

import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


A_							:			int											= logging.get_logger(__name__)

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			def constraint_to_multiple_of(snake_case__		,				snake_case__		,				snake_case__=0		,				snake_case__=None    ):
						SCREAMING_SNAKE_CASE__								=      round(val / multiple    ) * multiple

						if max_val is not None and x > max_val:
									SCREAMING_SNAKE_CASE__								=      math.floor(val / multiple    ) * multiple

						if x < min_val:
									SCREAMING_SNAKE_CASE__								=      math.ceil(val / multiple    ) * multiple

						return x

			SCREAMING_SNAKE_CASE__								=      (output_size, output_size) if isinstance(snake_case__		,				snake_case__    ) else output_size

			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      get_image_size(snake_case__    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      output_size

			# determine new height and width
			SCREAMING_SNAKE_CASE__								=      output_height / input_height
			SCREAMING_SNAKE_CASE__								=      output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width    ) < abs(1 - scale_height    ):
									# fit width
									SCREAMING_SNAKE_CASE__								=      scale_width
						else:
									# fit height
									SCREAMING_SNAKE_CASE__								=      scale_height

			SCREAMING_SNAKE_CASE__								=      constraint_to_multiple_of(scale_height * input_height		,				multiple=snake_case__    )
			SCREAMING_SNAKE_CASE__								=      constraint_to_multiple_of(scale_width * input_width		,				multiple=snake_case__    )

			return (new_height, new_width)





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[Any]							=							['pixel_values']

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Dict[str, int] = None    ,				__UpperCAmelCase				:						PILImageResampling = PILImageResampling.BILINEAR    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						int = 1    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Union[int, float] = 1 / 2_5_5    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				**__UpperCAmelCase				:						Dict    ,				)   ->    None:
						super().__init__(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      do_resize
						SCREAMING_SNAKE_CASE__								=      size
						SCREAMING_SNAKE_CASE__								=      keep_aspect_ratio
						SCREAMING_SNAKE_CASE__								=      ensure_multiple_of
						SCREAMING_SNAKE_CASE__								=      resample
						SCREAMING_SNAKE_CASE__								=      do_rescale
						SCREAMING_SNAKE_CASE__								=      rescale_factor
						SCREAMING_SNAKE_CASE__								=      do_normalize
						SCREAMING_SNAKE_CASE__								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE__								=      image_std if image_std is not None else IMAGENET_STANDARD_STD

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						Dict[str, int]    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						int = 1    ,				__UpperCAmelCase				:						PILImageResampling = PILImageResampling.BICUBIC    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						Tuple    ,				)   ->    np.ndarray:
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						if "height" not in size or "width" not in size:
									raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""       )
						SCREAMING_SNAKE_CASE__								=      get_resize_output_image_size(
						    __UpperCAmelCase    ,				output_size=(size["""height"""], size["""width"""])    ,				keep_aspect_ratio=__UpperCAmelCase    ,				multiple=__UpperCAmelCase    ,				)
						return resize(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase       )

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						ImageInput    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						int = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						int = None    ,				__UpperCAmelCase				:						PILImageResampling = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						float = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[str, TensorType]] = None    ,				__UpperCAmelCase				:						ChannelDimension = ChannelDimension.FIRST    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    PIL.Image.Image:
						SCREAMING_SNAKE_CASE__								=      do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__								=      size if size is not None else self.size
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						SCREAMING_SNAKE_CASE__								=      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						SCREAMING_SNAKE_CASE__								=      resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE__								=      do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__								=      rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE__								=      do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE__								=      image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE__								=      image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE__								=      make_list_of_images(__UpperCAmelCase       )

						if not valid_images(__UpperCAmelCase       ):
									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_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__								=      [to_numpy_array(__UpperCAmelCase       ) for image in images]

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

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

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

						SCREAMING_SNAKE_CASE__								=      [to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase       ) for image in images]

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Tuple] = None       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(__UpperCAmelCase       ) != len(__UpperCAmelCase       ):
												raise ValueError(
												    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""       )

									if is_torch_tensor(__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__								=      target_sizes.numpy()

									SCREAMING_SNAKE_CASE__								=      []

									for idx in range(len(__UpperCAmelCase       )       ):
												SCREAMING_SNAKE_CASE__								=      torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0       )    ,				size=target_sizes[idx]    ,				mode="""bilinear"""    ,				align_corners=__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      resized_logits[0].argmax(dim=0       )
												semantic_segmentation.append(__UpperCAmelCase       )
						else:
									SCREAMING_SNAKE_CASE__								=      logits.argmax(dim=1       )
									SCREAMING_SNAKE_CASE__								=      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

						return semantic_segmentation





"""simple docstring"""




def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not nums:  # Makes sure that the list is not empty
						raise ValueError("""List is empty"""    )

			SCREAMING_SNAKE_CASE__								=      sum(snake_case__    ) / len(snake_case__    )  # Calculate the average
			return sum(abs(x - average    ) for x in nums    ) / len(snake_case__    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


