequence'							)
			SCREAMING_SNAKE_CASE_						:			str       						= nums[0]
			for x in nums:
						if x > max_num:
									SCREAMING_SNAKE_CASE_						:			Optional[int]       						= x
			return max_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)





