
'''simple docstring'''

import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed






class 				A       (		SCREAMING_SNAKE_CASE__      ):





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    str ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    List[Any]=False ,							__magic_name__     :    Optional[int]=1024 ,							__magic_name__     :    Tuple=1024 ,							__magic_name__     :    Dict=3.6 ,							__magic_name__     :    List[Any]=False ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							tokenizer
				lowerCAmelCase__									=							tokenizer.bos_token_id
				lowerCAmelCase__									=							dataset
				lowerCAmelCase__									=							seq_length
				lowerCAmelCase__									=							0
				lowerCAmelCase__									=							infinite
				lowerCAmelCase__									=							0
				lowerCAmelCase__									=							tokenized

				if self.tokenized:
							lowerCAmelCase__									=							seq_length * num_of_sequences
							lowerCAmelCase__									=							"input_ids"
				else:
							lowerCAmelCase__									=							seq_length * chars_per_token * num_of_sequences
							lowerCAmelCase__									=							"content"





	def __iter__(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							iter(self.dataset							)
				lowerCAmelCase__									=							True
				while more_examples:
							lowerCAmelCase__		,lowerCAmelCase__									=							[], 0
							while True:
										if buffer_len >= self.max_buffer_size:
													break
										try:
													buffer.append(next(__magic_name__							)[self.content_field]							)
													buffer_len += len(buffer[-1]							)
										except StopIteration:
													if self.infinite:
																lowerCAmelCase__									=							iter(self.dataset							)
																self.epoch += 1
																logger.info(f"""Dataset epoch: {self.epoch}"""							)
													else:
																lowerCAmelCase__									=							False
																break
							if self.tokenized:
										lowerCAmelCase__									=							buffer
							else:
										lowerCAmelCase__									=							self.tokenizer(__magic_name__ ,							truncation=__magic_name__							)["input_ids"]
							lowerCAmelCase__									=							[]
							for tokenized_input in tokenized_inputs:
										all_token_ids.extend(tokenized_input + [self.concat_token_id]							)
							for i in range(0 ,							len(__magic_name__							) ,							self.seq_length							):
										lowerCAmelCase__									=							all_token_ids[i : i + self.seq_length]
										if len(__magic_name__							) == self.seq_length:
													self.current_size += 1
													yield torch.tensor(__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    int=1000							):


				"""simple docstring"""

				return ShufflerIterDataPipe(self ,							buffer_size=__magic_name__							)







def 					A       ( UpperCamelCase_   :       Any )   ->    Tuple:







			'''simple docstring'''



			lowerCAmelCase__									=							args.model_ckpt.split("/" )[-1]
			lowerCAmelCase__									=							logging.getLogger(__name__ )
			lowerCAmelCase__									=							Path(args.save_dir ) / "log/"
			log_dir.mkdir(exist_ok=UpperCamelCase_ )
			lowerCAmelCase__									=							F"""debug_{accelerator.process_index}.log"""
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,		datefmt="%m/%d/%Y %H:%M:%S"  ,		level=logging.INFO  ,		handlers=[logging.FileHandler(log_dir / filename ), logging.StreamHandler()]  ,		)
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(UpperCamelCase_  ,		vars(UpperCamelCase_ ) )
						lowerCAmelCase__									=							accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						lowerCAmelCase__									=							""
						logger.setLevel(logging.ERROR )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name







def 					A       ( UpperCamelCase_   :       int )   ->    List[Any]:







			'''simple docstring'''



			lowerCAmelCase__									=							{"streaming": True}
			lowerCAmelCase__									=							load_dataset(args.dataset_name_train  ,		split="train"  ,		**UpperCamelCase_ )
			lowerCAmelCase__									=							train_data.shuffle(buffer_size=args.shuffle_buffer  ,		seed=args.seed )
			lowerCAmelCase__									=							load_dataset(args.dataset_name_valid  ,		split="train"  ,		**UpperCamelCase_ )
			lowerCAmelCase__									=							ConstantLengthDataset(
			    UpperCamelCase_  ,		UpperCamelCase_  ,		infinite=UpperCamelCase_  ,		seq_length=args.seq_length  ,		tokenized=args.tokenized )
			lowerCAmelCase__									=							ConstantLengthDataset(
			    UpperCamelCase_  ,		UpperCamelCase_  ,		infinite=UpperCamelCase_  ,		seq_length=args.seq_length  ,		tokenized=args.tokenized )
			lowerCAmelCase__									=							train_dataset.shuffle(buffer_size=args.shuffle_buffer )
			lowerCAmelCase__									=							DataLoader(UpperCamelCase_  ,		batch_size=args.train_batch_size  ,		shuffle=UpperCamelCase_ )
			lowerCAmelCase__									=							DataLoader(UpperCamelCase_  ,		batch_size=args.valid_batch_size )
			return train_dataloader, eval_dataloader







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Dict  ,		UpperCamelCase_   :       List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__		,lowerCAmelCase__									=							[], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay ):
									params_without_wd.append(UpperCamelCase_ )
						else:
									params_with_wd.append(UpperCamelCase_ )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]







def 					A       ( UpperCamelCase_   :       List[str]  ,		UpperCamelCase_   :       Union[str, Any] )   ->    Optional[int]:







			'''simple docstring'''



			logger.info(F"""Step {step}: {metrics}""" )
			if accelerator.is_main_process:
						accelerator.log(UpperCamelCase_  ,		UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       List[str]  ,		UpperCamelCase_   :       Union[str, Any]  ,		UpperCamelCase_   :       int )   ->    Any:







			'''simple docstring'''



			lowerCAmelCase__									=							accelerator.unwrap_model(UpperCamelCase_ ).config
			lowerCAmelCase__									=							4 if args.gradient_checkpointing else 3
			lowerCAmelCase__									=							args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowerCAmelCase__									=							24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowerCAmelCase__									=							factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			lowerCAmelCase__									=							flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops







def 					A       ( UpperCamelCase_   :       Optional[int] )   ->    int:







			'''simple docstring'''



			model.eval()
			lowerCAmelCase__									=							[]
			for step, batch in enumerate(UpperCamelCase_ ):
						with torch.no_grad():
									lowerCAmelCase__									=							model(UpperCamelCase_  ,		labels=UpperCamelCase_ )
						lowerCAmelCase__									=							outputs.loss.repeat(args.valid_batch_size )
						losses.append(accelerator.gather(UpperCamelCase_ ) )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			lowerCAmelCase__									=							torch.cat(UpperCamelCase_ )
			lowerCAmelCase__									=							losses[: eval_dataloader.dataset.current_size].mean()
			try:
						lowerCAmelCase__									=							torch.exp(UpperCamelCase_ )
			except OverflowError:
						lowerCAmelCase__									=							float("inf" )
			return loss.item(), perplexity.item()


# Settings
UpperCAmelCase__				:	Tuple					=						HfArgumentParser(TrainingArguments)
UpperCAmelCase__				:	Any					=						parser.parse_args()

# Accelerator
UpperCAmelCase__				:	Union[str, Any]					=						ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
UpperCAmelCase__				:	Dict					=						Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
UpperCAmelCase__				:	Optional[int]					=						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

UpperCAmelCase__				:	List[str]					=						Namespace(**vars(args), **acc_state)
UpperCAmelCase__				:	int					=						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		UpperCAmelCase__				:	str					=						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
UpperCAmelCase__		,						UpperCAmelCase__				:	Union[str, Any]					=						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
UpperCAmelCase__				:	Optional[int]					=						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
UpperCAmelCase__				:	int					=						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCAmelCase__		,						UpperCAmelCase__				:	Tuple					=						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase__				:	Optional[Any]					=						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase__				:	List[str]					=						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)







def 					A       ( )   ->    int:







			'''simple docstring'''



			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCAmelCase__		,						UpperCAmelCase__		,						UpperCAmelCase__		,						UpperCAmelCase__				:	Optional[Any]					=						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
				accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}")
				accelerator.load_state(args.resume_from_checkpoint)
				UpperCAmelCase__				:	str					=						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				UpperCAmelCase__				:	Dict					=						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
				dirs.sort(key=os.path.getctime)
				UpperCAmelCase__				:	List[str]					=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		UpperCAmelCase__				:	Any					=						os.path.splitext(path)[0]
		UpperCAmelCase__				:	List[Any]					=						int(training_difference.replace("step_", ""))

# Train model
model.train()
UpperCAmelCase__				:	Dict					=						0
UpperCAmelCase__				:	Dict					=						time.time()
UpperCAmelCase__				:	int					=						0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		UpperCAmelCase__				:	str					=						model(batch, labels=batch, use_cache=False).loss
		UpperCAmelCase__				:	str					=						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
		UpperCAmelCase__				:	Union[str, Any]					=						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				UpperCAmelCase__				:	int					=						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				UpperCAmelCase__				:	Tuple					=						time.time() - t_start
				UpperCAmelCase__				:	Tuple					=						compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        "steps": completed_steps,
				        "loss/train": loss_tracking,
				        "lr": lr,
				        "tflops": tflops,
				        "time_per_iteration": elapsed_time,
				    },
				)
				UpperCAmelCase__				:	List[str]					=						time.time()
				UpperCAmelCase__				:	Dict					=						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("Evaluating and saving model checkpoint")
				UpperCAmelCase__		,						UpperCAmelCase__				:	Union[str, Any]					=						evaluate(args)
				log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
				accelerator.wait_for_everyone()
				UpperCAmelCase__				:	Union[str, Any]					=						os.path.join(args.save_dir, F"step_{step}")
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=F"step {step}")
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
UpperCAmelCase__		,						UpperCAmelCase__				:	str					=						evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
UpperCAmelCase__				:	int					=						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
UpperCAmelCase__				:	int					=						os.path.join(args.save_dir, F"step_{step}")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message="final model")







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :torch.FloatTensor






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





	@register_to_config
	def __init__(   self     :    Tuple ,							__magic_name__     :    Optional[Union[int, Tuple[int, int]]] = None ,							__magic_name__     :    int = 3 ,							__magic_name__     :    int = 3 ,							__magic_name__     :    bool = False ,							__magic_name__     :    str = "positional" ,							__magic_name__     :    int = 0 ,							__magic_name__     :    bool = True ,							__magic_name__     :    Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,							__magic_name__     :    Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,							__magic_name__     :    Tuple[int] = (224, 448, 672, 896) ,							__magic_name__     :    int = 2 ,							__magic_name__     :    float = 1 ,							__magic_name__     :    int = 1 ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "silu" ,							__magic_name__     :    Optional[int] = 8 ,							__magic_name__     :    int = 32 ,							__magic_name__     :    float = 1E-5 ,							__magic_name__     :    str = "default" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[str] = None ,							__magic_name__     :    Optional[int] = None ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							sample_size
				lowerCAmelCase__									=							block_out_channels[0] * 4

				# Check inputs
				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""							)

				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""							)

				# input
				lowerCAmelCase__									=							nn.Convad(__magic_name__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)							)

				# time
				if time_embedding_type == "fourier":
							lowerCAmelCase__									=							GaussianFourierProjection(embedding_size=block_out_channels[0] ,							scale=16							)
							lowerCAmelCase__									=							2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							lowerCAmelCase__									=							Timesteps(block_out_channels[0] ,							__magic_name__ ,							__magic_name__							)
							lowerCAmelCase__									=							block_out_channels[0]

				lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)

				# class embedding
				if class_embed_type is None and num_class_embeds is not None:
							lowerCAmelCase__									=							nn.Embedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "timestep":
							lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "identity":
							lowerCAmelCase__									=							nn.Identity(__magic_name__ ,							__magic_name__							)
				else:
							lowerCAmelCase__									=							None

				lowerCAmelCase__									=							nn.ModuleList([]							)
				lowerCAmelCase__									=							None
				lowerCAmelCase__									=							nn.ModuleList([]							)

				# down
				lowerCAmelCase__									=							block_out_channels[0]
				for i, down_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							block_out_channels[i]
							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_down_block(
							    __magic_name__ ,							num_layers=__magic_name__ ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							temb_channels=__magic_name__ ,							add_downsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							downsample_padding=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							downsample_type=__magic_name__ ,							)
							self.down_blocks.append(__magic_name__							)

				# mid
				lowerCAmelCase__									=							UNetMidBlockaD(
				    in_channels=block_out_channels[-1] ,							temb_channels=__magic_name__ ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							output_scale_factor=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1] ,							resnet_groups=__magic_name__ ,							add_attention=__magic_name__ ,							)

				# up
				lowerCAmelCase__									=							list(reversed(__magic_name__							)							)
				lowerCAmelCase__									=							reversed_block_out_channels[0]
				for i, up_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							reversed_block_out_channels[i]
							lowerCAmelCase__									=							reversed_block_out_channels[min(i + 1 ,							len(__magic_name__							) - 1							)]

							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_up_block(
							    __magic_name__ ,							num_layers=layers_per_block + 1 ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							prev_output_channel=__magic_name__ ,							temb_channels=__magic_name__ ,							add_upsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							resnet_time_scale_shift=__magic_name__ ,							upsample_type=__magic_name__ ,							)
							self.up_blocks.append(__magic_name__							)
							lowerCAmelCase__									=							output_channel

				# out
				lowerCAmelCase__									=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							32							)
				lowerCAmelCase__									=							nn.GroupNorm(num_channels=block_out_channels[0] ,							num_groups=__magic_name__ ,							eps=__magic_name__							)
				lowerCAmelCase__									=							nn.SiLU()
				lowerCAmelCase__									=							nn.Convad(block_out_channels[0] ,							__magic_name__ ,							kernel_size=3 ,							padding=1							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    Union[torch.Tensor, float, int] ,							__magic_name__     :    Optional[torch.Tensor] = None ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				if self.config.center_input_sample:
							lowerCAmelCase__									=							2 * sample - 1.0

				# 1. time
				lowerCAmelCase__									=							timestep
				if not torch.is_tensor(__magic_name__							):
							lowerCAmelCase__									=							torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device							)
				elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
							lowerCAmelCase__									=							timesteps[None].to(sample.device							)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				lowerCAmelCase__									=							timesteps * torch.ones(sample.shape[0] ,							dtype=timesteps.dtype ,							device=timesteps.device							)

				lowerCAmelCase__									=							self.time_proj(__magic_name__							)

				# timesteps does not contain any weights and will always return f32 tensors
				# but time_embedding might actually be running in fp16. so we need to cast here.
				# there might be better ways to encapsulate this.
				lowerCAmelCase__									=							t_emb.to(dtype=self.dtype							)
				lowerCAmelCase__									=							self.time_embedding(__magic_name__							)

				if self.class_embedding is not None:
							if class_labels is None:
										raise ValueError("class_labels should be provided when doing class conditioning"							)

							if self.config.class_embed_type == "timestep":
										lowerCAmelCase__									=							self.time_proj(__magic_name__							)

							lowerCAmelCase__									=							self.class_embedding(__magic_name__							).to(dtype=self.dtype							)
							lowerCAmelCase__									=							emb + class_emb

				# 2. pre-process
				lowerCAmelCase__									=							sample
				lowerCAmelCase__									=							self.conv_in(__magic_name__							)

				# 3. down
				lowerCAmelCase__									=							(sample,)
				for downsample_block in self.down_blocks:
							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(
										    hidden_states=__magic_name__ ,							temb=__magic_name__ ,							skip_sample=__magic_name__							)
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(hidden_states=__magic_name__ ,							temb=__magic_name__							)

							down_block_res_samples += res_samples

				# 4. mid
				lowerCAmelCase__									=							self.mid_block(__magic_name__ ,							__magic_name__							)

				# 5. up
				lowerCAmelCase__									=							None
				for upsample_block in self.up_blocks:
							lowerCAmelCase__									=							down_block_res_samples[-len(upsample_block.resnets							) :]
							lowerCAmelCase__									=							down_block_res_samples[: -len(upsample_block.resnets							)]

							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							else:
										lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

        # 6. post-process
				lowerCAmelCase__									=							self.conv_norm_out(__magic_name__							)
				lowerCAmelCase__									=							self.conv_act(__magic_name__							)
				lowerCAmelCase__									=							self.conv_out(__magic_name__							)

				if skip_sample is not None:
							sample += skip_sample

				if self.config.time_embedding_type == "fourier":
							lowerCAmelCase__									=							timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
							lowerCAmelCase__									=							sample / timesteps

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__magic_name__							)





