






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):





	"""simple docstring"""






	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]       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			size if size is not None else {'shortest_edge': 18}
							_UpperCAmelCase									=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			min_resolution
							_UpperCAmelCase									=			max_resolution
							_UpperCAmelCase									=			do_resize
							_UpperCAmelCase									=			size
							_UpperCAmelCase									=			do_center_crop
							_UpperCAmelCase									=			crop_size
							_UpperCAmelCase									=			do_normalize
							_UpperCAmelCase									=			image_mean
							_UpperCAmelCase									=			image_std

	def        UpperCamelCase    (							self    ):
							"""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 __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      LevitImageProcessor if is_vision_available() else None

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			LevitImageProcessingTester(self    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self.image_processor_tester.prepare_image_processor_dict()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			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}    )

							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							# Test not batched input
							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			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        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			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'],
							    )       ,    )








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
