
'''simple docstring'''






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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
					import PIL

if is_torch_available():
					import torch


A			      =      logging.get_logger(__name__)




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	['''pixel_values''']
					def __init__(     self,    A = True,    A = None,    A = PILImageResampling.BILINEAR,    A = True,    A = 1 / 255,    A = True,    A = None,    A = True,    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )
									lowerCamelCase					:			Optional[int]									= size if size is not None else {'shortest_edge': 224}
									lowerCamelCase					:			Dict									= get_size_dict(A,    default_to_square=A      )
									lowerCamelCase					:			Any									= crop_size if crop_size is not None else {'height': 256, 'width': 256}
									lowerCamelCase					:			List[str]									= get_size_dict(A,    param_name='crop_size'      )

									lowerCamelCase					:			List[str]									= do_resize
									lowerCamelCase					:			Dict									= size
									lowerCamelCase					:			str									= resample
									lowerCamelCase					:			Optional[Any]									= do_rescale
									lowerCamelCase					:			Union[str, Any]									= rescale_factor
									lowerCamelCase					:			Dict									= do_center_crop
									lowerCamelCase					:			Dict									= crop_size
									lowerCamelCase					:			Optional[int]									= do_flip_channel_order
					def     UpperCAmelCase_							(     self,    A,    A,    A = PIL.Image.BILINEAR,    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= get_size_dict(A,    default_to_square=A      )
									if "shortest_edge" not in size:
													raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''      )
									lowerCamelCase					:			Tuple									= 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     UpperCAmelCase_							(     self,    A,    A,    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= get_size_dict(A      )
									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()}'''      )
									return center_crop(A,    size=(size['height'], size['width']),    data_format=A,    **A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A = None,    **A,    ):

									"""simple docstring"""


									return rescale(A,    scale=A,    data_format=A,    **A      )
					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									return flip_channel_order(A,    data_format=A      )
					def     UpperCAmelCase_							(     self,    A,    A = None,    A = None,    A = None,    A = None,    A = None,    A = None,    A = None,    A = None,    A = None,    A = ChannelDimension.FIRST,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= do_resize if do_resize is not None else self.do_resize
									lowerCamelCase					:			List[Any]									= resample if resample is not None else self.resample
									lowerCamelCase					:			List[Any]									= do_rescale if do_rescale is not None else self.do_rescale
									lowerCamelCase					:			Optional[Any]									= rescale_factor if rescale_factor is not None else self.rescale_factor
									lowerCamelCase					:			Optional[int]									= do_center_crop if do_center_crop is not None else self.do_center_crop
									lowerCamelCase					:			Dict									= (
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									lowerCamelCase					:			List[Any]									= size if size is not None else self.size
									lowerCamelCase					:			str									= get_size_dict(A,    default_to_square=A      )
									lowerCamelCase					:			List[str]									= crop_size if crop_size is not None else self.crop_size
									lowerCamelCase					:			int									= get_size_dict(A,    param_name='crop_size'      )

									lowerCamelCase					:			int									= 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_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

									if do_center_crop and crop_size is None:
													raise ValueError('Crop size must be specified if do_center_crop is True.'      )

									# All transformations expect numpy arrays.
									lowerCamelCase					:			Union[str, Any]									= [to_numpy_array(A      ) for image in images]

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

									if do_center_crop:
													lowerCamelCase					:			Union[str, Any]									= [self.center_crop(image=A,    size=A      ) for image in images]

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

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
													lowerCamelCase					:			Tuple									= [self.flip_channel_order(image=A      ) for image in images]

									lowerCamelCase					:			Optional[Any]									= [to_channel_dimension_format(A,    A      ) for image in images]

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




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= outputs.logits

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

													if is_torch_tensor(A      ):
																	lowerCamelCase					:			str									= target_sizes.numpy()

													lowerCamelCase					:			List[Any]									= []

													for idx in range(len(A      )      ):
																	lowerCamelCase					:			int									= torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0      ),    size=target_sizes[idx],    mode='bilinear',    align_corners=A      )
																	lowerCamelCase					:			Optional[Any]									= resized_logits[0].argmax(dim=0      )
																	semantic_segmentation.append(A      )
									else:
													lowerCamelCase					:			Tuple									= logits.argmax(dim=1      )
													lowerCamelCase					:			Union[str, Any]									= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

									return semantic_segmentation


'''simple docstring'''






def    UpperCAmelCase							(  UpperCAmelCase__							:							list[int]):
				if not nums:  # Makes sure that the list is not empty
								raise ValueError('List is empty')

				lowerCamelCase					:			int									= sum(UpperCAmelCase__) / len(UpperCAmelCase__)  # Calculate the average
				return sum(abs(x - average) for x in nums) / len(UpperCAmelCase__)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
