

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, logging


A__						=							logging.get_logger(__name__)

class 				__UpperCamelCase				(					UpperCamelCase_			):
	_lowercase  :   Optional[int]       				=  ["pixel_values"]







	def __init__(   self: List[str]  , __UpperCamelCase: int = True  , __UpperCamelCase: Any = None  , __UpperCamelCase: List[Any] = PILImageResampling.BILINEAR  , __UpperCamelCase: Dict = True  , __UpperCamelCase: List[str] = None  , __UpperCamelCase: Any = True  , __UpperCamelCase: List[Any] = 1 / 2_55  , __UpperCamelCase: int = True  , __UpperCamelCase: str = None  , __UpperCamelCase: Optional[int] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						super().__init__(**__a      )
						__magic_name__							=    size if size is not None else {'shortest_edge': 2_56}
						__magic_name__							=    get_size_dict(__a  , default_to_square=__a      )
						__magic_name__							=    crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						__magic_name__							=    get_size_dict(__a      )
						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    resample
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_factor
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__magic_name__							=    image_std if image_std is not None else IMAGENET_STANDARD_STD







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Tuple = PILImageResampling.BICUBIC  , __UpperCamelCase: Tuple = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Optional[Any] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__a      )
						return center_crop(__a  , size=(size['height'], size['width'])  , data_format=__a  , **__a      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: str  , __UpperCamelCase: Tuple  , __UpperCamelCase: Optional[int] = None  , **__UpperCamelCase: str      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Any  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any] = None  , **__UpperCamelCase: Any  , ):

						'''simple docstring'''





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







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

						'''simple docstring'''





						__magic_name__							=    do_resize if do_resize is not None else self.do_resize
						__magic_name__							=    size if size is not None else self.size
						__magic_name__							=    get_size_dict(__a  , default_to_square=__a      )
						__magic_name__							=    resample if resample is not None else self.resample
						__magic_name__							=    do_center_crop if do_center_crop is not None else self.do_center_crop
						__magic_name__							=    crop_size if crop_size is not None else self.crop_size
						__magic_name__							=    get_size_dict(__a      )
						__magic_name__							=    do_rescale if do_rescale is not None else self.do_rescale
						__magic_name__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
						__magic_name__							=    do_normalize if do_normalize is not None else self.do_normalize
						__magic_name__							=    image_mean if image_mean is not None else self.image_mean
						__magic_name__							=    image_std if image_std is not None else self.image_std

						__magic_name__							=    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:
											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.
						__magic_name__							=    [to_numpy_array(__a      ) for image in images]

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

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

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

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

						__magic_name__							=    [to_channel_dimension_format(__a  , __a      ) for image in images]

						__magic_name__							=    {'pixel_values': images}
						return BatchFeature(data=__a  , tensor_type=__a      )



from __future__ import annotations




def 					_lowercase     (				a_				:					list[int]				)				->	int:




					'''simple docstring'''







					if not nums:
										return 0
					__magic_name__							=    nums[0]
					__magic_name__							=    0
					for num in nums[1:]:
										__magic_name__,	__magic_name__							=    (
										    max_excluding + num,
										    max(a_					,a_				),
										)
					return max(a_					,a_				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




