



'''simple docstring'''




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def        lowerCAmelCase_     (			_lowerCamelCase:     Dict      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.exp(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.sum(_lowerCamelCase ,   dim=1      )  # sum of exp(x_i)
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(x * exp_x ,   dim=1      )  # sum of x_i * exp(x_i)
		return torch.log(_lowerCamelCase      ) - B / A






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	config.output_attentions
					__SCREAMING_SNAKE_CASE :	Dict					 =	config.output_hidden_states
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.ModuleList([BertLayer(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.ModuleList([BertHighway(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	[-1 for _ in range(config.num_hidden_layers		)]

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					if (type(lowerCAmelCase__		) is float) or (type(lowerCAmelCase__		) is int):
							for i in range(len(self.early_exit_entropy		)		):
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	x
					else:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	x

			def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	pooler.state_dict()
					for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
									param.copy_(loaded_model[name]		)



			def UpperCamelCase__      (							self  :				Tuple							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				Dict=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	()
					__SCREAMING_SNAKE_CASE :	Dict					 =	()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	()
					for i, layer_module in enumerate(self.layer		):
							if self.output_hidden_states:
									__SCREAMING_SNAKE_CASE :	Tuple					 =	all_hidden_states + (hidden_states,)

							__SCREAMING_SNAKE_CASE :	List[Any]					 =	layer_module(
							    lowerCAmelCase__							,  lowerCAmelCase__							,  head_mask[i]							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_outputs[0]

							if self.output_attentions:
									__SCREAMING_SNAKE_CASE :	str					 =	all_attentions + (layer_outputs[1],)

							__SCREAMING_SNAKE_CASE :	int					 =	(hidden_states,)
							if self.output_hidden_states:
									__SCREAMING_SNAKE_CASE :	Tuple					 =	current_outputs + (all_hidden_states,)
							if self.output_attentions:
									__SCREAMING_SNAKE_CASE :	str					 =	current_outputs + (all_attentions,)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.highway[i](lowerCAmelCase__		)
							# logits, pooled_output

							if not self.training:
									__SCREAMING_SNAKE_CASE :	str					 =	highway_exit[0]
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	entropy(lowerCAmelCase__		)
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
									__SCREAMING_SNAKE_CASE :	Dict					 =	all_highway_exits + (highway_exit,)

									if highway_entropy < self.early_exit_entropy[i]:
											__SCREAMING_SNAKE_CASE :	int					 =	(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
											raise HighwayException(lowerCAmelCase__							,  i + 1		)
							else:
									__SCREAMING_SNAKE_CASE :	List[str]					 =	all_highway_exits + (highway_exit,)

        # Add last layer
					if self.output_hidden_states:
							__SCREAMING_SNAKE_CASE :	Dict					 =	all_hidden_states + (hidden_states,)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(hidden_states,)
					if self.output_hidden_states:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	outputs + (all_hidden_states,)
					if self.output_attentions:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	outputs + (all_attentions,)

					__SCREAMING_SNAKE_CASE :	str					 =	outputs + (all_highway_exits,)
					return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''					,      lowerCamelCase__					,      )
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	config

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BertEmbeddings(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	DeeBertEncoder(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BertPooler(lowerCAmelCase__		)

					self.init_weights()

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					self.encoder.init_highway_pooler(self.pooler		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					return self.embeddings.word_embeddings

			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	value

			def UpperCamelCase__      (							self  :				Tuple							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					for layer, heads in heads_to_prune.items():
							self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__		)



			@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Union[str, Any]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  lowerCAmelCase__  :				str=None							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				str=None							,  ):





					"""simple docstring"""






					if input_ids is not None and inputs_embeds is not None:
							raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""		)
					elif input_ids is not None:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	input_ids.size()
					elif inputs_embeds is not None:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	inputs_embeds.size()[:-1]
					else:
							raise ValueError("""You have to specify either input_ids or inputs_embeds"""		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
							__SCREAMING_SNAKE_CASE :	str					 =	torch.ones(lowerCAmelCase__							,  device=lowerCAmelCase__		)
					if encoder_attention_mask is None:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.ones(lowerCAmelCase__							,  device=lowerCAmelCase__		)
					if token_type_ids is None:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.zeros(lowerCAmelCase__							,  dtype=torch.long							,  device=lowerCAmelCase__		)

					# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
					# ourselves in which case we just need to make it broadcastable to all heads.
					__SCREAMING_SNAKE_CASE :	torch.Tensor					 =	self.get_extended_attention_mask(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)

					# If a 2D ou 3D attention mask is provided for the cross-attention
					# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
					if encoder_attention_mask.dim() == 3:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.dim() == 2:
							__SCREAMING_SNAKE_CASE :	Any					 =	encoder_attention_mask[:, None, None, :]

					__SCREAMING_SNAKE_CASE :	int					 =	encoder_extended_attention_mask.to(
					    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

					# Prepare head mask if needed
					# 1.0 in head_mask indicate we keep the head
					# attention_probs has shape bsz x n_heads x N x N
					# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
					# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_head_mask(lowerCAmelCase__							,  self.config.num_hidden_layers		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.embeddings(
					    input_ids=lowerCAmelCase__							,  position_ids=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  inputs_embeds=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.encoder(
					    lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  head_mask=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  encoder_attention_mask=lowerCAmelCase__							,  )
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	encoder_outputs[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.pooler(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
					    sequence_output,
					    pooled_output,
					) + encoder_outputs[
					    1:
					]  # add hidden_states and attentions if they are here
					return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	message
					__SCREAMING_SNAKE_CASE :	List[str]					 =	exit_layer  # start from 1!






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Any					 =	BertPooler(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	nn.Dropout(config.hidden_dropout_prob		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Linear(config.hidden_size							,  config.num_labels		)



			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	encoder_outputs[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.pooler(lowerCAmelCase__		)
					# "return" pooler_output

					# BertModel
					__SCREAMING_SNAKE_CASE :	str					 =	(pooler_input, pooler_output) + encoder_outputs[1:]
					# "return" bmodel_output

					# Dropout and classification
					__SCREAMING_SNAKE_CASE :	Tuple					 =	bmodel_output[1]

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.dropout(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	self.classifier(lowerCAmelCase__		)

					return logits, pooled_output






@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''					,      lowerCamelCase__					,      )
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	config.num_labels
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	config.num_hidden_layers

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	DeeBertModel(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	nn.Dropout(config.hidden_dropout_prob		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.Linear(config.hidden_size							,  self.config.num_labels		)

					self.init_weights()



			@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				str=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				int=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Tuple=-1							,  lowerCAmelCase__  :				Union[str, Any]=False							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.num_layers
					try:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.bert(
							    lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  position_ids=lowerCAmelCase__							,  head_mask=lowerCAmelCase__							,  inputs_embeds=lowerCAmelCase__							,  )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							__SCREAMING_SNAKE_CASE :	List[Any]					 =	outputs[1]

							__SCREAMING_SNAKE_CASE :	str					 =	self.dropout(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.classifier(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	(logits,) + outputs[2:]  # add hidden states and attention if they are here
					except HighwayException as e:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	e.message
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	e.exit_layer
							__SCREAMING_SNAKE_CASE :	List[str]					 =	outputs[0]

					if not self.training:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	entropy(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[]
					if labels is not None:
							if self.num_labels == 1:
									#  We are doing regression
									__SCREAMING_SNAKE_CASE :	Tuple					 =	MSELoss()
									__SCREAMING_SNAKE_CASE :	Dict					 =	loss_fct(logits.view(-1		)							,  labels.view(-1		)		)
							else:
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	CrossEntropyLoss()
									__SCREAMING_SNAKE_CASE :	str					 =	loss_fct(logits.view(-1							,  self.num_labels		)							,  labels.view(-1		)		)

							# work with highway exits
							__SCREAMING_SNAKE_CASE :	List[str]					 =	[]
							for highway_exit in outputs[-1]:
									__SCREAMING_SNAKE_CASE :	str					 =	highway_exit[0]
									if not self.training:
											highway_logits_all.append(lowerCAmelCase__		)
											highway_entropy.append(highway_exit[2]		)
									if self.num_labels == 1:
											#  We are doing regression
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	MSELoss()
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	loss_fct(highway_logits.view(-1		)							,  labels.view(-1		)		)
									else:
											__SCREAMING_SNAKE_CASE :	Any					 =	CrossEntropyLoss()
											__SCREAMING_SNAKE_CASE :	Dict					 =	loss_fct(highway_logits.view(-1							,  self.num_labels		)							,  labels.view(-1		)		)
									highway_losses.append(lowerCAmelCase__		)

							if train_highway:
									__SCREAMING_SNAKE_CASE :	int					 =	(sum(highway_losses[:-1]		),) + outputs
									# exclude the final highway, of course
							else:
									__SCREAMING_SNAKE_CASE :	Dict					 =	(loss,) + outputs
					if not self.training:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
									    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
									)  # use the highway of the last layer

					return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)






'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	(KDPMaDiscreteScheduler,)
			_A   :	Dict    					=	10

			def UpperCamelCase__      (							self  :				Optional[int]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	{
					    """num_train_timesteps""": 1_1_0_0,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]		):
							self.check_over_configs(beta_start=lowerCAmelCase__							,  beta_end=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






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

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	sample.to(lowerCAmelCase__		)

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

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.69_34E-07		) < 1E-2
							assert abs(result_mean.item() - 6.11_12E-10		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07		) < 1E-2
							assert abs(result_mean.item() - 0.00_02		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	str					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	int					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3



			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler_class(**lowerCAmelCase__		)

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

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter.to(lowerCAmelCase__		) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	str					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if str(lowerCAmelCase__		).startswith("""cpu"""		):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3