"""simple docstring"""






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
						import torch


lowerCAmelCase__  :  List[str]						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["pixel_values"]







					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Dict[str, int]] = None    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BILINEAR    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Union[int, float] = 1 / 255    ,lowerCamelCase__   :	bool = True    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,**lowerCamelCase__   :	List[str]    ,):
									super().__init__(**lowerCamelCase__			)
									UpperCAmelCase__             =							size if size is not None else {'shortest_edge': 256}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									UpperCAmelCase__             =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,param_name='crop_size'			)
									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									UpperCAmelCase__             =							resample
									UpperCAmelCase__             =							do_center_crop
									UpperCAmelCase__             =							crop_size
									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 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	PILImageResampling = PILImageResampling.BICUBIC    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	Tuple    ,):
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									if "shortest_edge" not in size:
													raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
									UpperCAmelCase__             =							get_resize_output_image_size(lowerCamelCase__    ,size=size['shortest_edge']    ,default_to_square=lowerCamelCase__			)
									return resize(lowerCamelCase__    ,size=lowerCamelCase__    ,resample=lowerCamelCase__    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	np.ndarray    ,lowerCamelCase__   :	Dict[str, int]    ,lowerCamelCase__   :	Optional[Union[str, ChannelDimension]] = None    ,**lowerCamelCase__   :	Tuple    ,):
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__			)
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''			)
									return center_crop(lowerCamelCase__    ,size=(size['height'], size['width'])    ,data_format=lowerCamelCase__    ,**lowerCamelCase__			)







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







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







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	ImageInput    ,lowerCamelCase__   :	Optional[bool] = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	PILImageResampling = None    ,lowerCamelCase__   :	bool = None    ,lowerCamelCase__   :	Dict[str, int] = None    ,lowerCamelCase__   :	Optional[bool] = None    ,lowerCamelCase__   :	Optional[float] = None    ,lowerCamelCase__   :	Optional[bool] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[float, List[float]]] = None    ,lowerCamelCase__   :	Optional[Union[str, TensorType]] = None    ,lowerCamelCase__   :	Union[str, ChannelDimension] = ChannelDimension.FIRST    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									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(lowerCamelCase__    ,default_to_square=lowerCamelCase__			)
									UpperCAmelCase__             =							resample if resample is not None else self.resample
									UpperCAmelCase__             =							do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__             =							crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__             =							get_size_dict(lowerCamelCase__    ,param_name='crop_size'			)
									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(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:
													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.'			)

									# All transformations expect numpy arrays.
									UpperCAmelCase__             =							[to_numpy_array(lowerCamelCase__			) for image in images]

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

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

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

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

									UpperCAmelCase__             =							[to_channel_dimension_format(lowerCamelCase__    ,lowerCamelCase__			) for image in images]

									UpperCAmelCase__             =							{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__    ,tensor_type=lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	List[Tuple] = None			):
									UpperCAmelCase__             =							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__			):
																	UpperCAmelCase__             =							target_sizes.numpy()

													UpperCAmelCase__             =							[]

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

									return semantic_segmentation




"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if a < 0 or b < 0:
								raise ValueError('the value of both inputs must be positive'       )

				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"
				UpperCAmelCase__             =							str(bin(lowerCamelCase       )       )[2:]  # remove the leading "0b"

				UpperCAmelCase__             =							max(len(lowerCamelCase       )					,			len(lowerCamelCase       )       )

				return "0b" + "".join(
				    str(int(char_a == '1' and char_b == '1'       )       )
				    for char_a, char_b in zip(a_binary.zfill(lowerCamelCase       )					,			b_binary.zfill(lowerCamelCase       )       )       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



