


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


lowerCAmelCase	=							logging.get_logger(__name__)


class 	_a							(      __SCREAMING_SNAKE_CASE      ):
							_lowercase     :      Optional[Any]  				=			['pixel_values']

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     Tuple = True   ,						UpperCamelCase_:     Union[str, Any] = None   ,						UpperCamelCase_:     List[Any] = PILImageResampling.BILINEAR   ,						UpperCamelCase_:     Any = True   ,						UpperCamelCase_:     List[str] = 1 / 255   ,						UpperCamelCase_:     int = True   ,						UpperCamelCase_:     int = None   ,						UpperCamelCase_:     Optional[int] = True   ,						**UpperCamelCase_:     Optional[Any]   ,						)      ->       Tuple:





									"""simple docstring"""







									super().__init__(**_a							)
									lowercase__						=				size if size is not None else {'''shortest_edge''': 224}
									lowercase__						=				get_size_dict(_a   ,						default_to_square=_a							)
									lowercase__						=				crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
									lowercase__						=				get_size_dict(_a   ,						param_name='''crop_size'''							)

									lowercase__						=				do_resize
									lowercase__						=				size
									lowercase__						=				resample
									lowercase__						=				do_rescale
									lowercase__						=				rescale_factor
									lowercase__						=				do_center_crop
									lowercase__						=				crop_size
									lowercase__						=				do_flip_channel_order

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Tuple = PIL.Image.BILINEAR   ,						UpperCamelCase_:     Any = None   ,						**UpperCamelCase_:     Any   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				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()}'							)
									lowercase__						=				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 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Any = None   ,						**UpperCamelCase_:     Optional[Any]   ,						)      ->       int:





									"""simple docstring"""







									lowercase__						=				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 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     str = None   ,						**UpperCamelCase_:     List[Any]   ,						)      ->       Any:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Dict = None							)      ->       Dict:





									"""simple docstring"""







									return flip_channel_order(_a   ,						data_format=_a							)

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     str = None   ,						UpperCamelCase_:     Optional[int] = None   ,						UpperCamelCase_:     Optional[Any] = None   ,						UpperCamelCase_:     List[Any] = None   ,						UpperCamelCase_:     Dict = None   ,						UpperCamelCase_:     Optional[int] = None   ,						UpperCamelCase_:     List[Any] = None   ,						UpperCamelCase_:     int = None   ,						UpperCamelCase_:     Union[str, Any] = None   ,						UpperCamelCase_:     int = ChannelDimension.FIRST   ,						**UpperCamelCase_:     List[str]   ,						)      ->       str:





									"""simple docstring"""







									lowercase__						=				do_resize if do_resize is not None else self.do_resize
									lowercase__						=				resample if resample is not None else self.resample
									lowercase__						=				do_rescale if do_rescale is not None else self.do_rescale
									lowercase__						=				rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__						=				do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase__						=				(
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									lowercase__						=				size if size is not None else self.size
									lowercase__						=				get_size_dict(_a   ,						default_to_square=_a							)
									lowercase__						=				crop_size if crop_size is not None else self.crop_size
									lowercase__						=				get_size_dict(_a   ,						param_name='''crop_size'''							)

									lowercase__						=				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.
									lowercase__						=				[to_numpy_array(_a							) for image in images]

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

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

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

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

									lowercase__						=				[to_channel_dimension_format(_a   ,						_a							) for image in images]

									lowercase__						=				{'''pixel_values''': images}
									return BatchFeature(data=_a   ,						tensor_type=_a							)


							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     Optional[int] = None							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				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							):
													lowercase__						=				target_sizes.numpy()

											lowercase__						=				[]

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

									return semantic_segmentation






def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		return base * power(SCREAMING_SNAKE_CASE						,    (exponent - 1)					) if exponent else 1


if __name__ == "__main__":
			print('Raise base to the power of exponent using recursion...')
			lowerCAmelCase	=							int(input('Enter the base: ').strip())
			lowerCAmelCase	=							int(input('Enter the exponent: ').strip())
			lowerCAmelCase	=							power(base, abs(exponent))
			if exponent < 0:  # power() does not properly deal w/ negative exponents
						lowerCAmelCase	=							1 / result
			print(f"""{base} to the power of {exponent} is {result}""")


