
"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowercase				 =					logging.get_logger(__name__)







class 					lowerCAmelCase_					(   SCREAMING_SNAKE_CASE_     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['pixel_values']


						def __init__(			self  :   List[Any]	,A_  :   bool = True	,A_  :   Optional[Dict[str, int]] = None	,A_  :   PILImageResampling = PILImageResampling.BICUBIC	,A_  :   bool = True	,A_  :   bool = True	,A_  :   Union[int, float] = 1 / 255	,A_  :   Dict[str, int] = None	,A_  :   bool = True	,A_  :   Optional[Union[float, List[float]]] = None	,A_  :   Optional[Union[float, List[float]]] = None	,**A_  :   Optional[int]	,)  -> Optional[Any]:
							super().__init__(**A_   )
							A     = size if size is not None else {'height': 224, 'width': 224}
							A     = get_size_dict(A_   )
							A     = crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A     = get_size_dict(A_	,default_to_square=A_	,param_name='crop_size'   )

							A     = do_resize
							A     = do_rescale
							A     = do_normalize
							A     = do_center_crop
							A     = crop_size
							A     = size
							A     = resample
							A     = rescale_factor
							A     = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							A     = image_std if image_std is not None else IMAGENET_DEFAULT_STD


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   np.ndarray	,A_  :   Dict[str, int]	,A_  :   PILImageResampling = PILImageResampling.BILINEAR	,A_  :   Optional[Union[str, ChannelDimension]] = None	,**A_  :   Any	,)  -> Tuple:
							A     = get_size_dict(A_   )

							if "shortest_edge" in size:
								A     = get_resize_output_image_size(A_	,size=size['shortest_edge']	,default_to_square=A_   )
								# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
								A     = (size['height'], size['width'])
							else:
								raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
							return resize(A_	,size=A_	,resample=A_	,data_format=A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   np.ndarray	,A_  :   Dict[str, int]	,A_  :   Optional[Union[str, ChannelDimension]] = None	,**A_  :   int	,)  -> int:
							A     = get_size_dict(A_   )
							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(A_	,size=(size['height'], size['width'])	,data_format=A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   np.ndarray	,A_  :   float	,A_  :   Optional[Union[str, ChannelDimension]] = None	,**A_  :   Dict   )  -> Dict:
							return rescale(A_	,scale=A_	,data_format=A_	,**A_   )


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




						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   ImageInput	,A_  :   Optional[bool] = None	,A_  :   Dict[str, int] = None	,A_  :   PILImageResampling = None	,A_  :   bool = None	,A_  :   int = None	,A_  :   Optional[bool] = None	,A_  :   Optional[float] = None	,A_  :   Optional[bool] = None	,A_  :   Optional[Union[float, List[float]]] = None	,A_  :   Optional[Union[float, List[float]]] = None	,A_  :   Optional[Union[str, TensorType]] = None	,A_  :   Union[str, ChannelDimension] = ChannelDimension.FIRST	,**A_  :   Optional[Any]	,)  -> Any:
							A     = do_resize if do_resize is not None else self.do_resize
							A     = do_rescale if do_rescale is not None else self.do_rescale
							A     = do_normalize if do_normalize is not None else self.do_normalize
							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(A_	,param_name='crop_size'	,default_to_square=A_   )
							A     = resample if resample is not None else self.resample
							A     = rescale_factor if rescale_factor is not None else self.rescale_factor
							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     = size if size is not None else self.size
							A     = get_size_dict(A_   )

							if not is_batched(A_   ):
								A     = [images]

							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.'   )

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

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

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

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

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

							A     = [to_channel_dimension_format(A_	,A_   ) for image in images]

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




from __future__ import annotations

import numpy as np

def        __a			(   __lowerCAmelCase   )     ->     Optional[Any]:
					return np.maximum(0      ,	__lowerCAmelCase   )


if __name__ == "__main__":
			print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]