


"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	(EulerAncestralDiscreteScheduler,)
		__UpperCamelCase							=	10







		def        a_       (    self					:				Optional[int]			,      **a__					:				Dict						)				->  Tuple:





						'''simple docstring'''







						_A         =	{
						    "num_train_timesteps": 11_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						}

						config.update(**a__						)
						return config







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





						'''simple docstring'''







						for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a__						)







		def        a_       (    self					:				Optional[Any]						)				->  Optional[Any]:





						'''simple docstring'''







						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,      [0.0_0_0_2, 0.0_0_2, 0.0_2]						):
										self.check_over_configs(beta_start=a__			,      beta_end=a__						)







		def        a_       (    self					:				str						)				->  str:





						'''simple docstring'''







						for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=a__						)







		def        a_       (    self					:				List[Any]						)				->  Optional[int]:





						'''simple docstring'''







						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a__						)







		def        a_       (    self					:				Optional[int]						)				->  Optional[Any]:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						_A         =	torch.manual_seed(0						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_A         =	sample.to(a__						)

						for i, t in enumerate(scheduler.timesteps						):
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__			,      generator=a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						assert abs(result_sum.item() - 1_5_2.3_1_9_2						) < 1E-2
						assert abs(result_mean.item() - 0.1_9_8_3						) < 1E-3







		def        a_       (    self					:				Tuple						)				->  Any:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config(prediction_type="v_prediction"						)
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps						)

						_A         =	torch.manual_seed(0						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma
						_A         =	sample.to(a__						)

						for i, t in enumerate(scheduler.timesteps						):
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__			,      generator=a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						assert abs(result_sum.item() - 1_0_8.4_4_3_9						) < 1E-2
						assert abs(result_mean.item() - 0.1_4_1_2						) < 1E-3







		def        a_       (    self					:				Optional[Any]						)				->  Optional[Any]:





						'''simple docstring'''







						_A         =	self.scheduler_classes[0]
						_A         =	self.get_scheduler_config()
						_A         =	scheduler_class(**a__						)

						scheduler.set_timesteps(self.num_inference_steps			,      device=a__						)
						_A         =	torch.manual_seed(0						)

						_A         =	self.dummy_model()
						_A         =	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						_A         =	sample.to(a__						)

						for t in scheduler.timesteps:
										_A         =	scheduler.scale_model_input(a__			,      a__						)

										_A         =	model(a__			,      a__						)

										_A         =	scheduler.step(a__			,      a__			,      a__			,      generator=a__						)
										_A         =	output.prev_sample

						_A         =	torch.sum(torch.abs(a__						)						)
						_A         =	torch.mean(torch.abs(a__						)						)

						assert abs(result_sum.item() - 1_5_2.3_1_9_2						) < 1E-2
						assert abs(result_mean.item() - 0.1_9_8_3						) < 1E-3



"""simple docstring"""


import unittest

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


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      device=a__			,      dtype=torch.float			,      )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]			,      a__			,      atol=1E-4						)						)