



'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCamelCase											=			logging.get_logger(__name__)


if is_vision_available():
					import PIL



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Tuple						      =			["pixel_values"]





		def __init__(     self ,       __UpperCAmelCase = True ,       __UpperCAmelCase = None ,       __UpperCAmelCase = PILImageResampling.BICUBIC ,       __UpperCAmelCase = True ,       __UpperCAmelCase = None ,       __UpperCAmelCase = True ,       __UpperCAmelCase = 1 / 255 ,       __UpperCAmelCase = True ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = True ,       **__UpperCAmelCase ,       )       ->     None:

					'''simple docstring'''
					super().__init__(**__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     size if size is not None else {"""shortest_edge""": 224}
					__UpperCAmelCase   :      Union[str, Any]										=     get_size_dict(__UpperCAmelCase ,       default_to_square=__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
					__UpperCAmelCase   :      Tuple										=     get_size_dict(__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     do_resize
					__UpperCAmelCase   :      Union[str, Any]										=     size
					__UpperCAmelCase   :      int										=     resample
					__UpperCAmelCase   :      Any										=     do_center_crop
					__UpperCAmelCase   :      int										=     crop_size
					__UpperCAmelCase   :      List[str]										=     do_rescale
					__UpperCAmelCase   :      List[Any]										=     rescale_factor
					__UpperCAmelCase   :      Any										=     do_normalize
					__UpperCAmelCase   :      Any										=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					__UpperCAmelCase   :      int										=     image_std if image_std is not None else OPENAI_CLIP_STD
					__UpperCAmelCase   :      List[Any]										=     do_convert_rgb





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase = PILImageResampling.BICUBIC ,       __UpperCAmelCase = None ,       **__UpperCAmelCase ,       )       ->     np.ndarray:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     get_size_dict(__UpperCAmelCase ,       default_to_square=__UpperCAmelCase				)
					__UpperCAmelCase   :      Dict										=     get_resize_output_image_size(
					    __UpperCAmelCase ,       size=(size["""height"""], size["""width"""]) ,       default_to_square=__UpperCAmelCase				)
					return resize(__UpperCAmelCase ,       size=__UpperCAmelCase ,       resample=__UpperCAmelCase ,       data_format=__UpperCAmelCase ,       **__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase = None ,       **__UpperCAmelCase ,       )       ->     np.ndarray:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     get_size_dict(__UpperCAmelCase				)
					return center_crop(__UpperCAmelCase ,       size=(size["""height"""], size["""width"""]) ,       data_format=__UpperCAmelCase ,       **__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase = None ,       **__UpperCAmelCase ,       )       ->     int:

					'''simple docstring'''
					return rescale(__UpperCAmelCase ,       scale=__UpperCAmelCase ,       data_format=__UpperCAmelCase ,       **__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase = None ,       **__UpperCAmelCase ,       )       ->     np.ndarray:

					'''simple docstring'''
					return normalize(__UpperCAmelCase ,       mean=__UpperCAmelCase ,       std=__UpperCAmelCase ,       data_format=__UpperCAmelCase ,       **__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = None ,       __UpperCAmelCase = ChannelDimension.FIRST ,       **__UpperCAmelCase ,       )       ->     PIL.Image.Image:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     do_resize if do_resize is not None else self.do_resize
					__UpperCAmelCase   :      str										=     size if size is not None else self.size
					__UpperCAmelCase   :      Dict										=     get_size_dict(__UpperCAmelCase ,       default_to_square=__UpperCAmelCase				)
					__UpperCAmelCase   :      int										=     resample if resample is not None else self.resample
					__UpperCAmelCase   :      Any										=     do_center_crop if do_center_crop is not None else self.do_center_crop
					__UpperCAmelCase   :      Any										=     crop_size if crop_size is not None else self.crop_size
					__UpperCAmelCase   :      Optional[Any]										=     get_size_dict(__UpperCAmelCase				)
					__UpperCAmelCase   :      Optional[Any]										=     do_rescale if do_rescale is not None else self.do_rescale
					__UpperCAmelCase   :      Any										=     rescale_factor if rescale_factor is not None else self.rescale_factor
					__UpperCAmelCase   :      Dict										=     do_normalize if do_normalize is not None else self.do_normalize
					__UpperCAmelCase   :      List[Any]										=     image_mean if image_mean is not None else self.image_mean
					__UpperCAmelCase   :      str										=     image_std if image_std is not None else self.image_std
					__UpperCAmelCase   :      Optional[int]										=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					__UpperCAmelCase   :      Optional[int]										=     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_center_crop and crop_size is None:
								raise ValueError("""Crop size must be specified if do_center_crop 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."""				)

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
								__UpperCAmelCase   :      List[Any]										=     [convert_to_rgb(__UpperCAmelCase				) for image in images]

					# All transformations expect numpy arrays.
					__UpperCAmelCase   :      Tuple										=     [to_numpy_array(__UpperCAmelCase				) for image in images]

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

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

					if do_rescale:
								__UpperCAmelCase   :      Optional[Any]										=     [self.rescale(image=__UpperCAmelCase ,       scale=__UpperCAmelCase				) for image in images]

					if do_normalize:
								__UpperCAmelCase   :      List[str]										=     [self.normalize(image=__UpperCAmelCase ,       mean=__UpperCAmelCase ,       std=__UpperCAmelCase				) for image in images]

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

					__UpperCAmelCase   :      Dict										=     {"""pixel_values""": images}
					return BatchFeature(data=__UpperCAmelCase ,       tensor_type=__UpperCAmelCase				)









'''simple docstring'''
from itertools import permutations


def   lowercase_	(   lowerCAmelCase__	:  int = 5 ):

			"""simple docstring"""





			if gon_side < 3 or gon_side > 5:
						raise ValueError("""gon_side must be in the range [3, 5]""" )

			# Since it's 16, we know 10 is on the outer ring
			# Put the big numbers at the end so that they are never the first number
			__UpperCAmelCase   :      int										=     list(range(gon_side + 1			,							0			,							-1 ) )
			__UpperCAmelCase   :      int										=     list(range(gon_side + 2			,							gon_side * 2 + 1 ) )

			for perm in permutations(small_numbers + big_numbers ):
						__UpperCAmelCase   :      Optional[int]										=     generate_gon_ring(lowerCAmelCase__			,							list(lowerCAmelCase__ ) )
						if is_magic_gon(lowerCAmelCase__ ):
									return int("""""".join(str(lowerCAmelCase__ ) for n in numbers ) )

			__UpperCAmelCase   :      Optional[int]										=     f'Magic {gon_side}-gon ring is impossible'
			raise ValueError(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     [0] * (gon_side * 3)
			__UpperCAmelCase   :      Optional[Any]										=     perm[0:3]
			perm.append(perm[1] )

			__UpperCAmelCase   :      Union[str, Any]										=     1 if gon_side < 5 else 2

			for i in range(1			,							len(lowerCAmelCase__ ) // 3 + magic_number ):
						__UpperCAmelCase   :      List[str]										=     perm[2 * i + 1]
						__UpperCAmelCase   :      Optional[int]										=     result[3 * i - 1]
						__UpperCAmelCase   :      Optional[int]										=     perm[2 * i + 2]

			return result


def   lowercase_	(   lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			if len(lowerCAmelCase__ ) % 3 != 0:
						raise ValueError("""a gon ring should have a length that is a multiple of 3""" )

			if min(numbers[::3] ) != numbers[0]:
						return False

			__UpperCAmelCase   :      Optional[int]										=     sum(numbers[:3] )

			return all(sum(numbers[i : i + 3] ) == total for i in range(3			,							len(lowerCAmelCase__ )			,							3 ) )


if __name__ == "__main__":
					print(solution())




