

"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import 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 LevitImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    str=7					,  UpperCamelCase__							:    Optional[int]=3					,  UpperCamelCase__							:    Any=1_8					,  UpperCamelCase__							:    int=3_0					,  UpperCamelCase__							:    List[Any]=4_0_0					,  UpperCamelCase__							:    int=True					,  UpperCamelCase__							:    List[Any]=None					,  UpperCamelCase__							:    Union[str, Any]=True					,  UpperCamelCase__							:    List[Any]=None					,  UpperCamelCase__							:    Tuple=True					,  UpperCamelCase__							:    List[str]=[0.5, 0.5, 0.5]					,  UpperCamelCase__							:    List[Any]=[0.5, 0.5, 0.5]					,  )->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      size if size is not None else {"shortest_edge": 1_8}
						__lowerCAmelCase:	Tuple   				=      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	List[Any]   				=      parent
						__lowerCAmelCase:	str   				=      batch_size
						__lowerCAmelCase:	List[Any]   				=      num_channels
						__lowerCAmelCase:	List[str]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      min_resolution
						__lowerCAmelCase:	Tuple   				=      max_resolution
						__lowerCAmelCase:	List[Any]   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	Dict   				=      do_center_crop
						__lowerCAmelCase:	int   				=      crop_size
						__lowerCAmelCase:	Optional[Any]   				=      do_normalize
						__lowerCAmelCase:	Optional[Any]   				=      image_mean
						__lowerCAmelCase:	int   				=      image_std




				def 							lowercase_					(     self							:    Optional[int])->       List[str]:



						'''simple docstring'''


						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}





@require_torch
@require_vision
class    snake_case     (    lowerCamelCase__,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  LevitImageProcessor if is_vision_available() else None

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      LevitImageProcessingTester(self)

				@property
				def 							lowercase_					(     self							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    str)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))

				def 							lowercase_					(     self							:    str)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 1_8})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2					,  crop_size=8_4)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})

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



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Tuple)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Tuple   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	List[Any]   				=      image_processing(UpperCamelCase__					,  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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

				def 							lowercase_					(     self							:    str)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	Dict   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# 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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	List[str]   				=      image_processing(UpperCamelCase__					,  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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )




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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	str   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Any   				=      image_processing(UpperCamelCase__					,  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.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )





"""simple docstring"""


from __future__ import annotations

from math import pi






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     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()


