






import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_MAPPING,
		    SegformerForImageClassification,
		    SegformerForSemanticSegmentation,
		    SegformerModel,
		)
		from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import SegformerImageProcessor





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






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







							_UpperCAmelCase									=			self.config_class(**self.inputs_dict    )
							self.parent.assertTrue(hasattr(UpperCAmelCase       ,    'hidden_sizes'    )    )
							self.parent.assertTrue(hasattr(UpperCAmelCase       ,    'num_attention_heads'    )    )
							self.parent.assertTrue(hasattr(UpperCAmelCase       ,    'num_encoder_blocks'    )    )





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=13       ,    UpperCAmelCase=64       ,    UpperCAmelCase=3       ,    UpperCAmelCase=4       ,    UpperCAmelCase=[2, 2, 2, 2]       ,    UpperCAmelCase=[8, 4, 2, 1]       ,    UpperCAmelCase=[16, 32, 64, 128]       ,    UpperCAmelCase=[1, 4, 8, 16]       ,    UpperCAmelCase=[1, 2, 4, 8]       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=3       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			num_encoder_blocks
							_UpperCAmelCase									=			sr_ratios
							_UpperCAmelCase									=			depths
							_UpperCAmelCase									=			hidden_sizes
							_UpperCAmelCase									=			downsampling_rates
							_UpperCAmelCase									=			num_attention_heads
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			hidden_dropout_prob
							_UpperCAmelCase									=			attention_probs_dropout_prob
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			num_labels
							_UpperCAmelCase									=			scope

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







							_UpperCAmelCase									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_UpperCAmelCase									=			None
							if self.use_labels:
													_UpperCAmelCase									=			ids_tensor([self.batch_size, self.image_size, self.image_size]       ,    self.num_labels    )

							_UpperCAmelCase									=			self.get_config()
							return config, pixel_values, labels

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







							return SegformerConfig(
							    image_size=self.image_size       ,    num_channels=self.num_channels       ,    num_encoder_blocks=self.num_encoder_blocks       ,    depths=self.depths       ,    hidden_sizes=self.hidden_sizes       ,    num_attention_heads=self.num_attention_heads       ,    hidden_act=self.hidden_act       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    initializer_range=self.initializer_range       ,    )

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







							_UpperCAmelCase									=			SegformerModel(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase    )
							_UpperCAmelCase									=			_UpperCAmelCase									=			self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape       ,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)    )

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







							_UpperCAmelCase									=			self.num_labels
							_UpperCAmelCase									=			SegformerForSemanticSegmentation(UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase    )
							self.parent.assertEqual(
							    result.logits.shape       ,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)    )
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    labels=UpperCAmelCase    )
							self.parent.assertEqual(
							    result.logits.shape       ,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)    )
							self.parent.assertGreater(result.loss       ,    0.0    )

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







							_UpperCAmelCase									=			1
							_UpperCAmelCase									=			SegformerForSemanticSegmentation(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			torch.randint(0       ,    1       ,    (self.batch_size, self.image_size, self.image_size)    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    labels=UpperCAmelCase    )
							self.parent.assertGreater(result.loss       ,    0.0    )

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







							_UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			config_and_inputs
							_UpperCAmelCase									=			{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase__								=      (
	    {
	        "feature-extraction": SegformerModel,
	        "image-classification": SegformerForImageClassification,
	        "image-segmentation": SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	UpperCamelCase__								=      True
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

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







							_UpperCAmelCase									=			SegformerModelTester(self    )
							_UpperCAmelCase									=			SegformerConfigTester(self       ,    config_class=UpperCAmelCase    )

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







							self.config_tester.run_common_tests()

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







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

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







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*UpperCAmelCase    )

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







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase    )

	@unittest.skip('SegFormer does not use inputs_embeds'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

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







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													_UpperCAmelCase									=			inspect.signature(model.forward    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_UpperCAmelCase									=			[*signature.parameters.keys()]

													_UpperCAmelCase									=			['pixel_values']
													self.assertListEqual(arg_names[:1]       ,    UpperCAmelCase    )

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







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			True

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			True
													_UpperCAmelCase									=			False
													_UpperCAmelCase									=			True
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																			_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )
													_UpperCAmelCase									=			outputs.attentions

													_UpperCAmelCase									=			sum(self.model_tester.depths    )
													self.assertEqual(len(UpperCAmelCase    )       ,    UpperCAmelCase    )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													_UpperCAmelCase									=			True
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																			_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )
													_UpperCAmelCase									=			outputs.attentions

													self.assertEqual(len(UpperCAmelCase    )       ,    UpperCAmelCase    )

													# verify the first attentions (first block, first layer)
													_UpperCAmelCase									=			(self.model_tester.image_size // 4) ** 2
													_UpperCAmelCase									=			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]    )       ,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,    )

													# verify the last attentions (last block, last layer)
													_UpperCAmelCase									=			(self.model_tester.image_size // 32) ** 2
													_UpperCAmelCase									=			(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]    )       ,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]       ,    )
													_UpperCAmelCase									=			len(UpperCAmelCase    )

													# Check attention is always last and order is fine
													_UpperCAmelCase									=			True
													_UpperCAmelCase									=			True
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()
													with torch.no_grad():
																			_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

													self.assertEqual(out_len + 1       ,    len(UpperCAmelCase    )    )

													_UpperCAmelCase									=			outputs.attentions

													self.assertEqual(len(UpperCAmelCase    )       ,    UpperCAmelCase    )
													# verify the first attentions (first block, first layer)
													_UpperCAmelCase									=			(self.model_tester.image_size // 4) ** 2
													_UpperCAmelCase									=			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]    )       ,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]       ,    )

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







							def check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()

													with torch.no_grad():
																			_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

													_UpperCAmelCase									=			outputs.hidden_states

													_UpperCAmelCase									=			self.model_tester.num_encoder_blocks
													self.assertEqual(len(UpperCAmelCase    )       ,    UpperCAmelCase    )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]    )       ,    [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]       ,    )

							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			True
													check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_UpperCAmelCase									=			True

													check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

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







							if not self.model_tester.is_training:
													return

							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			True

							for model_class in self.all_model_classes:
													if model_class in get_values(UpperCAmelCase    ):
																			continue

													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.train()
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													_UpperCAmelCase									=			model(**UpperCAmelCase    ).loss
													loss.backward()

	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

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







							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCAmelCase									=			SegformerModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )



def __A    (      )->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
						return image





@require_torch
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






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







							_UpperCAmelCase									=			SegformerImageProcessor(
							    image_scale=(512, 512)       ,    keep_ratio=UpperCAmelCase       ,    align=UpperCAmelCase       ,    do_random_crop=UpperCAmelCase    )
							_UpperCAmelCase									=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'    ).to(
							    UpperCAmelCase    )

							_UpperCAmelCase									=			prepare_img()
							_UpperCAmelCase									=			image_processor(images=UpperCAmelCase       ,    return_tensors='pt'    )
							_UpperCAmelCase									=			encoded_inputs.pixel_values.to(UpperCAmelCase    )

							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase    )

							_UpperCAmelCase									=			torch.Size((1, model.config.num_labels, 128, 128)    )
							self.assertEqual(outputs.logits.shape       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			torch.tensor(
							    [
							        [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
							        [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
							        [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
							    ]    ).to(UpperCAmelCase    )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,    UpperCAmelCase       ,    atol=1e-4    )    )

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







							_UpperCAmelCase									=			SegformerImageProcessor(
							    image_scale=(512, 512)       ,    keep_ratio=UpperCAmelCase       ,    align=UpperCAmelCase       ,    do_random_crop=UpperCAmelCase    )
							_UpperCAmelCase									=			SegformerForSemanticSegmentation.from_pretrained(
							    'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'    ).to(UpperCAmelCase    )

							_UpperCAmelCase									=			prepare_img()
							_UpperCAmelCase									=			image_processor(images=UpperCAmelCase       ,    return_tensors='pt'    )
							_UpperCAmelCase									=			encoded_inputs.pixel_values.to(UpperCAmelCase    )

							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase    )

							_UpperCAmelCase									=			torch.Size((1, model.config.num_labels, 128, 128)    )
							self.assertEqual(outputs.logits.shape       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			torch.tensor(
							    [
							        [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
							        [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
							        [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
							    ]    ).to(UpperCAmelCase    )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]       ,    UpperCAmelCase       ,    atol=1e-1    )    )

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







							_UpperCAmelCase									=			SegformerImageProcessor(
							    image_scale=(512, 512)       ,    keep_ratio=UpperCAmelCase       ,    align=UpperCAmelCase       ,    do_random_crop=UpperCAmelCase    )
							_UpperCAmelCase									=			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'    ).to(
							    UpperCAmelCase    )

							_UpperCAmelCase									=			prepare_img()
							_UpperCAmelCase									=			image_processor(images=UpperCAmelCase       ,    return_tensors='pt'    )
							_UpperCAmelCase									=			encoded_inputs.pixel_values.to(UpperCAmelCase    )

							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase    )

							_UpperCAmelCase									=			outputs.logits.detach().cpu()

							_UpperCAmelCase									=			image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase       ,    target_sizes=[(500, 300)]    )
							_UpperCAmelCase									=			torch.Size((500, 300)    )
							self.assertEqual(segmentation[0].shape       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase    )
							_UpperCAmelCase									=			torch.Size((128, 128)    )
							self.assertEqual(segmentation[0].shape       ,    UpperCAmelCase    )








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    )
