
"""simple docstring"""
import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						(LMSDiscreteScheduler,)
							SCREAMING_SNAKE_CASE		     =						10






							def 							__lowercase  (					self						:			List[Any]						, **snake_case__						:			Tuple    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					{
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0_001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**a_    )
												return config






							def 							__lowercase  (					self						:			Union[str, Any]    ):






												"""simple docstring"""



												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=a_    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						, [0.0_002, 0.002, 0.02]    ):
																	self.check_over_configs(beta_start=a_						, beta_end=a_    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Union[str, Any]    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												for t in [0, 5_0_0, 8_0_0]:
																	self.check_over_forward(time_step=a_    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						Any		    =					self.scheduler_classes[0]
												__a:						List[Any]		    =					self.get_scheduler_config()
												__a:						str		    =					scheduler_class(**a_    )

												scheduler.set_timesteps(self.num_inference_steps    )

												__a:						Optional[Any]		    =					self.dummy_model()
												__a:						Dict		    =					self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(scheduler.timesteps    ):
																	__a:						Union[str, Any]		    =					scheduler.scale_model_input(a_						, a_    )

																	__a:						Union[str, Any]		    =					model(a_						, a_    )

																	__a:						List[Any]		    =					scheduler.step(a_						, a_						, a_    )
																	__a:						Optional[int]		    =					output.prev_sample

												__a:						Any		    =					torch.sum(torch.abs(a_    )    )
												__a:						Any		    =					torch.mean(torch.abs(a_    )    )

												assert abs(result_sum.item() - 1_006.388    ) < 1e-2
												assert abs(result_mean.item() - 1.31    ) < 1e-3






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					self.scheduler_classes[0]
												__a:						List[Any]		    =					self.get_scheduler_config(prediction_type="""v_prediction"""    )
												__a:						Tuple		    =					scheduler_class(**a_    )

												scheduler.set_timesteps(self.num_inference_steps    )

												__a:						List[Any]		    =					self.dummy_model()
												__a:						Tuple		    =					self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(scheduler.timesteps    ):
																	__a:						List[str]		    =					scheduler.scale_model_input(a_						, a_    )

																	__a:						Tuple		    =					model(a_						, a_    )

																	__a:						Optional[int]		    =					scheduler.step(a_						, a_						, a_    )
																	__a:						Dict		    =					output.prev_sample

												__a:						List[Any]		    =					torch.sum(torch.abs(a_    )    )
												__a:						Union[str, Any]		    =					torch.mean(torch.abs(a_    )    )

												assert abs(result_sum.item() - 0.0_017    ) < 1e-2
												assert abs(result_mean.item() - 2.2676e-06    ) < 1e-3






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						Any		    =					self.scheduler_classes[0]
												__a:						Optional[int]		    =					self.get_scheduler_config()
												__a:						List[Any]		    =					scheduler_class(**a_    )

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

												__a:						str		    =					self.dummy_model()
												__a:						Dict		    =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												__a:						Tuple		    =					sample.to(a_    )

												for i, t in enumerate(scheduler.timesteps    ):
																	__a:						List[Any]		    =					scheduler.scale_model_input(a_						, a_    )

																	__a:						str		    =					model(a_						, a_    )

																	__a:						List[str]		    =					scheduler.step(a_						, a_						, a_    )
																	__a:						Optional[Any]		    =					output.prev_sample

												__a:						List[Any]		    =					torch.sum(torch.abs(a_    )    )
												__a:						Optional[int]		    =					torch.mean(torch.abs(a_    )    )

												assert abs(result_sum.item() - 1_006.388    ) < 1e-2
												assert abs(result_mean.item() - 1.31    ) < 1e-3






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												__a:						Any		    =					self.scheduler_classes[0]
												__a:						Tuple		    =					self.get_scheduler_config()
												__a:						List[Any]		    =					scheduler_class(**a_						, use_karras_sigmas=a_    )

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

												__a:						Optional[int]		    =					self.dummy_model()
												__a:						Tuple		    =					self.dummy_sample_deter.to(a_    ) * scheduler.init_noise_sigma
												__a:						str		    =					sample.to(a_    )

												for t in scheduler.timesteps:
																	__a:						List[str]		    =					scheduler.scale_model_input(a_						, a_    )

																	__a:						List[str]		    =					model(a_						, a_    )

																	__a:						int		    =					scheduler.step(a_						, a_						, a_    )
																	__a:						Dict		    =					output.prev_sample

												__a:						str		    =					torch.sum(torch.abs(a_    )    )
												__a:						Any		    =					torch.mean(torch.abs(a_    )    )

												assert abs(result_sum.item() - 3_812.9_927    ) < 2e-2
												assert abs(result_mean.item() - 4.9_648    ) < 1e-3


"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    _snake_case  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
