


from __future__ import annotations

from math import pi




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if (inductance, frequency, reactance).count(0       ) != 1:
										raise ValueError("One and only one argument must be 0"       )
					if inductance < 0:
										raise ValueError("Inductance cannot be negative"       )
					if frequency < 0:
										raise ValueError("Frequency cannot be negative"       )
					if reactance < 0:
										raise ValueError("Inductive reactance cannot be negative"       )
					if inductance == 0:
										return {"inductance": reactance / (2 * pi * frequency)}
					elif frequency == 0:
										return {"frequency": reactance / (2 * pi * inductance)}
					elif reactance == 0:
										return {"reactance": 2 * pi * frequency * inductance}
					else:
										raise ValueError("Exactly one argument must be 0"       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
		import PIL

if is_torch_available():
		import torch


a__    :							Optional[int]            =					logging.get_logger(__name__)




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





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




	def __init__(			self      :    Dict      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BILINEAR      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Union[int, float] = 1 / 2_5_5      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    bool = True      ,      **UpperCAmelCase__      :    Dict      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    size if size is not None else {"shortest_edge": 2_2_4}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="crop_size"       )

						__SCREAMING_SNAKE_CASE						       =    do_resize
						__SCREAMING_SNAKE_CASE						       =    size
						__SCREAMING_SNAKE_CASE						       =    resample
						__SCREAMING_SNAKE_CASE						       =    do_rescale
						__SCREAMING_SNAKE_CASE						       =    rescale_factor
						__SCREAMING_SNAKE_CASE						       =    do_center_crop
						__SCREAMING_SNAKE_CASE						       =    crop_size
						__SCREAMING_SNAKE_CASE						       =    do_flip_channel_order




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    PILImageResampling = PIL.Image.BILINEAR      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Optional[Any]      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						if "shortest_edge" not in size:
											raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""       )
						__SCREAMING_SNAKE_CASE						       =    get_resize_output_image_size(UpperCAmelCase__      ,      size=size["shortest_edge"]      ,      default_to_square=UpperCAmelCase__       )
						return resize(UpperCAmelCase__      ,      size=UpperCAmelCase__      ,      resample=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Any      ,      )	->     np.ndarray:
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
						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()}"""       )
						return center_crop(UpperCAmelCase__      ,      size=(size["height"], size["width"])      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[int, float]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Dict      ,      )	->     Dict:
						return rescale(UpperCAmelCase__      ,      scale=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None       )	->     np.ndarray:
						return flip_channel_order(UpperCAmelCase__      ,      data_format=UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    ImageInput      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    float = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[Union[str, TensorType]] = None      ,      UpperCAmelCase__      :    ChannelDimension = ChannelDimension.FIRST      ,      **UpperCAmelCase__      :    str      ,      )	->     PIL.Image.Image:
						__SCREAMING_SNAKE_CASE						       =    do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE						       =    do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE						       =    do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE						       =    (
						    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
						)

						__SCREAMING_SNAKE_CASE						       =    size if size is not None else self.size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="crop_size"       )

						__SCREAMING_SNAKE_CASE						       =    make_list_of_images(UpperCAmelCase__       )

						if not valid_images(UpperCAmelCase__       ):
											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_rescale and rescale_factor is None:
											raise ValueError("Rescale factor must be specified if do_rescale is True."       )

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop is True."       )

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

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

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

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

						# the pretrained checkpoints assume images are BGR, not RGB
						if do_flip_channel_order:
											__SCREAMING_SNAKE_CASE						       =    [self.flip_channel_order(image=UpperCAmelCase__       ) for image in images]

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

						__SCREAMING_SNAKE_CASE						       =    {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__      ,      tensor_type=UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    List[Tuple] = None       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    outputs.logits

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

											if is_torch_tensor(UpperCAmelCase__       ):
																__SCREAMING_SNAKE_CASE						       =    target_sizes.numpy()

											__SCREAMING_SNAKE_CASE						       =    []

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

						return semantic_segmentation
