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,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
      import torch


__lowercase										= logging.get_logger(__name__)


class      lowerCamelCase_					(       UpperCamelCase__			):






    '''simple docstring'''






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







    def __init__(     self			,			__lowercase = True			,			__lowercase = None			,			__lowercase = PILImageResampling.BILINEAR			,			__lowercase = True			,			__lowercase = None			,			__lowercase = True			,			__lowercase = 1 / 255			,			__lowercase = True			,			__lowercase = None			,			__lowercase = None			,			**__lowercase			,			)	->     List[Any]:
        super().__init__(**__a)
        __UpperCamelCase					:Optional[Any]        =		size if size is not None else {'''shortest_edge''': 256}
        __UpperCamelCase					:int        =		get_size_dict(__a			,			default_to_square=__a)
        __UpperCamelCase					:List[str]        =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        __UpperCamelCase					:Any        =		get_size_dict(__a			,			param_name='''crop_size''')
        __UpperCamelCase					:Optional[Any]        =		do_resize
        __UpperCamelCase					:Optional[Any]        =		size
        __UpperCamelCase					:int        =		resample
        __UpperCamelCase					:List[Any]        =		do_center_crop
        __UpperCamelCase					:List[Any]        =		crop_size
        __UpperCamelCase					:Any        =		do_rescale
        __UpperCamelCase					:int        =		rescale_factor
        __UpperCamelCase					:int        =		do_normalize
        __UpperCamelCase					:Dict        =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCamelCase					:Union[str, Any]        =		image_std if image_std is not None else IMAGENET_STANDARD_STD







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = PILImageResampling.BICUBIC			,			__lowercase = None			,			**__lowercase			,			)	->     str:
        __UpperCamelCase					:List[Any]        =		get_size_dict(__a			,			default_to_square=__a)
        if "shortest_edge" not in size:
            raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
        __UpperCamelCase					:Tuple        =		get_resize_output_image_size(__a			,			size=size['''shortest_edge''']			,			default_to_square=__a)
        return resize(__a			,			size=__a			,			resample=__a			,			data_format=__a			,			**__a)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase			,			)	->     Optional[int]:
        __UpperCamelCase					:str        =		get_size_dict(__a)
        if "height" not in size or "width" not in size:
            raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""")
        return center_crop(__a			,			size=(size['''height'''], size['''width'''])			,			data_format=__a			,			**__a)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase)	->     Tuple:
        return rescale(__a			,			scale=__a			,			data_format=__a			,			**__a)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase			,			)	->     List[str]:
        return normalize(__a			,			mean=__a			,			std=__a			,			data_format=__a			,			**__a)







    def       UpperCamelCase__       (     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			,			)	->     Optional[int]:
        __UpperCamelCase					:List[str]        =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase					:List[str]        =		size if size is not None else self.size
        __UpperCamelCase					:int        =		get_size_dict(__a			,			default_to_square=__a)
        __UpperCamelCase					:Any        =		resample if resample is not None else self.resample
        __UpperCamelCase					:Optional[Any]        =		do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCamelCase					:Optional[int]        =		crop_size if crop_size is not None else self.crop_size
        __UpperCamelCase					:Dict        =		get_size_dict(__a			,			param_name='''crop_size''')
        __UpperCamelCase					:Union[str, Any]        =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase					:List[str]        =		rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase					:Tuple        =		do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase					:List[str]        =		image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase					:Tuple        =		image_std if image_std is not None else self.image_std

        __UpperCamelCase					:List[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:
            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.''')

        # All transformations expect numpy arrays.
        __UpperCamelCase					:Optional[Any]        =		[to_numpy_array(__a) for image in images]

        if do_resize:
            __UpperCamelCase					:Union[str, Any]        =		[self.resize(image=__a			,			size=__a			,			resample=__a) for image in images]

        if do_center_crop:
            __UpperCamelCase					:Any        =		[self.center_crop(image=__a			,			size=__a) for image in images]

        if do_rescale:
            __UpperCamelCase					:Optional[Any]        =		[self.rescale(image=__a			,			scale=__a) for image in images]

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

        __UpperCamelCase					:Tuple        =		[to_channel_dimension_format(__a			,			__a) for image in images]

        __UpperCamelCase					:List[Any]        =		{'''pixel_values''': images}
        return BatchFeature(data=__a			,			tensor_type=__a)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[Any]:
        __UpperCamelCase					:Any        =		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):
                __UpperCamelCase					:Tuple        =		target_sizes.numpy()

            __UpperCamelCase					:Optional[Any]        =		[]

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

        return semantic_segmentation






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''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()





