

"""simple docstring"""


def     a__    (    __SCREAMING_SNAKE_CASE )	->	int:

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


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





"""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


__A						   =			logging.get_logger(__name__)





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  ["""pixel_values"""]

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Optional[Dict[str, int]] = None     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  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__							:				Optional[int]     ,  )->   None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			size if size is not None else {"shortest_edge": 2_5_6}
						__lowerCAmelCase:       str          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:       Optional[Any]          =			get_size_dict(UpperCamelCase__     ,  param_name="crop_size")
						__lowerCAmelCase:       str          =			do_resize
						__lowerCAmelCase:       Any          =			size
						__lowerCAmelCase:       Dict          =			resample
						__lowerCAmelCase:       Tuple          =			do_center_crop
						__lowerCAmelCase:       str          =			crop_size
						__lowerCAmelCase:       List[Any]          =			do_rescale
						__lowerCAmelCase:       int          =			rescale_factor
						__lowerCAmelCase:       List[Any]          =			do_normalize
						__lowerCAmelCase:       Any          =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase:       Optional[Any]          =			image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BICUBIC     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Dict     ,  )->   np.ndarray:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Tuple     ,  )->   np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			get_size_dict(UpperCamelCase__)
						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(UpperCamelCase__     ,  size=(size["height"], size["width"])     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				float     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Optional[int])->   np.ndarray:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Union[float, List[float]]     ,  UpperCamelCase__							:				Union[float, List[float]]     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   np.ndarray:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase:       str          =			size if size is not None else self.size
						__lowerCAmelCase:       Tuple          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			resample if resample is not None else self.resample
						__lowerCAmelCase:       str          =			do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCAmelCase:       Tuple          =			crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase:       List[Any]          =			get_size_dict(UpperCamelCase__     ,  param_name="crop_size")
						__lowerCAmelCase:       List[Any]          =			do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase:       Optional[int]          =			rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase:       Optional[int]          =			do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase:       Union[str, Any]          =			image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase:       Tuple          =			image_std if image_std is not None else self.image_std

						__lowerCAmelCase:       Union[str, Any]          =			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:
								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:       Tuple          =			[to_numpy_array(UpperCamelCase__) for image in images]

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

						if do_center_crop:
								__lowerCAmelCase:       Optional[Any]          =			[self.center_crop(image=UpperCamelCase__     ,  size=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase:       Optional[Any]          =			[self.rescale(image=UpperCamelCase__     ,  scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase:       List[str]          =			[self.normalize(image=UpperCamelCase__     ,  mean=UpperCamelCase__     ,  std=UpperCamelCase__) for image in images]

						__lowerCAmelCase:       Optional[Any]          =			[to_channel_dimension_format(UpperCamelCase__     ,  UpperCamelCase__) for image in images]

						__lowerCAmelCase:       List[str]          =			{"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__     ,  tensor_type=UpperCamelCase__)




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[Tuple] = None)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			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__):
										__lowerCAmelCase:       Optional[int]          =			target_sizes.numpy()

								__lowerCAmelCase:       List[Any]          =			[]

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

						return semantic_segmentation


