


'''simple docstring'''



from typing import Dict, List, Optional, 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_vision_available, logging


if is_vision_available():
		import PIL


__A	=logging.get_logger(__name__)


class 		_snake_case					(     __UpperCAmelCase	):
					lowerCAmelCase      :str   =					["""pixel_values"""]




					def __init__(      self							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = PILImageResampling.BILINEAR							,							_lowerCamelCase = True							,							_lowerCamelCase = 1 / 255							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								super().__init__(**UpperCAmelCase_)
								UpperCAmelCase__  :					str        =     size if size is not None else {"""shortest_edge""": 384}
								UpperCAmelCase__  :					Tuple        =     get_size_dict(UpperCAmelCase_							,							default_to_square=UpperCAmelCase_)

								UpperCAmelCase__  :					Tuple        =     do_resize
								UpperCAmelCase__  :					int        =     size
								# Default value set here for backwards compatibility where the value in config is None
								UpperCAmelCase__  :					List[Any]        =     crop_pct if crop_pct is not None else 224 / 256
								UpperCAmelCase__  :					Optional[Any]        =     resample
								UpperCAmelCase__  :					Dict        =     do_rescale
								UpperCAmelCase__  :					int        =     rescale_factor
								UpperCAmelCase__  :					Dict        =     do_normalize
								UpperCAmelCase__  :					List[str]        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase__  :					Optional[Any]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					List[Any]        =     get_size_dict(UpperCAmelCase_							,							default_to_square=UpperCAmelCase_)
								if "shortest_edge" not in size:
											raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''')
								UpperCAmelCase__  :					List[str]        =     size["""shortest_edge"""]

								if shortest_edge < 384:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											UpperCAmelCase__  :					Tuple        =     int(shortest_edge / crop_pct)
											UpperCAmelCase__  :					Any        =     get_resize_output_image_size(UpperCAmelCase_							,							size=UpperCAmelCase_							,							default_to_square=UpperCAmelCase_)
											UpperCAmelCase__  :					Optional[int]        =     resize(image=UpperCAmelCase_							,							size=UpperCAmelCase_							,							resample=UpperCAmelCase_							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=UpperCAmelCase_							,							size=(shortest_edge, shortest_edge)							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    UpperCAmelCase_							,							size=(shortest_edge, shortest_edge)							,							resample=UpperCAmelCase_							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return rescale(UpperCAmelCase_							,							scale=UpperCAmelCase_							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return normalize(UpperCAmelCase_							,							mean=UpperCAmelCase_							,							std=UpperCAmelCase_							,							data_format=UpperCAmelCase_							,							**UpperCAmelCase_)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = ChannelDimension.FIRST							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Union[str, Any]        =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase__  :					Dict        =     crop_pct if crop_pct is not None else self.crop_pct
								UpperCAmelCase__  :					Union[str, Any]        =     resample if resample is not None else self.resample
								UpperCAmelCase__  :					Union[str, Any]        =     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__  :					Any        =     do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase__  :					Any        =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase__  :					Any        =     image_std if image_std is not None else self.image_std

								UpperCAmelCase__  :					List[Any]        =     size if size is not None else self.size
								UpperCAmelCase__  :					List[Any]        =     get_size_dict(UpperCAmelCase_							,							default_to_square=UpperCAmelCase_)

								UpperCAmelCase__  :					List[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 or resample is None:
											raise ValueError("""Size and resample must be specified if do_resize is True.""")

								if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
											raise ValueError("""crop_pct must be specified if size < 384.""")

								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.
								UpperCAmelCase__  :					int        =     [to_numpy_array(UpperCAmelCase_) for image in images]

								if do_resize:
											UpperCAmelCase__  :					Optional[Any]        =     [self.resize(image=UpperCAmelCase_							,							size=UpperCAmelCase_							,							crop_pct=UpperCAmelCase_							,							resample=UpperCAmelCase_) for image in images]

								if do_rescale:
											UpperCAmelCase__  :					Tuple        =     [self.rescale(image=UpperCAmelCase_							,							scale=UpperCAmelCase_) for image in images]

								if do_normalize:
											UpperCAmelCase__  :					Any        =     [self.normalize(image=UpperCAmelCase_							,							mean=UpperCAmelCase_							,							std=UpperCAmelCase_) for image in images]

								UpperCAmelCase__  :					List[Any]        =     [to_channel_dimension_format(UpperCAmelCase_							,							UpperCAmelCase_) for image in images]

								UpperCAmelCase__  :					str        =     {"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_							,							tensor_type=UpperCAmelCase_)



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()