
'''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,
    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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


UpperCAmelCase__ :			List[Any]											=		logging.get_logger(__name__)






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Union[str, Any]   =  ['pixel_values']





	def __init__(       self      :							Union[str, Any]   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Optional[Dict[str, int]] = None   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BILINEAR   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Union[int, float] = 1 / 255   ,				__magic_name__      :							bool = True   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				**__magic_name__      :							Any   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)
				lowerCAmelCase__										=				size if size is not None else {"shortest_edge": 256}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				default_to_square=__magic_name__				)
				lowerCAmelCase__										=				crop_size if crop_size is not None else {"height": 224, "width": 224}
				lowerCAmelCase__										=				get_size_dict(__magic_name__   ,				param_name="crop_size"				)
				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				resample
				lowerCAmelCase__										=				do_center_crop
				lowerCAmelCase__										=				crop_size
				lowerCAmelCase__										=				do_rescale
				lowerCAmelCase__										=				rescale_factor
				lowerCAmelCase__										=				do_normalize
				lowerCAmelCase__										=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				lowerCAmelCase__										=				image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Any   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							PILImageResampling = PILImageResampling.BICUBIC   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Dict[str, int]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Dict   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__				)
				if "height" not in size or "width" not in size:
							raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
				return center_crop(__magic_name__   ,				size=(size["height"], size["width"])   ,				data_format=__magic_name__   ,				**__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							float   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							np.ndarray   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Union[float, List[float]]   ,				__magic_name__      :							Optional[Union[str, ChannelDimension]] = None   ,				**__magic_name__      :							Tuple   ,				):


				"""simple docstring"""

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





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str   ,				__magic_name__      :							ImageInput   ,				__magic_name__      :							Optional[bool] = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							PILImageResampling = None   ,				__magic_name__      :							bool = None   ,				__magic_name__      :							Dict[str, int] = None   ,				__magic_name__      :							Optional[bool] = None   ,				__magic_name__      :							Optional[float] = None   ,				__magic_name__      :							Optional[bool] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[float, List[float]]] = None   ,				__magic_name__      :							Optional[Union[str, TensorType]] = None   ,				__magic_name__      :							Union[str, ChannelDimension] = ChannelDimension.FIRST   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

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

				lowerCAmelCase__										=				make_list_of_images(__magic_name__				)

				if not valid_images(__magic_name__				):
							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.
				lowerCAmelCase__										=				[to_numpy_array(__magic_name__				) for image in images]

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

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

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

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

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

				lowerCAmelCase__										=				{"pixel_values": images}
				return BatchFeature(data=__magic_name__   ,				tensor_type=__magic_name__				)







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]   ,				__magic_name__      :							Tuple   ,				__magic_name__      :							List[Tuple] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				outputs.logits

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

							if is_torch_tensor(__magic_name__				):
										lowerCAmelCase__										=				target_sizes.numpy()

							lowerCAmelCase__										=				[]

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

				return semantic_segmentation












'''simple docstring'''






def 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise TypeError("""only integers accepted as input""" )
			else:
						__snake_case					:      List[Any]							      =     str(abs(_lowerCamelCase ) )
						__snake_case					:      Union[str, Any]							      =     [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
						for index in range(len(_lowerCamelCase ) ):
									num_transpositions[index].pop(_lowerCamelCase )
						return max(
						    int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
	__import__("doctest").testmod()


