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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


lowerCamelCase__          =		logging.get_logger(__name__)
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Union[str, Any]:







			def constraint_to_multiple_of(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=0       ,							SCREAMING_SNAKE_CASE_=None     ):
						lowerCAmelCase__      :      List[Any]         =		round(val / multiple     ) * multiple

						if max_val is not None and x > max_val:
									lowerCAmelCase__      :      int         =		math.floor(val / multiple     ) * multiple

						if x < min_val:
									lowerCAmelCase__      :      Optional[int]         =		math.ceil(val / multiple     ) * multiple

						return x

			lowerCAmelCase__      :      List[Any]         =		(output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ) else output_size

			lowerCAmelCase__      :      Tuple         =		get_image_size(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[Any]         =		output_size

			# determine new height and width
			lowerCAmelCase__      :      Dict         =		output_height / input_height
			lowerCAmelCase__      :      Tuple         =		output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width     ) < abs(1 - scale_height     ):
									# fit width
									lowerCAmelCase__      :      Dict         =		scale_width
						else:
									# fit height
									lowerCAmelCase__      :      Dict         =		scale_height

			lowerCAmelCase__      :      List[Any]         =		constraint_to_multiple_of(scale_height * input_height       ,							multiple=SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Optional[Any]         =		constraint_to_multiple_of(scale_width * input_width       ,							multiple=SCREAMING_SNAKE_CASE_     )

			return (new_height, new_width)







class      A__							(     __magic_name__	):
				lowercase			    =  ["pixel_values"]






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






							'''simple docstring'''







							super().__init__(**snake_case__   )
							lowerCAmelCase__      :      int         =		size if size is not None else {"height": 384, "width": 384}
							lowerCAmelCase__      :      Tuple         =		get_size_dict(snake_case__   )
							lowerCAmelCase__      :      int         =		do_resize
							lowerCAmelCase__      :      Tuple         =		size
							lowerCAmelCase__      :      Dict         =		keep_aspect_ratio
							lowerCAmelCase__      :      List[Any]         =		ensure_multiple_of
							lowerCAmelCase__      :      Optional[int]         =		resample
							lowerCAmelCase__      :      Tuple         =		do_rescale
							lowerCAmelCase__      :      int         =		rescale_factor
							lowerCAmelCase__      :      List[Any]         =		do_normalize
							lowerCAmelCase__      :      str         =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCAmelCase__      :      List[str]         =		image_std if image_std is not None else IMAGENET_STANDARD_STD






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      Tuple = False    ,     a					:      List[Any] = 1    ,     a					:      List[Any] = PILImageResampling.BICUBIC    ,     a					:      Dict = None    ,     **a					:      Optional[Any]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		get_size_dict(snake_case__   )
							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()}'''   )
							lowerCAmelCase__      :      int         =		get_resize_output_image_size(
							    snake_case__    ,     output_size=(size['height'], size['width'])    ,     keep_aspect_ratio=snake_case__    ,     multiple=snake_case__    ,     )
							return resize(snake_case__    ,     size=snake_case__    ,     resample=snake_case__    ,     data_format=snake_case__    ,     **snake_case__   )






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      Any    ,     a					:      Dict    ,     a					:      Dict = None    ,     **a					:      Tuple    ,     ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      List[str]    ,     a					:      Any    ,     a					:      int    ,     a					:      Dict = None    ,     **a					:      Optional[int]    ,     ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      Optional[int]    ,     a					:      List[str] = None    ,     a					:      Dict = None    ,     a					:      str = None    ,     a					:      Union[str, Any] = None    ,     a					:      Tuple = None    ,     a					:      str = None    ,     a					:      Dict = None    ,     a					:      Any = None    ,     a					:      Union[str, Any] = None    ,     a					:      Any = None    ,     a					:      str = None    ,     a					:      Dict = ChannelDimension.FIRST    ,     **a					:      List[str]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase__      :      Dict         =		size if size is not None else self.size
							lowerCAmelCase__      :      Any         =		get_size_dict(snake_case__   )
							lowerCAmelCase__      :      Any         =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							lowerCAmelCase__      :      List[Any]         =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							lowerCAmelCase__      :      Tuple         =		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__      :      str         =		rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase__      :      Tuple         =		do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase__      :      str         =		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__      :      str         =		make_list_of_images(snake_case__   )

							if not valid_images(snake_case__   ):
										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 or resample is None:
										raise ValueError('Size and resample 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_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__      :      Optional[Any]         =		[to_numpy_array(snake_case__   ) for image in images]

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

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

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

							lowerCAmelCase__      :      Optional[Any]         =		[to_channel_dimension_format(snake_case__    ,     snake_case__   ) for image in images]

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






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[Any]    ,     a					:      Dict = None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		outputs.logits

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

										if is_torch_tensor(snake_case__   ):
													lowerCAmelCase__      :      Optional[Any]         =		target_sizes.numpy()

										lowerCAmelCase__      :      Dict         =		[]

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

							return semantic_segmentation



"""simple docstring"""







def 					a__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  if height >= 1:
    move_tower(height - 1				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    move_disk(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    move_tower(height - 1				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  print("moving disk from"				,      SCREAMING_SNAKE_CASE				,      "to"				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							int(input("Height of hanoi: "				).strip()				)
  move_tower(SCREAMING_SNAKE_CASE				,      "A"				,      "B"				,      "C"				)


if __name__ == "__main__":
     main()





