




"""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      a__				(	__magic_name__   ):
			lowercase_    =	["pixel_values"]


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


						"""simple docstring"""




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

						__UpperCAmelCase	: Optional[Any]		=					do_resize
						__UpperCAmelCase	: int		=					size
						__UpperCAmelCase	: Dict		=					resample
						__UpperCAmelCase	: str		=					do_rescale
						__UpperCAmelCase	: str		=					rescale_factor
						__UpperCAmelCase	: Dict		=					do_center_crop
						__UpperCAmelCase	: List[str]		=					crop_size
						__UpperCAmelCase	: Tuple		=					do_flip_channel_order


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    PILImageResampling = PIL.Image.BILINEAR      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    int      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						if "shortest_edge" not in size:
									raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
						__UpperCAmelCase	: int		=					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       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    List[Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					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()}")
						return center_crop(UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


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


						"""simple docstring"""




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


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None):


						"""simple docstring"""




						return flip_channel_order(UpperCamelCase_      ,  data_format=UpperCamelCase_)


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


						"""simple docstring"""




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

						__UpperCAmelCase	: Tuple		=					size if size is not None else self.size
						__UpperCAmelCase	: List[str]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					crop_size if crop_size is not None else self.crop_size
						__UpperCAmelCase	: Optional[int]		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

						__UpperCAmelCase	: Tuple		=					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_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.
						__UpperCAmelCase	: Tuple		=					[to_numpy_array(UpperCamelCase_) for image in images]

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

						if do_center_crop:
									__UpperCAmelCase	: List[Any]		=					[self.center_crop(image=UpperCamelCase_      ,  size=UpperCamelCase_) for image in images]

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

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

						__UpperCAmelCase	: Tuple		=					[to_channel_dimension_format(UpperCamelCase_      ,  UpperCamelCase_) for image in images]

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


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    List[Tuple] = None):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[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_):
												__UpperCAmelCase	: str		=					target_sizes.numpy()

									__UpperCAmelCase	: Tuple		=					[]

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

						return semantic_segmentation








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						__UpperCAmelCase	: Union[str, Any]		=					_modexpt(UpperCamelCase				,       exponent // 2				,       UpperCamelCase							) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase				,       exponent - 1				,       UpperCamelCase							)) % modulo_value

def 					_UpperCamelCase    (			UpperCamelCase = 1777				,       UpperCamelCase = 1855				,       UpperCamelCase = 8							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					base
			for _ in range(1				,       UpperCamelCase							):
						__UpperCAmelCase	: str		=					_modexpt(UpperCamelCase				,       UpperCamelCase				,       10**digits							)

			return result


if __name__ == "__main__":
					print(f'''{solution() = }''')


