


'''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				:Union[str, Any]        =      logging.get_logger(__name__)





def lowerCamelCase      (      lowerCAmelCase    :     np.ndarray		,		lowerCAmelCase    :     Union[int, Iterable[int]]		,		lowerCAmelCase    :     bool		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	def constraint_to_multiple_of(lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]=0		,		lowerCAmelCase    :     Tuple=None		):
		__magic_name__	:   Tuple					     =					round(val / multiple		) * multiple

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

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

		return x

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

	__magic_name__     ,    __magic_name__	:   Any					     =					get_image_size(lowerCAmelCase		)
	__magic_name__     ,    __magic_name__	:   int					     =					output_size

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

	__magic_name__	:   Dict					     =					constraint_to_multiple_of(scale_height * input_height		,		multiple=lowerCAmelCase		)
	__magic_name__	:   Any					     =					constraint_to_multiple_of(scale_width * input_width		,		multiple=lowerCAmelCase		)

	return (new_height, new_width)





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    ["""pixel_values"""]







		def __init__( self		:						Tuple  ,					_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		:						Tuple  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'height': 384, 'width': 384}
			__magic_name__	:   Any					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[str]					     =					size
			__magic_name__	:   int					     =					keep_aspect_ratio
			__magic_name__	:   str					     =					ensure_multiple_of
			__magic_name__	:   Optional[int]					     =					resample
			__magic_name__	:   Optional[int]					     =					do_rescale
			__magic_name__	:   int					     =					rescale_factor
			__magic_name__	:   Optional[Any]					     =					do_normalize
			__magic_name__	:   Dict					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   List[Any]					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						bool = False  ,					_A		:						int = 1  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[Any]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[int]					     =					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()}'      )
			__magic_name__	:   int					     =					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        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Union[int, float]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[Any]  ,					)  ->			Union[str, Any]:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







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







		def        __lowerCAmelCase		( self		:						str  ,					_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		:						Optional[int]  ,					)  ->			PIL.Image.Image:
			__magic_name__	:   str					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   int					     =					size if size is not None else self.size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__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__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[int]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   str					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   List[Any]					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   List[Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   Union[str, Any]					     =					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.
			__magic_name__	:   Tuple					     =					[to_numpy_array(_A      ) for image in images]

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

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

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

			__magic_name__	:   Dict					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

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







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						List[Tuple] = None      )  ->			Optional[Any]:
			__magic_name__	:   Tuple					     =					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      ):
					__magic_name__	:   List[Any]					     =					target_sizes.numpy()

				__magic_name__	:   List[str]					     =					[]

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

			return semantic_segmentation



'''simple docstring'''

import math





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     float		):





	"""simple docstring"""







	return math.pow(lowerCAmelCase		,		2		) - a





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	return 2 * x





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					2.0

	while start <= a:
		__magic_name__	:   List[str]					     =					math.pow(lowerCAmelCase		,		2		)

	return start





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     int = 9999		,		lowerCAmelCase    :     float = 0.00_0000_0000_0001		):





	"""simple docstring"""







	if a < 0:
		raise ValueError('math domain error'		)

	__magic_name__	:   Any					     =					get_initial_point(lowerCAmelCase		)

	for _ in range(lowerCAmelCase		):
		__magic_name__	:   List[str]					     =					value
		__magic_name__	:   Optional[int]					     =					value - fx(lowerCAmelCase		,		lowerCAmelCase		) / fx_derivative(lowerCAmelCase		)
		if abs(prev_value - value		) < tolerance:
			return value

	return value


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

							testmod()