def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     int(__snake_case						)
					if decimal in (0, 1):  # Exit cases for the recursion
										return str(__snake_case						)
					_UpperCamelCase     ,    _UpperCamelCase			     =     divmod(__snake_case  ,     2						)
					return binary_recursive(__snake_case						) + str(__snake_case						)
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     str(__snake_case						).strip()
					if not number:
										raise ValueError('''No input value was provided'''						)
					_UpperCamelCase			     =     '''-''' if number.startswith('''-'''						) else ''''''
					_UpperCamelCase			     =     number.lstrip('''-'''						)
					if not number.isnumeric():
										raise ValueError('''Input value is not an integer'''						)
					return f"""{negative}0b{binary_recursive(int(__snake_case						)						)}"""


if __name__ == "__main__":
					from doctest import testmod

					testmod()

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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					def constraint_to_multiple_of(__snake_case  ,     __snake_case  ,     __snake_case=0  ,     __snake_case=None						):
										_UpperCamelCase			     =     round(val / multiple						) * multiple

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

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

										return x

					_UpperCamelCase			     =     (output_size, output_size) if isinstance(__snake_case  ,     __snake_case						) else output_size

					_UpperCamelCase     ,    _UpperCamelCase			     =     get_image_size(__snake_case						)
					_UpperCamelCase     ,    _UpperCamelCase			     =     output_size

					# determine new height and width
					_UpperCamelCase			     =     output_height / input_height
					_UpperCamelCase			     =     output_width / input_width

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

					_UpperCamelCase			     =     constraint_to_multiple_of(scale_height * input_height  ,     multiple=__snake_case						)
					_UpperCamelCase			     =     constraint_to_multiple_of(scale_width * input_width  ,     multiple=__snake_case						)

					return (new_height, new_width)







class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     ["pixel_values"]







							def __init__(   self      :     List[Any] ,			_A      :     bool = True ,			_A      :     Dict[str, int] = None ,			_A      :     PILImageResampling = PILImageResampling.BILINEAR ,			_A      :     bool = False ,			_A      :     int = 1 ,			_A      :     bool = True ,			_A      :     Union[int, float] = 1 / 255 ,			_A      :     bool = True ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			**_A      :     List[str] ,			):
												super().__init__(**_A		)
												_UpperCamelCase			     =     size if size is not None else {'''height''': 384, '''width''': 384}
												_UpperCamelCase			     =     get_size_dict(_A		)
												_UpperCamelCase			     =     do_resize
												_UpperCamelCase			     =     size
												_UpperCamelCase			     =     keep_aspect_ratio
												_UpperCamelCase			     =     ensure_multiple_of
												_UpperCamelCase			     =     resample
												_UpperCamelCase			     =     do_rescale
												_UpperCamelCase			     =     rescale_factor
												_UpperCamelCase			     =     do_normalize
												_UpperCamelCase			     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_UpperCamelCase			     =     image_std if image_std is not None else IMAGENET_STANDARD_STD







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     np.ndarray ,			_A      :     Dict[str, int] ,			_A      :     bool = False ,			_A      :     int = 1 ,			_A      :     PILImageResampling = PILImageResampling.BICUBIC ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												_UpperCamelCase			     =     get_size_dict(_A		)
												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()}"""		)
												_UpperCamelCase			     =     get_resize_output_image_size(
												    _A ,			output_size=(size['''height'''], size['''width''']) ,			keep_aspect_ratio=_A ,			multiple=_A ,			)
												return resize(_A ,			size=_A ,			resample=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     np.ndarray ,			_A      :     Union[int, float] ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												return rescale(_A ,			scale=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     np.ndarray ,			_A      :     Union[float, List[float]] ,			_A      :     Union[float, List[float]] ,			_A      :     Optional[Union[str, ChannelDimension]] = None ,			**_A      :     Any ,			):
												return normalize(_A ,			mean=_A ,			std=_A ,			data_format=_A ,			**_A		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     ImageInput ,			_A      :     bool = None ,			_A      :     int = None ,			_A      :     bool = None ,			_A      :     int = None ,			_A      :     PILImageResampling = None ,			_A      :     bool = None ,			_A      :     float = None ,			_A      :     bool = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[float, List[float]]] = None ,			_A      :     Optional[Union[str, TensorType]] = None ,			_A      :     ChannelDimension = ChannelDimension.FIRST ,			**_A      :     str ,			):
												_UpperCamelCase			     =     do_resize if do_resize is not None else self.do_resize
												_UpperCamelCase			     =     size if size is not None else self.size
												_UpperCamelCase			     =     get_size_dict(_A		)
												_UpperCamelCase			     =     keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_UpperCamelCase			     =     ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_UpperCamelCase			     =     resample if resample is not None else self.resample
												_UpperCamelCase			     =     do_rescale if do_rescale is not None else self.do_rescale
												_UpperCamelCase			     =     rescale_factor if rescale_factor is not None else self.rescale_factor
												_UpperCamelCase			     =     do_normalize if do_normalize is not None else self.do_normalize
												_UpperCamelCase			     =     image_mean if image_mean is not None else self.image_mean
												_UpperCamelCase			     =     image_std if image_std is not None else self.image_std

												_UpperCamelCase			     =     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_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.
												_UpperCamelCase			     =     [to_numpy_array(_A		) for image in images]

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

												if do_rescale:
																	_UpperCamelCase			     =     [self.rescale(image=_A ,			scale=_A		) for image in images]

												if do_normalize:
																	_UpperCamelCase			     =     [self.normalize(image=_A ,			mean=_A ,			std=_A		) for image in images]

												_UpperCamelCase			     =     [to_channel_dimension_format(_A ,			_A		) for image in images]

												_UpperCamelCase			     =     {'''pixel_values''': images}
												return BatchFeature(data=_A ,			tensor_type=_A		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     Any ,			_A      :     List[Tuple] = None		):
												_UpperCamelCase			     =     outputs.logits

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

																	if is_torch_tensor(_A		):
																						_UpperCamelCase			     =     target_sizes.numpy()

																	_UpperCamelCase			     =     []

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

												return semantic_segmentation
