


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DonutImageProcessor







class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=7   ,						__lowercase=3   ,						__lowercase=1_8   ,						__lowercase=3_0   ,						__lowercase=4_0_0   ,						__lowercase=True   ,						__lowercase=None   ,						__lowercase=True   ,						__lowercase=False   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=[0.5, 0.5, 0.5]   ,						__lowercase=[0.5, 0.5, 0.5]   ,						)					->    List[str]:
										lowerCAmelCase_ :      Any    				=				parent
										lowerCAmelCase_ :      List[str]    				=				batch_size
										lowerCAmelCase_ :      Tuple    				=				num_channels
										lowerCAmelCase_ :      int    				=				image_size
										lowerCAmelCase_ :      Optional[Any]    				=				min_resolution
										lowerCAmelCase_ :      List[str]    				=				max_resolution
										lowerCAmelCase_ :      Any    				=				do_resize
										lowerCAmelCase_ :      str    				=				size if size is not None else {'''height''': 1_8, '''width''': 2_0}
										lowerCAmelCase_ :      List[str]    				=				do_thumbnail
										lowerCAmelCase_ :      str    				=				do_align_axis
										lowerCAmelCase_ :      Any    				=				do_pad
										lowerCAmelCase_ :      Any    				=				do_normalize
										lowerCAmelCase_ :      Optional[Any]    				=				image_mean
										lowerCAmelCase_ :      str    				=				image_std




						def 						lowercase_ (				self							)					->    int:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}







@require_torch
@require_vision
class 				snake_case__(							UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							DonutImageProcessor if is_vision_available() else None




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Union[str, Any]    				=				DonutImageProcessingTester(self							)




						@property
						def 						lowercase_ (				self							)					->    Any:
										return self.image_processor_tester.prepare_image_processor_dict()




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Tuple    				=				self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''size'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_thumbnail'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_align_long_axis'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_pad'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''do_normalize'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''image_mean'''							)							)
										self.assertTrue(hasattr(__lowercase   ,						'''image_std'''							)							)




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      Dict    				=				self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size   ,						{'''height''': 1_8, '''width''': 2_0}							)

										lowerCAmelCase_ :      Optional[int]    				=				self.image_processing_class.from_dict(self.image_processor_dict   ,						size=4_2							)
										self.assertEqual(image_processor.size   ,						{'''height''': 4_2, '''width''': 4_2}							)

										# Previous config had dimensions in (width, height) order
										lowerCAmelCase_ :      int    				=				self.image_processing_class.from_dict(self.image_processor_dict   ,						size=(4_2, 8_4)							)
										self.assertEqual(image_processor.size   ,						{'''height''': 8_4, '''width''': 4_2}							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										pass




						@is_flaky()
						def 						lowercase_ (				self							)					->    Optional[Any]:
										# Initialize image_processing
										lowerCAmelCase_ :      int    				=				self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_ :      int    				=				prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase   ,						Image.Image							)

										# Test not batched input
										lowerCAmelCase_ :      List[str]    				=				image_processing(image_inputs[0]   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )   ,						)

										# Test batched
										lowerCAmelCase_ :      Optional[int]    				=				image_processing(__lowercase   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )   ,						)




						@is_flaky()
						def 						lowercase_ (				self							)					->    List[Any]:
										# Initialize image_processing
										lowerCAmelCase_ :      Optional[int]    				=				self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_ :      Optional[Any]    				=				prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=__lowercase   ,						numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase   ,						np.ndarray							)

										# Test not batched input
										lowerCAmelCase_ :      Optional[int]    				=				image_processing(image_inputs[0]   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )   ,						)

										# Test batched
										lowerCAmelCase_ :      List[str]    				=				image_processing(__lowercase   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )   ,						)




						@is_flaky()
						def 						lowercase_ (				self							)					->    Any:
										# Initialize image_processing
										lowerCAmelCase_ :      Optional[int]    				=				self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_ :      Optional[int]    				=				prepare_image_inputs(self.image_processor_tester   ,						equal_resolution=__lowercase   ,						torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase   ,						torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_ :      List[Any]    				=				image_processing(image_inputs[0]   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )   ,						)

										# Test batched
										lowerCAmelCase_ :      Tuple    				=				image_processing(__lowercase   ,						return_tensors='''pt'''							).pixel_values
										self.assertEqual(
										    encoded_images.shape   ,						(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )   ,						)



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()