




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     (EulerDiscreteScheduler,)
							a__						  =     1_0





							def    _UpperCAmelCase     (					self       :	str       ,     **a       :	Tuple			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					{
										    """num_train_timesteps""": 1_100,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**a			)
										return config





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Tuple:
										for timesteps in [10, 50, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=a			)





							def    _UpperCAmelCase     (					self       :	int			)			->					List[str]:
										for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,     [0.0002, 0.002, 0.02]			):
													self.check_over_configs(beta_start=a       ,     beta_end=a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					List[str]:
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=a			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Dict:
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=a			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config()
										SCREAMING_SNAKE_CASE           =					scheduler_class(**a			)

										scheduler.set_timesteps(self.num_inference_steps			)

										SCREAMING_SNAKE_CASE           =					torch.manual_seed(0			)

										SCREAMING_SNAKE_CASE           =					self.dummy_model()
										SCREAMING_SNAKE_CASE           =					self.dummy_sample_deter * scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(a			)

										for i, t in enumerate(scheduler.timesteps			):
													SCREAMING_SNAKE_CASE           =					scheduler.scale_model_input(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					model(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					scheduler.step(a       ,     a       ,     a       ,     generator=a			)
													SCREAMING_SNAKE_CASE           =					output.prev_sample

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(a			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(a			)			)

										assert abs(result_sum.item() - 10.0807			) < 1E-2
										assert abs(result_mean.item() - 0.0131			) < 1E-3





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config(prediction_type="""v_prediction"""			)
										SCREAMING_SNAKE_CASE           =					scheduler_class(**a			)

										scheduler.set_timesteps(self.num_inference_steps			)

										SCREAMING_SNAKE_CASE           =					torch.manual_seed(0			)

										SCREAMING_SNAKE_CASE           =					self.dummy_model()
										SCREAMING_SNAKE_CASE           =					self.dummy_sample_deter * scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(a			)

										for i, t in enumerate(scheduler.timesteps			):
													SCREAMING_SNAKE_CASE           =					scheduler.scale_model_input(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					model(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					scheduler.step(a       ,     a       ,     a       ,     generator=a			)
													SCREAMING_SNAKE_CASE           =					output.prev_sample

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(a			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(a			)			)

										assert abs(result_sum.item() - 0.0002			) < 1E-2
										assert abs(result_mean.item() - 2.2_6_7_6E-0_6			) < 1E-3





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config()
										SCREAMING_SNAKE_CASE           =					scheduler_class(**a			)

										scheduler.set_timesteps(self.num_inference_steps       ,     device=a			)

										SCREAMING_SNAKE_CASE           =					torch.manual_seed(0			)

										SCREAMING_SNAKE_CASE           =					self.dummy_model()
										SCREAMING_SNAKE_CASE           =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										SCREAMING_SNAKE_CASE           =					sample.to(a			)

										for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE           =					scheduler.scale_model_input(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					model(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					scheduler.step(a       ,     a       ,     a       ,     generator=a			)
													SCREAMING_SNAKE_CASE           =					output.prev_sample

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(a			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(a			)			)

										assert abs(result_sum.item() - 10.0807			) < 1E-2
										assert abs(result_mean.item() - 0.0131			) < 1E-3







							def    _UpperCAmelCase     (					self       :	int			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE           =					self.get_scheduler_config()
										SCREAMING_SNAKE_CASE           =					scheduler_class(**a       ,     use_karras_sigmas=a			)

										scheduler.set_timesteps(self.num_inference_steps       ,     device=a			)

										SCREAMING_SNAKE_CASE           =					torch.manual_seed(0			)

										SCREAMING_SNAKE_CASE           =					self.dummy_model()
										SCREAMING_SNAKE_CASE           =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										SCREAMING_SNAKE_CASE           =					sample.to(a			)

										for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE           =					scheduler.scale_model_input(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					model(a       ,     a			)

													SCREAMING_SNAKE_CASE           =					scheduler.step(a       ,     a       ,     a       ,     generator=a			)
													SCREAMING_SNAKE_CASE           =					output.prev_sample

										SCREAMING_SNAKE_CASE           =					torch.sum(torch.abs(a			)			)
										SCREAMING_SNAKE_CASE           =					torch.mean(torch.abs(a			)			)

										assert abs(result_sum.item() - 124.52_2994_9951_1719			) < 1E-2
										assert abs(result_mean.item() - 0.1_6213_9326_3339_9963			) < 1E-3












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():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




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




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






