


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCAmelCase			:  List[Any]     =logging.get_logger(__name__)


if is_vision_available():
						import PIL







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Tuple					=							["""pixel_values"""]




						def __init__(				self   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = PILImageResampling.BICUBIC   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = True   ,						__lowercase = 1 / 2_5_5   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = True   ,						**__lowercase   ,						)					->    None:
										super().__init__(**__lowercase							)
										lowerCAmelCase_ :      int    				=				size if size is not None else {'''shortest_edge''': 2_2_4}
										lowerCAmelCase_ :      Optional[int]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)
										lowerCAmelCase_ :      Tuple    				=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
										lowerCAmelCase_ :      Tuple    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase   ,						param_name='''crop_size'''							)

										lowerCAmelCase_ :      Optional[int]    				=				do_resize
										lowerCAmelCase_ :      Union[str, Any]    				=				size
										lowerCAmelCase_ :      List[str]    				=				resample
										lowerCAmelCase_ :      Dict    				=				do_center_crop
										lowerCAmelCase_ :      List[str]    				=				crop_size
										lowerCAmelCase_ :      List[str]    				=				do_rescale
										lowerCAmelCase_ :      Any    				=				rescale_factor
										lowerCAmelCase_ :      List[str]    				=				do_normalize
										lowerCAmelCase_ :      List[str]    				=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCAmelCase_ :      Any    				=				image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCAmelCase_ :      List[Any]    				=				do_convert_rgb




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = PILImageResampling.BICUBIC   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										lowerCAmelCase_ :      Optional[int]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)
										if "shortest_edge" not in size:
														raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
										lowerCAmelCase_ :      Any    				=				get_resize_output_image_size(__lowercase   ,						size=size['''shortest_edge''']   ,						default_to_square=__lowercase							)
										return resize(__lowercase   ,						size=__lowercase   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase							)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
										return center_crop(__lowercase   ,						size=(size['''height'''], size['''width'''])   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    List[str]:
										return rescale(__lowercase   ,						scale=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										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 = None   ,						__lowercase = ChannelDimension.FIRST   ,						**__lowercase   ,						)					->    PIL.Image.Image:
										lowerCAmelCase_ :      int    				=				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_ :      Optional[int]    				=				size if size is not None else self.size
										lowerCAmelCase_ :      Tuple    				=				get_size_dict(__lowercase   ,						param_name='''size'''   ,						default_to_square=__lowercase							)
										lowerCAmelCase_ :      Optional[int]    				=				resample if resample is not None else self.resample
										lowerCAmelCase_ :      Optional[Any]    				=				do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCAmelCase_ :      List[str]    				=				crop_size if crop_size is not None else self.crop_size
										lowerCAmelCase_ :      Optional[int]    				=				get_size_dict(__lowercase   ,						param_name='''crop_size'''   ,						default_to_square=__lowercase							)
										lowerCAmelCase_ :      str    				=				do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_ :      List[Any]    				=				rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_ :      int    				=				do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_ :      Optional[int]    				=				image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_ :      Optional[Any]    				=				image_std if image_std is not None else self.image_std
										lowerCAmelCase_ :      Tuple    				=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCAmelCase_ :      Any    				=				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:
														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.'''							)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
														lowerCAmelCase_ :      Dict    				=				[convert_to_rgb(__lowercase							) for image in images]

										# All transformations expect numpy arrays.
										lowerCAmelCase_ :      Union[str, Any]    				=				[to_numpy_array(__lowercase							) for image in images]

										if do_resize:
														lowerCAmelCase_ :      Optional[int]    				=				[self.resize(image=__lowercase   ,						size=__lowercase   ,						resample=__lowercase							) for image in images]

										if do_center_crop:
														lowerCAmelCase_ :      Dict    				=				[self.center_crop(image=__lowercase   ,						size=__lowercase							) for image in images]

										if do_rescale:
														lowerCAmelCase_ :      Any    				=				[self.rescale(image=__lowercase   ,						scale=__lowercase							) for image in images]

										if do_normalize:
														lowerCAmelCase_ :      Any    				=				[self.normalize(image=__lowercase   ,						mean=__lowercase   ,						std=__lowercase							) for image in images]

										lowerCAmelCase_ :      str    				=				[to_channel_dimension_format(__lowercase   ,						__lowercase							) for image in images]

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



from __future__ import annotations

from math import pi



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> dict[str, float]:

				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()