
'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       (EulerDiscreteScheduler,)
						_lowercase		:				Optional[Any]									=       1_0




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{
												    """num_train_timesteps""": 1_100,
												    """beta_start""": 0.0001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**_lowercase				)
												return config




						def  _lowercase     ( self				):

												"""simple docstring"""



												for timesteps in [10, 50, 100, 1_000]:
																		self.check_over_configs(num_train_timesteps=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												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=_lowercase   ,       beta_end=_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												scheduler.set_timesteps(self.num_inference_steps				)

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for i, t in enumerate(scheduler.timesteps				):
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config(prediction_type="""v_prediction"""				)
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

												scheduler.set_timesteps(self.num_inference_steps				)

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for i, t in enumerate(scheduler.timesteps				):
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												assert abs(result_sum.item() - 0.0002				) < 1e-2
												assert abs(result_mean.item() - 2.2676e-06				) < 1e-3




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase				)

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

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for t in scheduler.timesteps:
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.scheduler_classes[0]
												_lowerCAmelCase  						=			self.get_scheduler_config()
												_lowerCAmelCase  						=			scheduler_class(**_lowercase   ,       use_karras_sigmas=_lowercase				)

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

												_lowerCAmelCase  						=			torch.manual_seed(0				)

												_lowerCAmelCase  						=			self.dummy_model()
												_lowerCAmelCase  						=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												_lowerCAmelCase  						=			sample.to(_lowercase				)

												for t in scheduler.timesteps:
																		_lowerCAmelCase  						=			scheduler.scale_model_input(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			model(_lowercase   ,       _lowercase				)

																		_lowerCAmelCase  						=			scheduler.step(_lowercase   ,       _lowercase   ,       _lowercase   ,       generator=_lowercase				)
																		_lowerCAmelCase  						=			output.prev_sample

												_lowerCAmelCase  						=			torch.sum(torch.abs(_lowercase				)				)
												_lowerCAmelCase  						=			torch.mean(torch.abs(_lowercase				)				)

												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


'''simple docstring'''



from ...configuration_utils import PretrainedConfig




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Tuple									=       '''bert-generation'''




						def __init__( self   ,       _lowercase=50_358   ,       _lowercase=1_024   ,       _lowercase=24   ,       _lowercase=16   ,       _lowercase=4_096   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=1   ,       _lowercase="absolute"   ,       _lowercase=True   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase				)

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			position_embedding_type
												_lowerCAmelCase  						=			use_cache
