




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,
    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_vision_available, logging


if is_vision_available():
       import PIL


SCREAMING_SNAKE_CASE__			: str    =  logging.get_logger(__name__)




class 						UpperCamelCase__      (_a	):

    '''simple docstring'''



    lowerCamelCase_ :      str      	= ["""pixel_values"""]







    def __init__(       self	,    UpperCamelCase__ = True	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = PILImageResampling.BILINEAR	,    UpperCamelCase__ = True	,    UpperCamelCase__ = 1 / 255	,    UpperCamelCase__ = True	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    **UpperCamelCase__	,    )	-> Optional[int]:
           super().__init__(**__lowerCAmelCase	)
           lowerCamelCase  : Any     					=   size if size is not None else {"shortest_edge": 384}
           lowerCamelCase  : Any     					=   get_size_dict(__lowerCAmelCase	,    default_to_square=__lowerCAmelCase	)

           lowerCamelCase  : Optional[int]     					=   do_resize
           lowerCamelCase  : Dict     					=   size
           # Default value set here for backwards compatibility where the value in config is None
           lowerCamelCase  : Optional[int]     					=   crop_pct if crop_pct is not None else 224 / 256
           lowerCamelCase  : Union[str, Any]     					=   resample
           lowerCamelCase  : Dict     					=   do_rescale
           lowerCamelCase  : List[str]     					=   rescale_factor
           lowerCamelCase  : Tuple     					=   do_normalize
           lowerCamelCase  : List[str]     					=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase  : str     					=   image_std if image_std is not None else IMAGENET_STANDARD_STD







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__ = PILImageResampling.BICUBIC	,    UpperCamelCase__ = None	,    **UpperCamelCase__	,    )	-> Tuple:
           lowerCamelCase  : List[Any]     					=   get_size_dict(__lowerCAmelCase	,    default_to_square=__lowerCAmelCase	)
           if "shortest_edge" not in size:
                  raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''	)
           lowerCamelCase  : Dict     					=   size["shortest_edge"]

           if shortest_edge < 384:
                  # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                  lowerCamelCase  : Any     					=   int(shortest_edge / crop_pct	)
                  lowerCamelCase  : int     					=   get_resize_output_image_size(__lowerCAmelCase	,    size=__lowerCAmelCase	,    default_to_square=__lowerCAmelCase	)
                  lowerCamelCase  : Optional[int]     					=   resize(image=__lowerCAmelCase	,    size=__lowerCAmelCase	,    resample=__lowerCAmelCase	,    data_format=__lowerCAmelCase	,    **__lowerCAmelCase	)
                  # then crop to (shortest_edge, shortest_edge)
                  return center_crop(image=__lowerCAmelCase	,    size=(shortest_edge, shortest_edge)	,    data_format=__lowerCAmelCase	,    **__lowerCAmelCase	)
           else:
                  # warping (no cropping) when evaluated at 384 or larger
                  return resize(
                      __lowerCAmelCase	,    size=(shortest_edge, shortest_edge)	,    resample=__lowerCAmelCase	,    data_format=__lowerCAmelCase	,    **__lowerCAmelCase	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__ = None	,    **UpperCamelCase__	,    )	-> List[Any]:
           return rescale(__lowerCAmelCase	,    scale=__lowerCAmelCase	,    data_format=__lowerCAmelCase	,    **__lowerCAmelCase	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__ = None	,    **UpperCamelCase__	,    )	-> int:
           return normalize(__lowerCAmelCase	,    mean=__lowerCAmelCase	,    std=__lowerCAmelCase	,    data_format=__lowerCAmelCase	,    **__lowerCAmelCase	)







    def        _lowercase	(       self	,    UpperCamelCase__	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = None	,    UpperCamelCase__ = ChannelDimension.FIRST	,    **UpperCamelCase__	,    )	-> str:
           lowerCamelCase  : str     					=   do_resize if do_resize is not None else self.do_resize
           lowerCamelCase  : Union[str, Any]     					=   crop_pct if crop_pct is not None else self.crop_pct
           lowerCamelCase  : Any     					=   resample if resample is not None else self.resample
           lowerCamelCase  : List[Any]     					=   do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase  : Union[str, Any]     					=   rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase  : Any     					=   do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase  : List[Any]     					=   image_mean if image_mean is not None else self.image_mean
           lowerCamelCase  : List[str]     					=   image_std if image_std is not None else self.image_std

           lowerCamelCase  : str     					=   size if size is not None else self.size
           lowerCamelCase  : Tuple     					=   get_size_dict(__lowerCAmelCase	,    default_to_square=__lowerCAmelCase	)

           lowerCamelCase  : Dict     					=   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 or resample is None:
                  raise ValueError("Size and resample must be specified if do_resize is True."	)

           if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
                  raise ValueError("crop_pct must be specified if size < 384."	)

           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  : Optional[int]     					=   [to_numpy_array(__lowerCAmelCase	) for image in images]

           if do_resize:
                  lowerCamelCase  : int     					=   [self.resize(image=__lowerCAmelCase	,    size=__lowerCAmelCase	,    crop_pct=__lowerCAmelCase	,    resample=__lowerCAmelCase	) for image in images]

           if do_rescale:
                  lowerCamelCase  : List[str]     					=   [self.rescale(image=__lowerCAmelCase	,    scale=__lowerCAmelCase	) for image in images]

           if do_normalize:
                  lowerCamelCase  : Dict     					=   [self.normalize(image=__lowerCAmelCase	,    mean=__lowerCAmelCase	,    std=__lowerCAmelCase	) for image in images]

           lowerCamelCase  : Optional[Any]     					=   [to_channel_dimension_format(__lowerCAmelCase	,    __lowerCAmelCase	) for image in images]

           lowerCamelCase  : Tuple     					=   {"pixel_values": images}
           return BatchFeature(data=__lowerCAmelCase	,    tensor_type=__lowerCAmelCase	)













from __future__ import annotations

from math import pi





def      _UpperCamelCase  (a__ :float ,      a__ :float ,      a__ :float							):






							"""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()




