





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						(EulerAncestralDiscreteScheduler,)
					lowerCAmelCase_				=						10




					def 						lowerCAmelCase      (		self					,    **lowerCAmelCase_	)		->				Tuple:
										_snake_case						=					{
										    'num_train_timesteps': 1100,
										    'beta_start': 0.00_01,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										}

										config.update(**lowerCAmelCase_	)
										return config




					def 						lowerCAmelCase      (		self	)		->				List[str]:
										for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,    [0.00_02, 0.0_02, 0.02]	):
															self.check_over_configs(beta_start=lowerCAmelCase_					,    beta_end=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				List[str]:
										for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.scheduler_classes[0]
										_snake_case						=					self.get_scheduler_config()
										_snake_case						=					scheduler_class(**lowerCAmelCase_	)

										scheduler.set_timesteps(self.num_inference_steps	)

										_snake_case						=					torch.manual_seed(0	)

										_snake_case						=					self.dummy_model()
										_snake_case						=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										_snake_case						=					sample.to(lowerCAmelCase_	)

										for i, t in enumerate(scheduler.timesteps	):
															_snake_case						=					scheduler.scale_model_input(lowerCAmelCase_					,    lowerCAmelCase_	)

															_snake_case						=					model(lowerCAmelCase_					,    lowerCAmelCase_	)

															_snake_case						=					scheduler.step(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    generator=lowerCAmelCase_	)
															_snake_case						=					output.prev_sample

										_snake_case						=					torch.sum(torch.abs(lowerCAmelCase_	)	)
										_snake_case						=					torch.mean(torch.abs(lowerCAmelCase_	)	)

										assert abs(result_sum.item() - 1_52.31_92	) < 1E-2
										assert abs(result_mean.item() - 0.19_83	) < 1E-3




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					self.scheduler_classes[0]
										_snake_case						=					self.get_scheduler_config(prediction_type='v_prediction'	)
										_snake_case						=					scheduler_class(**lowerCAmelCase_	)

										scheduler.set_timesteps(self.num_inference_steps	)

										_snake_case						=					torch.manual_seed(0	)

										_snake_case						=					self.dummy_model()
										_snake_case						=					self.dummy_sample_deter * scheduler.init_noise_sigma
										_snake_case						=					sample.to(lowerCAmelCase_	)

										for i, t in enumerate(scheduler.timesteps	):
															_snake_case						=					scheduler.scale_model_input(lowerCAmelCase_					,    lowerCAmelCase_	)

															_snake_case						=					model(lowerCAmelCase_					,    lowerCAmelCase_	)

															_snake_case						=					scheduler.step(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    generator=lowerCAmelCase_	)
															_snake_case						=					output.prev_sample

										_snake_case						=					torch.sum(torch.abs(lowerCAmelCase_	)	)
										_snake_case						=					torch.mean(torch.abs(lowerCAmelCase_	)	)

										assert abs(result_sum.item() - 1_08.44_39	) < 1E-2
										assert abs(result_mean.item() - 0.14_12	) < 1E-3






					def 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case						=					self.scheduler_classes[0]
										_snake_case						=					self.get_scheduler_config()
										_snake_case						=					scheduler_class(**lowerCAmelCase_	)

										scheduler.set_timesteps(self.num_inference_steps					,    device=lowerCAmelCase_	)
										_snake_case						=					torch.manual_seed(0	)

										_snake_case						=					self.dummy_model()
										_snake_case						=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										_snake_case						=					sample.to(lowerCAmelCase_	)

										for t in scheduler.timesteps:
															_snake_case						=					scheduler.scale_model_input(lowerCAmelCase_					,    lowerCAmelCase_	)

															_snake_case						=					model(lowerCAmelCase_					,    lowerCAmelCase_	)

															_snake_case						=					scheduler.step(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    generator=lowerCAmelCase_	)
															_snake_case						=					output.prev_sample

										_snake_case						=					torch.sum(torch.abs(lowerCAmelCase_	)	)
										_snake_case						=					torch.mean(torch.abs(lowerCAmelCase_	)	)

										assert abs(result_sum.item() - 1_52.31_92	) < 1E-2
										assert abs(result_mean.item() - 0.19_83	) < 1E-3










import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
					import torch
class       UpperCamelCase_		(	unittest.TestCase				,	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					load_tool('speech-to-text'	)
										self.tool.setup()




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.tool(torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)






					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					self.tool(audio=torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)



