
"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						(DPMSolverMultistepScheduler,)
							SCREAMING_SNAKE_CASE		     =						(('''num_inference_steps''', 25),)






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






												"""simple docstring"""



												__a:						Optional[int]		    =					{
												    """num_train_timesteps""": 1_0_0_0,
												    """beta_start""": 0.0_001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												    """solver_order""": 2,
												    """prediction_type""": """epsilon""",
												    """thresholding""": False,
												    """sample_max_value""": 1.0,
												    """algorithm_type""": """dpmsolver++""",
												    """solver_type""": """midpoint""",
												    """lower_order_final""": False,
												    """lambda_min_clipped""": -float("""inf"""    ),
												    """variance_type""": None,
												}

												config.update(**snake_case__    )
												return config






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]=0						, **snake_case__						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					dict(self.forward_default_kwargs    )
												__a:						Tuple		    =					kwargs.pop("""num_inference_steps"""						, snake_case__    )
												__a:						Any		    =					self.dummy_sample
												__a:						List[Any]		    =					0.1 * sample
												__a:						str		    =					[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	__a:						List[Any]		    =					self.get_scheduler_config(**snake_case__    )
																	__a:						Dict		    =					scheduler_class(**snake_case__    )
																	scheduler.set_timesteps(snake_case__    )
																	# copy over dummy past residuals
																	__a:						Optional[int]		    =					dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(snake_case__    )
																						__a:						List[Any]		    =					scheduler_class.from_pretrained(snake_case__    )
																						new_scheduler.set_timesteps(snake_case__    )
																						# copy over dummy past residuals
																						__a:						Optional[Any]		    =					dummy_past_residuals[: new_scheduler.config.solver_order]

																	__a    ,				__a:						List[Any]		    =					sample, sample
																	for t in range(snake_case__						, time_step + scheduler.config.solver_order + 1    ):
																						__a:						Any		    =					scheduler.step(snake_case__						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample
																						__a:						List[Any]		    =					new_scheduler.step(snake_case__						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample

																						assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												pass






							def 							__lowercase  (					self						:			int						, snake_case__						:			Optional[int]=0						, **snake_case__						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					dict(self.forward_default_kwargs    )
												__a:						Any		    =					kwargs.pop("""num_inference_steps"""						, snake_case__    )
												__a:						int		    =					self.dummy_sample
												__a:						int		    =					0.1 * sample
												__a:						str		    =					[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	__a:						Any		    =					self.get_scheduler_config()
																	__a:						List[str]		    =					scheduler_class(**snake_case__    )
																	scheduler.set_timesteps(snake_case__    )

																	# copy over dummy past residuals (must be after setting timesteps)
																	__a:						Optional[int]		    =					dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(snake_case__    )
																						__a:						int		    =					scheduler_class.from_pretrained(snake_case__    )
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(snake_case__    )

																						# copy over dummy past residual (must be after setting timesteps)
																						__a:						Union[str, Any]		    =					dummy_past_residuals[: new_scheduler.config.solver_order]

																	__a:						Tuple		    =					scheduler.step(snake_case__						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample
																	__a:						Any		    =					new_scheduler.step(snake_case__						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample

																	assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






							def 							__lowercase  (					self						:			str						, snake_case__						:			int=None						, **snake_case__						:			List[str]    ):






												"""simple docstring"""



												if scheduler is None:
																	__a:						Any		    =					self.scheduler_classes[0]
																	__a:						str		    =					self.get_scheduler_config(**snake_case__    )
																	__a:						List[str]		    =					scheduler_class(**snake_case__    )

												__a:						int		    =					1_0
												__a:						int		    =					self.dummy_model()
												__a:						Dict		    =					self.dummy_sample_deter
												scheduler.set_timesteps(snake_case__    )

												for i, t in enumerate(scheduler.timesteps    ):
																	__a:						Optional[Any]		    =					model(snake_case__						, snake_case__    )
																	__a:						Tuple		    =					scheduler.step(snake_case__						, snake_case__						, snake_case__    ).prev_sample

												return sample






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						List[str]		    =					dict(self.forward_default_kwargs    )

												__a:						List[Any]		    =					kwargs.pop("""num_inference_steps"""						, snake_case__    )

												for scheduler_class in self.scheduler_classes:
																	__a:						Dict		    =					self.get_scheduler_config()
																	__a:						int		    =					scheduler_class(**snake_case__    )

																	__a:						Tuple		    =					self.dummy_sample
																	__a:						int		    =					0.1 * sample

																	if num_inference_steps is not None and hasattr(snake_case__						, """set_timesteps"""    ):
																						scheduler.set_timesteps(snake_case__    )
																	elif num_inference_steps is not None and not hasattr(snake_case__						, """set_timesteps"""    ):
																						__a:						int		    =					num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	__a:						Union[str, Any]		    =					[residual + 0.2, residual + 0.15, residual + 0.10]
																	__a:						Optional[Any]		    =					dummy_past_residuals[: scheduler.config.solver_order]

																	__a:						str		    =					scheduler.timesteps[5]
																	__a:						Tuple		    =					scheduler.timesteps[6]

																	__a:						Optional[Any]		    =					scheduler.step(snake_case__						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample
																	__a:						List[Any]		    =					scheduler.step(snake_case__						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample

																	self.assertEqual(output_a.shape						, sample.shape    )
																	self.assertEqual(output_a.shape						, output_a.shape    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=snake_case__    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												self.check_over_configs(thresholding=snake_case__    )
												for order in [1, 2, 3]:
																	for solver_type in ["midpoint", "heun"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=snake_case__						, prediction_type=snake_case__						, sample_max_value=snake_case__						, algorithm_type="""dpmsolver++"""						, solver_order=snake_case__						, solver_type=snake_case__						, )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																						for order in [1, 2, 3]:
																											for prediction_type in ["epsilon", "sample"]:
																																if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																					if order == 3:
																																										continue
																																else:
																																					self.check_over_configs(
																																					    solver_order=snake_case__						, solver_type=snake_case__						, prediction_type=snake_case__						, algorithm_type=snake_case__						, )
																																__a:						List[Any]		    =					self.full_loop(
																																    solver_order=snake_case__						, solver_type=snake_case__						, prediction_type=snake_case__						, algorithm_type=snake_case__						, )
																																assert not torch.isnan(snake_case__    ).any(), "Samples have nan numbers"






							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												self.check_over_configs(lower_order_final=snake_case__    )
												self.check_over_configs(lower_order_final=snake_case__    )






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






												"""simple docstring"""



												self.check_over_configs(lambda_min_clipped=-float("""inf"""    )    )
												self.check_over_configs(lambda_min_clipped=-5.1    )






							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												self.check_over_configs(variance_type=snake_case__    )
												self.check_over_configs(variance_type="""learned_range"""    )






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






												"""simple docstring"""



												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=snake_case__						, time_step=0    )






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					self.full_loop()
												__a:						str		    =					torch.mean(torch.abs(snake_case__    )    )

												assert abs(result_mean.item() - 0.3_301    ) < 1e-3






							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.full_loop(thresholding=snake_case__						, dynamic_thresholding_ratio=0.87						, sample_max_value=0.5    )
												__a:						int		    =					torch.mean(torch.abs(snake_case__    )    )

												assert abs(result_mean.item() - 1.1_364    ) < 1e-3






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					self.full_loop(prediction_type="""v_prediction"""    )
												__a:						Union[str, Any]		    =					torch.mean(torch.abs(snake_case__    )    )

												assert abs(result_mean.item() - 0.2_251    ) < 1e-3






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						str		    =					self.full_loop(prediction_type="""v_prediction"""						, use_karras_sigmas=snake_case__    )
												__a:						str		    =					torch.mean(torch.abs(snake_case__    )    )

												assert abs(result_mean.item() - 0.2_096    ) < 1e-3






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






												"""simple docstring"""



												__a:						Dict		    =					DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
												__a:						Optional[int]		    =					self.full_loop(scheduler=snake_case__    )
												__a:						Optional[int]		    =					torch.mean(torch.abs(snake_case__    )    )

												assert abs(result_mean.item() - 0.3_301    ) < 1e-3

												__a:						str		    =					DPMSolverSinglestepScheduler.from_config(scheduler.config    )
												__a:						Optional[Any]		    =					UniPCMultistepScheduler.from_config(scheduler.config    )
												__a:						Tuple		    =					DEISMultistepScheduler.from_config(scheduler.config    )
												__a:						Optional[Any]		    =					DPMSolverMultistepScheduler.from_config(scheduler.config    )

												__a:						Any		    =					self.full_loop(scheduler=snake_case__    )
												__a:						Optional[int]		    =					torch.mean(torch.abs(snake_case__    )    )

												assert abs(result_mean.item() - 0.3_301    ) < 1e-3






							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						Any		    =					self.scheduler_classes[0]
												__a:						Tuple		    =					self.get_scheduler_config(thresholding=snake_case__						, dynamic_thresholding_ratio=0    )
												__a:						Any		    =					scheduler_class(**snake_case__    )

												__a:						str		    =					1_0
												__a:						Union[str, Any]		    =					self.dummy_model()
												__a:						Optional[int]		    =					self.dummy_sample_deter.half()
												scheduler.set_timesteps(snake_case__    )

												for i, t in enumerate(scheduler.timesteps    ):
																	__a:						List[Any]		    =					model(snake_case__						, snake_case__    )
																	__a:						int		    =					scheduler.step(snake_case__						, snake_case__						, snake_case__    ).prev_sample

												assert sample.dtype == torch.floataa






							def 							__lowercase  (					self						:			List[str]						, **snake_case__						:			int    ):






												"""simple docstring"""



												for scheduler_class in self.scheduler_classes:
																	__a:						Tuple		    =					self.get_scheduler_config(**snake_case__    )
																	__a:						Optional[int]		    =					scheduler_class(**snake_case__    )

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
																	assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps


"""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
