






'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
			import torch

			from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class _lowercase			(       unittest.TestCase	):




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Any 						= XLMRobertaModel.from_pretrained("""xlm-roberta-base"""  )
														lowerCamelCase__      :			Tuple 						= torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]  )
														# The dog is cute and lives in the garden house

														lowerCamelCase__      :			List[Any] 						= torch.Size((1, 12, 768)  )  # batch_size, sequence_length, embedding_vector_dim
														lowerCamelCase__      :			Tuple 						= torch.tensor(
														    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]  )
														#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
														#  xlmr.eval()
														#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
														with torch.no_grad():
																					lowerCamelCase__      :			Union[str, Any] 						= model(UpperCamelCase__  )["""last_hidden_state"""].detach()
														self.assertEqual(output.shape    ,							UpperCamelCase__  )
														# compare the actual values for a slice of last dim
														self.assertTrue(torch.allclose(output[:, :, -1]    ,							UpperCamelCase__    ,							atol=1e-3  )  )




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Any 						= XLMRobertaModel.from_pretrained("""xlm-roberta-large"""  )
														lowerCamelCase__      :			List[Any] 						= torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]  )
														# The dog is cute and lives in the garden house

														lowerCamelCase__      :			Dict 						= torch.Size((1, 12, 1_024)  )  # batch_size, sequence_length, embedding_vector_dim
														lowerCamelCase__      :			Optional[int] 						= torch.tensor(
														    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]  )
														#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
														#  xlmr.eval()
														#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
														with torch.no_grad():
																					lowerCamelCase__      :			Any 						= model(UpperCamelCase__  )["""last_hidden_state"""].detach()
														self.assertEqual(output.shape    ,							UpperCamelCase__  )
														# compare the actual values for a slice of last dim
														self.assertTrue(torch.allclose(output[:, :, -1]    ,							UpperCamelCase__    ,							atol=1e-3  )  )









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

