





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


__lowercase       :Tuple       =  logging.get_logger(__name__)


if is_vision_available():
				import PIL



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     Dict							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     bool = True							,	**a				:     Tuple							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				str   =					size if size is not None else {"shortest_edge": 2_24}
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				int   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				int   =					get_size_dict(a							,	default_to_square=a							,	param_name="crop_size"    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					size
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Any   =					crop_size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													SCREAMING_SNAKE_CASE__     :				Any   =					image_std if image_std is not None else OPENAI_CLIP_STD
													SCREAMING_SNAKE_CASE__     :				Tuple   =					do_convert_rgb

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Any							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				str   =					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()}"""    )
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[Any]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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      A_  (      self				:     str							,	a				:     np.ndarray							,	a				:     Union[int, float]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Any							,	)	->Dict:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

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






							def      A_  (      self				:     int							,	a				:     ImageInput							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = None							,	a				:     bool = None							,	a				:     int = None							,	a				:     bool = None							,	a				:     float = None							,	a				:     bool = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     bool = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Optional[ChannelDimension] = ChannelDimension.FIRST							,	**a				:     Union[str, Any]							,	)	->PIL.Image.Image:
													SCREAMING_SNAKE_CASE__     :				Any   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a							,	param_name="size"							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :				List[str]   =					crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					get_size_dict(a							,	param_name="crop_size"							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				str   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std if image_std is not None else self.image_std
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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."    )

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					[convert_to_rgb(a    ) for image in images]

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE__     :				List[str]   =					[to_numpy_array(a    ) for image in images]

													if do_resize:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[self.resize(image=a							,	size=a							,	resample=a    ) for image in images]

													if do_center_crop:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[self.center_crop(image=a							,	size=a    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[self.rescale(image=a							,	scale=a    ) for image in images]

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

													SCREAMING_SNAKE_CASE__     :				Dict   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					{"pixel_values": images}
													return BatchFeature(data=a							,	tensor_type=a    )






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list[int]			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) < k or k < 0:
												raise ValueError("Invalid Input"     )
						SCREAMING_SNAKE_CASE__     :				int   =					sum(array[:k]     )
						for i in range(len(_lowerCamelCase     ) - k     ):
												SCREAMING_SNAKE_CASE__     :				str   =					current_sum - array[i] + array[i + k]
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max(_lowerCamelCase			,   _lowerCamelCase     )
						return max_sum


if __name__ == "__main__":
				from doctest import testmod
				from random import randint

				testmod()
				__lowercase       :List[str]       =  [randint(-1_000, 1_000) for i in range(100)]
				__lowercase       :Any       =  randint(0, 110)
				print(f"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")