
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       _lowerCAmelCase   (      unittest.TestCase			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=7							,							_UpperCamelCase=3							,							_UpperCamelCase=18							,							_UpperCamelCase=30							,							_UpperCamelCase=400							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=[0.5, 0.5, 0.5]							,							_UpperCamelCase=[0.5, 0.5, 0.5]							,							)							-> Optional[int]:
							lowerCAmelCase_         =      size if size is not None else {'''shortest_edge''': 18}
							lowerCAmelCase_         =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      min_resolution
							lowerCAmelCase_         =      max_resolution
							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size
							lowerCAmelCase_         =      do_center_crop
							lowerCAmelCase_         =      crop_size
							lowerCAmelCase_         =      do_normalize
							lowerCAmelCase_         =      image_mean
							lowerCAmelCase_         =      image_std




				def 	__a		(   self	)							-> Dict:
							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       _lowerCAmelCase   (      lowercase__   , unittest.TestCase			):
				_lowercase     						=LevitImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      LevitImageProcessingTester(self	)




				@property
				def 	__a		(   self	)							-> List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      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 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size							,							{"shortest_edge": 18}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 18, "width": 18}	)

							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84	)
							self.assertEqual(image_processor.size							,							{"shortest_edge": 42}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 84, "width": 84}	)




				def 	__a		(   self	)							-> Dict:
							pass




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      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_         =      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_         =      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 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCAmelCase_         =      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_         =      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_         =      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 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCAmelCase_         =      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_         =      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_         =      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






def 				_lowerCAmelCase			(   __magic_name__						:     list[float]						)				->  float:
						lowercase :							Any    		=0.0_0
						lowercase :							Tuple    		=0
						for resistor in resistors:
												if resistor <= 0:
																		lowercase :							Dict    		=f'''Resistor at index {index} has a negative or zero value!'''
																		raise ValueError(__magic_name__						)
												first_sum += 1 / float(__magic_name__						)
												index += 1
						return 1 / first_sum






def 				_lowerCAmelCase			(   __magic_name__						:     list[float]						)				->  float:
						lowercase :							Optional[Any]    		=0.0_0
						lowercase :							int    		=0
						for resistor in resistors:
												sum_r += resistor
												if resistor < 0:
																		lowercase :							Tuple    		=f'''Resistor at index {index} has a negative value!'''
																		raise ValueError(__magic_name__						)
												index += 1
						return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()





