



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 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_vision_available, logging


if is_vision_available():
   import PIL


_lowercase						  =		logging.get_logger(__name__)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       Union[str, Any]	)->  Dict:
    A__		 =					b.T
    A__		 =					np.sum(np.square(UpperCamelCase_	) ,       axis=1	)
    A__		 =					np.sum(np.square(UpperCamelCase_	) ,       axis=0	)
    A__		 =					np.matmul(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					aa[:, None] - 2 * ab + ba[None, :]
    return d



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       str	)->  Any:
    A__		 =					x.reshape(-1 ,       3	)
    A__		 =					squared_euclidean_distance(UpperCamelCase_ ,       UpperCamelCase_	)
    return np.argmin(UpperCamelCase_ ,       axis=1	)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				['''pixel_values''']




     def __init__(				self ,						a__ = None ,						a__ = True ,						a__ = None ,						a__ = PILImageResampling.BILINEAR ,						a__ = True ,						a__ = True ,						**a__ ,						):
         super().__init__(**a__)
         A__		 =					size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
         A__		 =					get_size_dict(a__)
         A__		 =					np.array(a__) if clusters is not None else None
         A__		 =					do_resize
         A__		 =					size
         A__		 =					resample
         A__		 =					do_normalize
         A__		 =					do_color_quantize




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ = PILImageResampling.BILINEAR ,						a__ = None ,						**a__ ,						):
         A__		 =					get_size_dict(a__)
         if "height" not in size or "width" not in size:
             raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}")
         return resize(
             a__ ,						size=(size['''height'''], size['''width''']) ,						resample=a__ ,						data_format=a__ ,						**a__)




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						):
         A__		 =					rescale(image=a__ ,						scale=1 / 1_2_7.5 ,						data_format=a__)
         A__		 =					image - 1
         return image




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = ChannelDimension.FIRST ,						**a__ ,						):
         A__		 =					do_resize if do_resize is not None else self.do_resize
         A__		 =					size if size is not None else self.size
         A__		 =					get_size_dict(a__)
         A__		 =					resample if resample is not None else self.resample
         A__		 =					do_normalize if do_normalize is not None else self.do_normalize
         A__		 =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         A__		 =					clusters if clusters is not None else self.clusters
         A__		 =					np.array(a__)

         A__		 =					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 or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.''')

         if do_color_quantize and clusters is None:
             raise ValueError('''Clusters must be specified if do_color_quantize is True.''')

         # All transformations expect numpy arrays.
         A__		 =					[to_numpy_array(a__) for image in images]

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

         if do_normalize:
             A__		 =					[self.normalize(image=a__) for image in images]

         if do_color_quantize:
             A__		 =					[to_channel_dimension_format(a__ ,						ChannelDimension.LAST) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             A__		 =					np.array(a__)
             A__		 =					color_quantize(a__ ,						a__).reshape(images.shape[:-1])

             # flatten to (batch_size, height*width)
             A__		 =					images.shape[0]
             A__		 =					images.reshape(a__ ,						-1)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             A__		 =					list(a__)
         else:
             A__		 =					[to_channel_dimension_format(a__ ,						a__) for image in images]

         A__		 =					{'''input_ids''': images}
         return BatchFeature(data=a__ ,						tensor_type=a__)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       List[str] ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       str	)->  int:
    if height >= 1:
        move_tower(height - 1 ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)
        move_disk(UpperCamelCase_ ,       UpperCamelCase_	)
        move_tower(height - 1 ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       List[Any]	)->  str:
    print('''moving disk from''' ,       UpperCamelCase_ ,       '''to''' ,       UpperCamelCase_	)



def 		lowerCAmelCase__    (					)->  Tuple:
    A__		 =					int(input('''Height of hanoi: '''	).strip()	)
    move_tower(UpperCamelCase_ ,       '''A''' ,       '''B''' ,       '''C'''	)


if __name__ == "__main__":
   main()
