

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


a		=logging.get_logger(__name__)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Tuple[int, int]:
	def constraint_to_multiple_of(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=0	, lowerCamelCase__=None			):
		__lowerCamelCase				:				Dict					       = round(val / multiple			) * multiple

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

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

		return x

	__lowerCamelCase				:				Tuple					       = (output_size, output_size) if isinstance(lowerCamelCase__	, lowerCamelCase__			) else output_size

	__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = get_image_size(lowerCamelCase__			)
	__lowerCamelCase       , __lowerCamelCase				:				Tuple					       = output_size

	# determine new height and width
	__lowerCamelCase				:				Any					       = output_height / input_height
	__lowerCamelCase				:				int					       = 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				:				str					       = scale_width
		else:
			# fit height
			__lowerCamelCase				:				Optional[int]					       = scale_height

	__lowerCamelCase				:				Union[str, Any]					       = constraint_to_multiple_of(scale_height * input_height	, multiple=lowerCamelCase__			)
	__lowerCamelCase				:				Any					       = constraint_to_multiple_of(scale_width * input_width	, multiple=lowerCamelCase__			)

	return (new_height, new_width)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  str						      =      ['''pixel_values''']
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BILINEAR   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			int = 1   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Union[int, float] = 1 / 2_5_5   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[str]   ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Dict					       = size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
								__lowerCamelCase				:				str					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = do_resize
								__lowerCamelCase				:				Optional[Any]					       = size
								__lowerCamelCase				:				Optional[Any]					       = keep_aspect_ratio
								__lowerCamelCase				:				List[Any]					       = ensure_multiple_of
								__lowerCamelCase				:				Any					       = resample
								__lowerCamelCase				:				Union[str, Any]					       = do_rescale
								__lowerCamelCase				:				Tuple					       = rescale_factor
								__lowerCamelCase				:				List[Any]					       = do_normalize
								__lowerCamelCase				:				Optional[Any]					       = 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		:			Any   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			bool = False   ,SCREAMING_SNAKE_CASE__		:			int = 1   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Any   ,):
								__lowerCamelCase				:				Tuple					       = get_size_dict(SCREAMING_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				:				Tuple					       = get_resize_output_image_size(
								    SCREAMING_SNAKE_CASE__   ,output_size=(size['height'], size['width'])   ,keep_aspect_ratio=SCREAMING_SNAKE_CASE__   ,multiple=SCREAMING_SNAKE_CASE__   ,)
								return resize(SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,resample=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[int, float]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Optional[int]   ,):
								return rescale(SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			str   ,):
								return normalize(SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			ImageInput   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			int = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			int = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			float = None   ,SCREAMING_SNAKE_CASE__		:			bool = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, List[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, TensorType]] = None   ,SCREAMING_SNAKE_CASE__		:			ChannelDimension = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,):
								__lowerCamelCase				:				List[Any]					       = do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase				:				Tuple					       = size if size is not None else self.size
								__lowerCamelCase				:				Dict					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								__lowerCamelCase				:				Dict					       = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								__lowerCamelCase				:				Optional[int]					       = 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				:				int					       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase				:				int					       = do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase				:				List[Any]					       = image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase				:				Dict					       = image_std if image_std is not None else self.image_std

								__lowerCamelCase				:				Tuple					       = make_list_of_images(SCREAMING_SNAKE_CASE__)

								if not valid_images(SCREAMING_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				:				Dict					       = [to_numpy_array(SCREAMING_SNAKE_CASE__) for image in images]

								if do_resize:
									__lowerCamelCase				:				Union[str, Any]					       = [self.resize(image=SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,resample=SCREAMING_SNAKE_CASE__) for image in images]

								if do_rescale:
									__lowerCamelCase				:				Union[str, Any]					       = [self.rescale(image=SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase				:				Dict					       = [self.normalize(image=SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Optional[int]					       = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Union[str, Any]					       = {'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__   ,tensor_type=SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			List[Tuple] = None):
								__lowerCamelCase				:				Union[str, Any]					       = outputs.logits

								# Resize logits and compute semantic segmentation maps
								if target_sizes is not None:
									if len(SCREAMING_SNAKE_CASE__) != len(SCREAMING_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(SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				List[str]					       = target_sizes.numpy()

									__lowerCamelCase				:				Any					       = []

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

								return semantic_segmentation











from math import loga






def lowerCamelCase__    (							snake_case_	:  int       )      ->				int:
   if a < 0:
      raise ValueError('''Input value must be a positive integer'''       )
   elif isinstance(snake_case_       ,      snake_case_       ):
      raise TypeError('''Input value must be a \'int\' type'''       )
   return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

