
'''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


_lowercase          =						logging.get_logger(__name__)


def A (__lowerCamelCase :np.ndarray					,      __lowerCamelCase :Union[int, Iterable[int]]					,      __lowerCamelCase :bool					,      __lowerCamelCase :int						):
						def constraint_to_multiple_of(__lowerCamelCase :List[str]					,      __lowerCamelCase :str					,      __lowerCamelCase :Optional[int]=0					,      __lowerCamelCase :Any=None						):
												_lowerCAmelCase    		=					round(val / multiple						) * multiple

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

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

												return x

						_lowerCAmelCase    		=					(output_size, output_size) if isinstance(__lowerCamelCase					,      __lowerCamelCase						) else output_size

						_lowerCAmelCase		,       _lowerCAmelCase    		=					get_image_size(__lowerCamelCase						)
						_lowerCAmelCase		,       _lowerCAmelCase    		=					output_size

						# determine new height and width
						_lowerCAmelCase    		=					output_height / input_height
						_lowerCAmelCase    		=					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    		=					scale_width
												else:
																		# fit height
																		_lowerCAmelCase    		=					scale_height

						_lowerCAmelCase    		=					constraint_to_multiple_of(scale_height * input_height					,      multiple=__lowerCamelCase						)
						_lowerCAmelCase    		=					constraint_to_multiple_of(scale_width * input_width					,      multiple=__lowerCamelCase						)

						return (new_height, new_width)





class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''pixel_values''']




						def __init__(   self       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = PILImageResampling.BILINEAR       ,  _lowercase = False       ,  _lowercase = 1       ,  _lowercase = True       ,  _lowercase = 1 / 255       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(**_lowercase )
												_lowerCAmelCase    		=					size if size is not None else {"""height""": 384, """width""": 384}
												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												_lowerCAmelCase    		=					do_resize
												_lowerCAmelCase    		=					size
												_lowerCAmelCase    		=					keep_aspect_ratio
												_lowerCAmelCase    		=					ensure_multiple_of
												_lowerCAmelCase    		=					resample
												_lowerCAmelCase    		=					do_rescale
												_lowerCAmelCase    		=					rescale_factor
												_lowerCAmelCase    		=					do_normalize
												_lowerCAmelCase    		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_lowerCAmelCase    		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = False       ,  _lowercase = 1       ,  _lowercase = PILImageResampling.BICUBIC       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												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    		=					get_resize_output_image_size(
												    _lowercase       ,  output_size=(size["""height"""], size["""width"""])       ,  keep_aspect_ratio=_lowercase       ,  multiple=_lowercase       ,  )
												return resize(_lowercase       ,  size=_lowercase       ,  resample=_lowercase       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												return rescale(_lowercase       ,  scale=_lowercase       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												return normalize(_lowercase       ,  mean=_lowercase       ,  std=_lowercase       ,  data_format=_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = ChannelDimension.FIRST       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					do_resize if do_resize is not None else self.do_resize
												_lowerCAmelCase    		=					size if size is not None else self.size
												_lowerCAmelCase    		=					get_size_dict(_lowercase )
												_lowerCAmelCase    		=					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_lowerCAmelCase    		=					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_lowerCAmelCase    		=					resample if resample is not None else self.resample
												_lowerCAmelCase    		=					do_rescale if do_rescale is not None else self.do_rescale
												_lowerCAmelCase    		=					rescale_factor if rescale_factor is not None else self.rescale_factor
												_lowerCAmelCase    		=					do_normalize if do_normalize is not None else self.do_normalize
												_lowerCAmelCase    		=					image_mean if image_mean is not None else self.image_mean
												_lowerCAmelCase    		=					image_std if image_std is not None else self.image_std

												_lowerCAmelCase    		=					make_list_of_images(_lowercase )

												if not valid_images(_lowercase ):
																		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    		=					[to_numpy_array(_lowercase ) for image in images]

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

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

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

												_lowerCAmelCase    		=					[to_channel_dimension_format(_lowercase       ,  _lowercase ) for image in images]

												_lowerCAmelCase    		=					{"""pixel_values""": images}
												return BatchFeature(data=_lowercase       ,  tensor_type=_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					outputs.logits

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

																		if is_torch_tensor(_lowercase ):
																								_lowerCAmelCase    		=					target_sizes.numpy()

																		_lowerCAmelCase    		=					[]

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

												return semantic_segmentation








'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






