
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin




class   __lowercase						(						__snake_case   , __snake_case       ):






	@register_to_config
	def __init__(self				: Dict      ,	snake_case				: int = 128      ,	snake_case				: int = 256      ,	snake_case				: float = 20_00.0      ,	snake_case				: int = 768      ,	snake_case				: int = 12      ,	snake_case				: int = 12      ,	snake_case				: int = 64      ,	snake_case				: int = 2048      ,	snake_case				: float = 0.1      ,	) ->			Optional[int]:
							super().__init__()

							_lowercase       :	Any           =   nn.Sequential(
							    nn.Linear(snake_case      ,	d_model * 4      ,	bias=snake_case    )      ,	nn.SiLU()      ,	nn.Linear(d_model * 4      ,	d_model * 4      ,	bias=snake_case    )      ,	nn.SiLU()      ,	)

							_lowercase       :	Dict           =   nn.Embedding(snake_case      ,	snake_case    )
							_lowercase       :	List[str]           =   False

							_lowercase       :	List[Any]           =   nn.Linear(snake_case      ,	snake_case      ,	bias=snake_case    )

							_lowercase       :	List[Any]           =   nn.Dropout(p=snake_case    )

							_lowercase       :	str           =   nn.ModuleList()
							for lyr_num in range(snake_case    ):
													# FiLM conditional T5 decoder
													_lowercase       :	List[str]           =   DecoderLayer(d_model=snake_case      ,	d_kv=snake_case      ,	num_heads=snake_case      ,	d_ff=snake_case      ,	dropout_rate=snake_case    )
													self.decoders.append(snake_case    )

							_lowercase       :	Optional[Any]           =   TaLayerNorm(snake_case    )

							_lowercase       :	Tuple           =   nn.Dropout(p=snake_case    )
							_lowercase       :	Optional[Any]           =   nn.Linear(snake_case      ,	snake_case      ,	bias=snake_case    )






	def       _a(self				: Dict      ,	snake_case				: List[Any]      ,	snake_case				: Dict    ) ->			Dict:
							_lowercase       :	List[Any]           =   torch.mul(query_input.unsqueeze(-1    )      ,	key_input.unsqueeze(-2    )    )
							return mask.unsqueeze(-3    )






	def       _a(self				: List[Any]      ,	snake_case				: int      ,	snake_case				: Any      ,	snake_case				: str    ) ->			str:
							_lowercase      ,    _lowercase      ,    _lowercase       :	Any           =   decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							_lowercase       :	Dict           =   get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time      ,	embedding_dim=self.config.d_model      ,	max_period=self.config.max_decoder_noise_time      ,	).to(dtype=self.dtype    )

							_lowercase       :	Tuple           =   self.conditioning_emb(snake_case    ).unsqueeze(1    )

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							_lowercase       :	List[str]           =   decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							_lowercase       :	List[str]           =   torch.broadcast_to(
							    torch.arange(snake_case      ,	device=decoder_input_tokens.device    )      ,	(batch, seq_length)      ,	)

							_lowercase       :	int           =   self.position_encoding(snake_case    )

							_lowercase       :	Union[str, Any]           =   self.continuous_inputs_projection(snake_case    )
							inputs += position_encodings
							_lowercase       :	Optional[int]           =   self.dropout(snake_case    )

							# decoder: No padding present.
							_lowercase       :	int           =   torch.ones(
							    decoder_input_tokens.shape[:2]      ,	device=decoder_input_tokens.device      ,	dtype=inputs.dtype    )

							# Translate encoding masks to encoder-decoder masks.
							_lowercase       :	str           =   [(x, self.encoder_decoder_mask(snake_case      ,	snake_case    )) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							_lowercase       :	Optional[Any]           =   torch.cat([x[0] for x in encodings_and_encdec_masks]      ,	dim=1    )
							_lowercase       :	int           =   torch.cat([x[1] for x in encodings_and_encdec_masks]      ,	dim=-1    )

							for lyr in self.decoders:
													_lowercase       :	List[Any]           =   lyr(
													    snake_case      ,	conditioning_emb=snake_case      ,	encoder_hidden_states=snake_case      ,	encoder_attention_mask=snake_case      ,	)[0]

							_lowercase       :	Tuple           =   self.decoder_norm(snake_case    )
							_lowercase       :	Optional[int]           =   self.post_dropout(snake_case    )

							_lowercase       :	List[str]           =   self.spec_out(snake_case    )
							return spec_out




class   __lowercase						(						nn.Module       ):






	def __init__(self				: Optional[Any]      ,	snake_case				: Optional[Any]      ,	snake_case				: Tuple      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]      ,	snake_case				: List[Any]      ,	snake_case				: Any=1e-6    ) ->			Optional[int]:
							super().__init__()
							_lowercase       :	List[Any]           =   nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=snake_case      ,	d_kv=snake_case      ,	num_heads=snake_case      ,	dropout_rate=snake_case    )    )

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=snake_case      ,	d_kv=snake_case      ,	num_heads=snake_case      ,	dropout_rate=snake_case      ,	layer_norm_epsilon=snake_case      ,	)    )

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=snake_case      ,	d_ff=snake_case      ,	dropout_rate=snake_case      ,	layer_norm_epsilon=snake_case    )    )






	def       _a(self				: Union[str, Any]      ,	snake_case				: Optional[int]      ,	snake_case				: Any=None      ,	snake_case				: List[str]=None      ,	snake_case				: int=None      ,	snake_case				: List[Any]=None      ,	snake_case				: List[Any]=None      ,	) ->			str:
							_lowercase       :	Tuple           =   self.layer[0](
							    snake_case      ,	conditioning_emb=snake_case      ,	attention_mask=snake_case      ,	)

							if encoder_hidden_states is not None:
													_lowercase       :	List[Any]           =   torch.where(encoder_attention_mask > 0      ,	0      ,	-1e10    ).to(
													    encoder_hidden_states.dtype    )

													_lowercase       :	Tuple           =   self.layer[1](
													    snake_case      ,	key_value_states=snake_case      ,	attention_mask=snake_case      ,	)

							# Apply Film Conditional Feed Forward layer
							_lowercase       :	List[Any]           =   self.layer[-1](snake_case      ,	snake_case    )

							return (hidden_states,)




class   __lowercase						(						nn.Module       ):






	def __init__(self				: Union[str, Any]      ,	snake_case				: Dict      ,	snake_case				: Any      ,	snake_case				: Optional[int]      ,	snake_case				: str    ) ->			Any:
							super().__init__()
							_lowercase       :	Union[str, Any]           =   TaLayerNorm(snake_case    )
							_lowercase       :	Any           =   TaFiLMLayer(in_features=d_model * 4      ,	out_features=snake_case    )
							_lowercase       :	Optional[Any]           =   Attention(query_dim=snake_case      ,	heads=snake_case      ,	dim_head=snake_case      ,	out_bias=snake_case      ,	scale_qk=snake_case    )
							_lowercase       :	int           =   nn.Dropout(snake_case    )






	def       _a(self				: List[str]      ,	snake_case				: Tuple      ,	snake_case				: Optional[int]=None      ,	snake_case				: str=None      ,	) ->			int:
							# pre_self_attention_layer_norm
							_lowercase       :	Any           =   self.layer_norm(snake_case    )

							if conditioning_emb is not None:
													_lowercase       :	Any           =   self.FiLMLayer(snake_case      ,	snake_case    )

							# Self-attention block
							_lowercase       :	str           =   self.attention(snake_case    )

							_lowercase       :	str           =   hidden_states + self.dropout(snake_case    )

							return hidden_states




class   __lowercase						(						nn.Module       ):






	def __init__(self				: int      ,	snake_case				: Optional[int]      ,	snake_case				: Tuple      ,	snake_case				: Optional[int]      ,	snake_case				: Union[str, Any]      ,	snake_case				: Union[str, Any]    ) ->			Tuple:
							super().__init__()
							_lowercase       :	Optional[Any]           =   Attention(query_dim=snake_case      ,	heads=snake_case      ,	dim_head=snake_case      ,	out_bias=snake_case      ,	scale_qk=snake_case    )
							_lowercase       :	List[str]           =   TaLayerNorm(snake_case      ,	eps=snake_case    )
							_lowercase       :	Tuple           =   nn.Dropout(snake_case    )






	def       _a(self				: Tuple      ,	snake_case				: List[str]      ,	snake_case				: Union[str, Any]=None      ,	snake_case				: int=None      ,	) ->			Tuple:
							_lowercase       :	Optional[Any]           =   self.layer_norm(snake_case    )
							_lowercase       :	List[str]           =   self.attention(
							    snake_case      ,	encoder_hidden_states=snake_case      ,	attention_mask=attention_mask.squeeze(1    )      ,	)
							_lowercase       :	List[str]           =   hidden_states + self.dropout(snake_case    )
							return layer_output




class   __lowercase						(						nn.Module       ):






	def __init__(self				: int      ,	snake_case				: Optional[int]      ,	snake_case				: List[str]      ,	snake_case				: Optional[Any]      ,	snake_case				: Tuple    ) ->			Tuple:
							super().__init__()
							_lowercase       :	Optional[int]           =   TaDenseGatedActDense(d_model=snake_case      ,	d_ff=snake_case      ,	dropout_rate=snake_case    )
							_lowercase       :	Dict           =   TaFiLMLayer(in_features=d_model * 4      ,	out_features=snake_case    )
							_lowercase       :	List[str]           =   TaLayerNorm(snake_case      ,	eps=snake_case    )
							_lowercase       :	Any           =   nn.Dropout(snake_case    )






	def       _a(self				: List[Any]      ,	snake_case				: Dict      ,	snake_case				: List[str]=None    ) ->			Optional[int]:
							_lowercase       :	str           =   self.layer_norm(snake_case    )
							if conditioning_emb is not None:
													_lowercase       :	Optional[int]           =   self.film(snake_case      ,	snake_case    )

							_lowercase       :	List[Any]           =   self.DenseReluDense(snake_case    )
							_lowercase       :	List[Any]           =   hidden_states + self.dropout(snake_case    )
							return hidden_states




class   __lowercase						(						nn.Module       ):






	def __init__(self				: List[str]      ,	snake_case				: Dict      ,	snake_case				: int      ,	snake_case				: List[str]    ) ->			List[Any]:
							super().__init__()
							_lowercase       :	Dict           =   nn.Linear(snake_case      ,	snake_case      ,	bias=snake_case    )
							_lowercase       :	str           =   nn.Linear(snake_case      ,	snake_case      ,	bias=snake_case    )
							_lowercase       :	Any           =   nn.Linear(snake_case      ,	snake_case      ,	bias=snake_case    )
							_lowercase       :	str           =   nn.Dropout(snake_case    )
							_lowercase       :	str           =   NewGELUActivation()






	def       _a(self				: Union[str, Any]      ,	snake_case				: List[str]    ) ->			str:
							_lowercase       :	List[Any]           =   self.act(self.wi_a(snake_case    )    )
							_lowercase       :	Optional[int]           =   self.wi_a(snake_case    )
							_lowercase       :	str           =   hidden_gelu * hidden_linear
							_lowercase       :	int           =   self.dropout(snake_case    )

							_lowercase       :	Dict           =   self.wo(snake_case    )
							return hidden_states




class   __lowercase						(						nn.Module       ):






	def __init__(self				: List[str]      ,	snake_case				: Union[str, Any]      ,	snake_case				: Tuple=1e-6    ) ->			Optional[int]:
							super().__init__()
							_lowercase       :	int           =   nn.Parameter(torch.ones(snake_case    )    )
							_lowercase       :	Union[str, Any]           =   eps






	def       _a(self				: Tuple      ,	snake_case				: Union[str, Any]    ) ->			Optional[Any]:
							# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
							# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
							# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
							# half-precision inputs is done in fp32

							_lowercase       :	str           =   hidden_states.to(torch.floataa    ).pow(2    ).mean(-1      ,	keepdim=snake_case    )
							_lowercase       :	Optional[int]           =   hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
													_lowercase       :	Optional[Any]           =   hidden_states.to(self.weight.dtype    )

							return self.weight * hidden_states




class   __lowercase						(						nn.Module       ):






	def       _a(self				: int      ,	snake_case				: torch.Tensor    ) ->			torch.Tensor:
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.04_47_15 * torch.pow(snake_case      ,	3.0    ))    ))




class   __lowercase						(						nn.Module       ):






	def __init__(self				: Optional[Any]      ,	snake_case				: Optional[Any]      ,	snake_case				: List[str]    ) ->			int:
							super().__init__()
							_lowercase       :	Optional[Any]           =   nn.Linear(snake_case      ,	out_features * 2      ,	bias=snake_case    )






	def       _a(self				: List[str]      ,	snake_case				: Any      ,	snake_case				: List[str]    ) ->			Union[str, Any]:
							_lowercase       :	Optional[Any]           =   self.scale_bias(snake_case    )
							_lowercase      ,    _lowercase       :	List[str]           =   torch.chunk(snake_case      ,	2      ,	-1    )
							_lowercase       :	Tuple           =   x * (1 + scale) + shift
							return x






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, 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       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

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

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	str           =   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)
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   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       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   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"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, 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       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			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="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	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       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




