



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_  (		_snake_case				):

						def __init__(    self	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Union[str, Any]   ,						_lowercase	:Tuple=False   ,						_lowercase	:Tuple=1024   ,						_lowercase	:Union[str, Any]=1024   ,						_lowercase	:List[Any]=3.6   ,						_lowercase	:Optional[Any]=False   ,						)		->					List[Any]:
												UpperCAmelCase_     							=     tokenizer
												UpperCAmelCase_     							=     tokenizer.bos_token_id
												UpperCAmelCase_     							=     dataset
												UpperCAmelCase_     							=     seq_length
												UpperCAmelCase_     							=     0
												UpperCAmelCase_     							=     infinite
												UpperCAmelCase_     							=     0
												UpperCAmelCase_     							=     tokenized

												if self.tokenized:
																		UpperCAmelCase_     							=     seq_length * num_of_sequences
																		UpperCAmelCase_     							=     '''input_ids'''
												else:
																		UpperCAmelCase_     							=     seq_length * chars_per_token * num_of_sequences
																		UpperCAmelCase_     							=     '''content'''

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




						def 	__a       (    self	:Dict   ,						_lowercase	:Any=1000)		->					Dict:
												return ShufflerIterDataPipe(self   ,						buffer_size=_lowercase)







def 				A			(		__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     args.model_ckpt.split('''/'''	)[-1]
						UpperCAmelCase_     							=     logging.getLogger(__name__	)
						UpperCAmelCase_     							=     Path(args.save_dir	) / '''log/'''
						log_dir.mkdir(exist_ok=__UpperCAmelCase	)
						UpperCAmelCase_     							=     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	)	)
												UpperCAmelCase_     							=     accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO	)
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												UpperCAmelCase_     							=     ''''''
												logger.setLevel(logging.ERROR	)
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name







def 				A			(		__UpperCAmelCase	)       ->       Any:



						'''simple docstring'''






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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_							,      UpperCAmelCase_     							=     [], []
						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							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Optional[Any]:



						'''simple docstring'''






						UpperCAmelCase_     							=     accelerator.unwrap_model(__UpperCAmelCase	).config
						UpperCAmelCase_     							=     4 if args.gradient_checkpointing else 3
						UpperCAmelCase_     							=     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						UpperCAmelCase_     							=     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						UpperCAmelCase_     							=     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))
						)
						UpperCAmelCase_     							=     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def 				A			(		__UpperCAmelCase	)       ->       str:



						'''simple docstring'''






						model.eval()
						UpperCAmelCase_     							=     []
						for step, batch in enumerate(__UpperCAmelCase	):
												with torch.no_grad():
																		UpperCAmelCase_     							=     model(__UpperCAmelCase							,					labels=__UpperCAmelCase	)
												UpperCAmelCase_     							=     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
						UpperCAmelCase_     							=     torch.cat(__UpperCAmelCase	)
						UpperCAmelCase_     							=     losses[: eval_dataloader.dataset.current_size].mean()
						try:
												UpperCAmelCase_     							=     torch.exp(__UpperCAmelCase	)
						except OverflowError:
												UpperCAmelCase_     							=     float('''inf'''	)
						return loss.item(), perplexity.item()


# Settings
UpperCamelCase_	       =   HfArgumentParser(TrainingArguments)
UpperCamelCase_	       =   parser.parse_args()

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

UpperCamelCase_	       =   Namespace(**vars(args), **acc_state)
UpperCamelCase_	       =   accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
UpperCamelCase_       ,			UpperCamelCase_	       =   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_	       =   AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
							model.gradient_checkpointing_enable()
UpperCamelCase_	       =   AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCamelCase_       ,			UpperCamelCase_	       =   create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCamelCase_	       =   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCamelCase_	       =   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			(		)       ->       Optional[Any]:



						'''simple docstring'''






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


# Prepare everything with our `accelerator`.
UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_       ,			UpperCamelCase_	       =   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_	       =   os.path.basename(args.resume_from_checkpoint)
							else:
														# Get the most recent checkpoint
														UpperCamelCase_	       =   [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_	       =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
							# Extract the step of the checkpoint to continue from there
							UpperCamelCase_	       =   os.path.splitext(path)[0]
							UpperCamelCase_	       =   int(training_difference.replace("step_", ""))

# Train model
model.train()
UpperCamelCase_	       =   0
UpperCamelCase_	       =   time.time()
UpperCamelCase_	       =   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_	       =   model(batch, labels=batch, use_cache=False).loss
							UpperCamelCase_	       =   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_	       =   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_	       =   get_lr()
														accelerator.backward(loss)
														accelerator.clip_grad_norm_(model.parameters(), 1.0)
														optimizer.step()
														lr_scheduler.step()
														optimizer.zero_grad()
														UpperCamelCase_	       =   time.time() - t_start
														UpperCamelCase_	       =   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_	       =   time.time()
														UpperCamelCase_	       =   0
														completed_steps += 1
							if step % args.save_checkpoint_steps == 0:
														logger.info("Evaluating and saving model checkpoint")
														UpperCamelCase_       ,			UpperCamelCase_	       =   evaluate(args)
														log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
														accelerator.wait_for_everyone()
														UpperCamelCase_	       =   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_	       =   evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
UpperCamelCase_	       =   accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
UpperCamelCase_	       =   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"""

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	RePaintPipeline
	lowercase_        =	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	lowercase_        =	PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	lowercase_        =	IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			lowerCamelCase     :			Optional[Any]		=    UNetaDModel(
			    block_out_channels=(32, 64)		, layers_per_block=2		, sample_size=32		, in_channels=3		, out_channels=3		, down_block_types=('DownBlock2D', 'AttnDownBlock2D')		, up_block_types=('AttnUpBlock2D', 'UpBlock2D')		, )
			lowerCamelCase     :			Union[str, Any]		=    RePaintScheduler()
			lowerCamelCase     :			Dict		=    {'unet': unet, 'scheduler': scheduler}
			return components




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=0					)		->							Optional[int]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			str		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Tuple		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			lowerCamelCase     :			str		=    np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			lowerCamelCase     :			Tuple		=    torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[int]		=    (image > 0).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[Any]		=    {
			    'image': image,
			    'mask_image': mask,
			    'generator': generator,
			    'num_inference_steps': 5,
			    'eta': 0.0,
			    'jump_length': 2,
			    'jump_n_sample': 2,
			    'output_type': 'numpy',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Any		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			Union[str, Any]		=    self.get_dummy_components()
			lowerCamelCase     :			List[Any]		=    RePaintPipeline(**UpperCAmelCase_					)
			lowerCamelCase     :			int		=    sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    self.get_dummy_inputs(UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    sd_pipe(**UpperCAmelCase_					).images
			lowerCamelCase     :			List[Any]		=    image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase     :			Optional[Any]		=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return super().test_save_load_local()




	@unittest.skip('non-deterministic pipeline'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_inference_batch_single_identical()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			return super().test_save_load_optional_components()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class        _lowercase   (				unittest.TestCase		):




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256.png'					)
			lowerCamelCase     :			str		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'					)
			lowerCamelCase     :			Optional[Any]		=    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256_result.npy'					)

			lowerCamelCase     :			Any		=    'google/ddpm-ema-celebahq-256'
			lowerCamelCase     :			List[Any]		=    UNetaDModel.from_pretrained(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    RePaintPipeline(unet=UpperCAmelCase_		, scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)
			lowerCamelCase     :			str		=    repaint(
			    UpperCAmelCase_		, UpperCAmelCase_		, num_inference_steps=250		, eta=0.0		, jump_length=10		, jump_n_sample=10		, generator=UpperCAmelCase_		, output_type='np'		, )
			lowerCamelCase     :			Optional[int]		=    output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2






