
"""simple docstring"""







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    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	def constraint_to_multiple_of(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=0	,  _UpperCamelCase=None		):
		__lowerCAmelCase      :						Optional[int]						      =				round(val / multiple		) * multiple

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

		if x < min_val:
			__lowerCAmelCase      :						str						      =				math.ceil(val / multiple		) * multiple

		return x

	__lowerCAmelCase      :						Any						      =				(output_size, output_size) if isinstance(lowerCAmelCase__	,  lowerCAmelCase__		) else output_size

	__lowerCAmelCase      :						List[Any]						      =				get_image_size(lowerCAmelCase__		)
	__lowerCAmelCase      :						str						      =				output_size

	# determine new height and width
	__lowerCAmelCase      :						str						      =				output_height / input_height
	__lowerCAmelCase      :						Dict						      =				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      :						List[str]						      =				scale_width
		else:
			# fit height
			__lowerCAmelCase      :						Dict						      =				scale_height

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

	return (new_height, new_width)




class 			A__	(     __lowercase):
		A_						:	str           =     ["pixel_values"]

		def __init__(				self	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = 1 / 2_55	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(**snake_case_			)
			__lowerCAmelCase      :						str						      =				size if size is not None else {'''height''': 3_84, '''width''': 3_84}
			__lowerCAmelCase      :						Dict						      =				get_size_dict(snake_case_			)
			__lowerCAmelCase      :						Optional[int]						      =				do_resize
			__lowerCAmelCase      :						Dict						      =				size
			__lowerCAmelCase      :						Union[str, Any]						      =				keep_aspect_ratio
			__lowerCAmelCase      :						Optional[Any]						      =				ensure_multiple_of
			__lowerCAmelCase      :						Union[str, Any]						      =				resample
			__lowerCAmelCase      :						List[Any]						      =				do_rescale
			__lowerCAmelCase      :						Optional[Any]						      =				rescale_factor
			__lowerCAmelCase      :						List[str]						      =				do_normalize
			__lowerCAmelCase      :						Tuple						      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__lowerCAmelCase      :						Optional[int]						      =				image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = False	,		_SCREAMING_SNAKE_CASE = 1	,		_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						Union[str, Any]						      =				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      :						Optional[Any]						      =				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	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			return rescale(snake_case_	,		scale=snake_case_	,		data_format=snake_case_	,		**snake_case_			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			return normalize(snake_case_	,		mean=snake_case_	,		std=snake_case_	,		data_format=snake_case_	,		**snake_case_			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,		**_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						Optional[Any]						      =				do_resize if do_resize is not None else self.do_resize
			__lowerCAmelCase      :						Optional[Any]						      =				size if size is not None else self.size
			__lowerCAmelCase      :						Union[str, Any]						      =				get_size_dict(snake_case_			)
			__lowerCAmelCase      :						Union[str, Any]						      =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
			__lowerCAmelCase      :						Optional[int]						      =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
			__lowerCAmelCase      :						Any						      =				resample if resample is not None else self.resample
			__lowerCAmelCase      :						Optional[Any]						      =				do_rescale if do_rescale is not None else self.do_rescale
			__lowerCAmelCase      :						Dict						      =				rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowerCAmelCase      :						Optional[int]						      =				do_normalize if do_normalize is not None else self.do_normalize
			__lowerCAmelCase      :						Any						      =				image_mean if image_mean is not None else self.image_mean
			__lowerCAmelCase      :						str						      =				image_std if image_std is not None else self.image_std

			__lowerCAmelCase      :						List[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      :						Union[str, Any]						      =				[to_numpy_array(snake_case_			) for image in images]

			if do_resize:
				__lowerCAmelCase      :						str						      =				[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      :						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      :						Union[str, Any]						      =				{'''pixel_values''': images}
			return BatchFeature(data=snake_case_	,		tensor_type=snake_case_			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						List[str]						      =				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      :						Tuple						      =				target_sizes.numpy()

				__lowerCAmelCase      :						Union[str, Any]						      =				[]

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

			return semantic_segmentation

"""simple docstring"""







from __future__ import annotations
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				str(_UpperCamelCase		)
	return len(_UpperCamelCase		) == 9 and set(_UpperCamelCase		) == set('123456789'		)
def    __lowerCAmelCase    ():
	for base_num in range(9999	,  4999	,  -1		):
		__lowerCAmelCase      :						Union[str, Any]						      =				10_0002 * base_num
		if is_9_pandigital(_UpperCamelCase		):
			return candidate

	for base_num in range(333	,  99	,  -1		):
		__lowerCAmelCase      :						Dict						      =				100_2003 * base_num
		if is_9_pandigital(_UpperCamelCase		):
			return candidate

	return None


if __name__ == "__main__":
							print(f'{solution() = }')