






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


SCREAMING_SNAKE_CASE__      :			int							=		logging.get_logger(__name__)

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				def constraint_to_multiple_of(snake_case,  snake_case,  snake_case=0,  snake_case=None ):
								__magic_name__		:str        =      round(val / multiple ) * multiple

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

								if x < min_val:
												__magic_name__		:Tuple        =      math.ceil(val / multiple ) * multiple

								return x

				__magic_name__		:Union[str, Any]        =      (output_size, output_size) if isinstance(snake_case,  snake_case ) else output_size

				__magic_name__     ,				__magic_name__		:List[str]        =      get_image_size(snake_case )
				__magic_name__     ,				__magic_name__		:int        =      output_size

				# determine new height and width
				__magic_name__		:Any        =      output_height / input_height
				__magic_name__		:Union[str, Any]        =      output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width ) < abs(1 - scale_height ):
												# fit width
												__magic_name__		:Dict        =      scale_width
								else:
												# fit height
												__magic_name__		:str        =      scale_height

				__magic_name__		:Optional[Any]        =      constraint_to_multiple_of(scale_height * input_height,  multiple=snake_case )
				__magic_name__		:Optional[Any]        =      constraint_to_multiple_of(scale_width * input_width,  multiple=snake_case )

				return (new_height, new_width)

class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  ['''pixel_values''']







				def __init__(						self       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = PILImageResampling.BILINEAR       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = 1 / 2_5_5       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
								__magic_name__		:List[Any]        =      get_size_dict(__lowerCAmelCase     )
								__magic_name__		:Union[str, Any]        =      do_resize
								__magic_name__		:List[Any]        =      size
								__magic_name__		:Union[str, Any]        =      keep_aspect_ratio
								__magic_name__		:int        =      ensure_multiple_of
								__magic_name__		:Any        =      resample
								__magic_name__		:str        =      do_rescale
								__magic_name__		:Optional[Any]        =      rescale_factor
								__magic_name__		:int        =      do_normalize
								__magic_name__		:str        =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__magic_name__		:Optional[int]        =      image_std if image_std is not None else IMAGENET_STANDARD_STD







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = False       ,					__lowerCAmelCase = 1       ,					__lowerCAmelCase = PILImageResampling.BICUBIC       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      get_size_dict(__lowerCAmelCase     )
								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()}'''     )
								__magic_name__		:Tuple        =      get_resize_output_image_size(
								    __lowerCAmelCase       ,					output_size=(size['''height'''], size['''width'''])       ,					keep_aspect_ratio=__lowerCAmelCase       ,					multiple=__lowerCAmelCase       ,					)
								return resize(__lowerCAmelCase       ,					size=__lowerCAmelCase       ,					resample=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								return rescale(__lowerCAmelCase       ,					scale=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								return normalize(__lowerCAmelCase       ,					mean=__lowerCAmelCase       ,					std=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = ChannelDimension.FIRST       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:Tuple        =      do_resize if do_resize is not None else self.do_resize
								__magic_name__		:str        =      size if size is not None else self.size
								__magic_name__		:int        =      get_size_dict(__lowerCAmelCase     )
								__magic_name__		:List[str]        =      keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								__magic_name__		:Union[str, Any]        =      ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								__magic_name__		:Tuple        =      resample if resample is not None else self.resample
								__magic_name__		:Dict        =      do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__		:int        =      rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__		:List[str]        =      do_normalize if do_normalize is not None else self.do_normalize
								__magic_name__		:Optional[int]        =      image_mean if image_mean is not None else self.image_mean
								__magic_name__		:int        =      image_std if image_std is not None else self.image_std

								__magic_name__		:Union[str, Any]        =      make_list_of_images(__lowerCAmelCase     )

								if not valid_images(__lowerCAmelCase     ):
												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.
								__magic_name__		:int        =      [to_numpy_array(__lowerCAmelCase     ) for image in images]

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

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

								if do_normalize:
												__magic_name__		:Union[str, Any]        =      [self.normalize(image=__lowerCAmelCase       ,					mean=__lowerCAmelCase       ,					std=__lowerCAmelCase     ) for image in images]

								__magic_name__		:str        =      [to_channel_dimension_format(__lowerCAmelCase       ,					__lowerCAmelCase     ) for image in images]

								__magic_name__		:Optional[Any]        =      {'''pixel_values''': images}
								return BatchFeature(data=__lowerCAmelCase       ,					tensor_type=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      outputs.logits

								# Resize logits and compute semantic segmentation maps
								if target_sizes is not None:
												if len(__lowerCAmelCase     ) != len(__lowerCAmelCase     ):
																raise ValueError(
																    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''     )

												if is_torch_tensor(__lowerCAmelCase     ):
																__magic_name__		:Optional[int]        =      target_sizes.numpy()

												__magic_name__		:List[str]        =      []

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

								return semantic_segmentation














from __future__ import annotations

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      0
				__magic_name__		:Tuple        =      len(snake_case ) - 1

				while i < j:
								if nums[i] + nums[j] == target:
												return [i, j]
								elif nums[i] + nums[j] < target:
												__magic_name__		:str        =      i + 1
								else:
												__magic_name__		:List[Any]        =      j - 1

				return []


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(f"{two_pointer([2, 7, 11, 15], 9) = }")






