


"""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      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=1024     ,					__UpperCAmelCase=1024     ,					__UpperCAmelCase=3.6     ,					__UpperCAmelCase=False     ,					)							->				List[Any]:
          _a     						=					tokenizer
          _a     						=					tokenizer.bos_token_id
          _a     						=					dataset
          _a     						=					seq_length
          _a     						=					0
          _a     						=					infinite
          _a     						=					0
          _a     						=					tokenized

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


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





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase=1000				)							->				Any:
          return ShufflerIterDataPipe(self     ,					buffer_size=__UpperCAmelCase				)







def    A_ ( _lowerCAmelCase  :       str							):






     """simple docstring"""

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






     """simple docstring"""

     _a     						=					{'''streaming''': True}
     _a     						=					load_dataset(args.dataset_name_train,  split='''train''',  **_lowerCAmelCase							)
     _a     						=					train_data.shuffle(buffer_size=args.shuffle_buffer,  seed=args.seed							)
     _a     						=					load_dataset(args.dataset_name_valid,  split='''train''',  **_lowerCAmelCase							)
     _a     						=					ConstantLengthDataset(
         _lowerCAmelCase,  _lowerCAmelCase,  infinite=_lowerCAmelCase,  seq_length=args.seq_length,  tokenized=args.tokenized							)
     _a     						=					ConstantLengthDataset(
         _lowerCAmelCase,  _lowerCAmelCase,  infinite=_lowerCAmelCase,  seq_length=args.seq_length,  tokenized=args.tokenized							)
     _a     						=					train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
     _a     						=					DataLoader(_lowerCAmelCase,  batch_size=args.train_batch_size,  shuffle=_lowerCAmelCase							)
     _a     						=					DataLoader(_lowerCAmelCase,  batch_size=args.valid_batch_size							)
     return train_dataloader, eval_dataloader
def    A_ ( _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Optional[Any],  _lowerCAmelCase  :       Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):






     """simple docstring"""

     _a						,   _a     						=					[], []
     for n, p in model.named_parameters():
          if any(nd in n for nd in no_decay							):
               params_without_wd.append(_lowerCAmelCase							)
          else:
               params_with_wd.append(_lowerCAmelCase							)
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]
def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     logger.info(f'Step {step}: {metrics}'							)
     if accelerator.is_main_process:
          accelerator.log(_lowerCAmelCase,  _lowerCAmelCase							)
def    A_ ( _lowerCAmelCase  :       List[Any],  _lowerCAmelCase  :       Any,  _lowerCAmelCase  :       Union[str, Any]							):






     """simple docstring"""

     _a     						=					accelerator.unwrap_model(_lowerCAmelCase							).config
     _a     						=					4 if args.gradient_checkpointing else 3
     _a     						=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     _a     						=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     _a     						=					factor * (
         1.0
         + (args.seq_length / (6.0 * config_model.n_embd))
         + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
     )
     _a     						=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops
def    A_ ( _lowerCAmelCase  :       Optional[int]							):






     """simple docstring"""

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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
__snake_case												=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__snake_case												=							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_ ( ):






     """simple docstring"""

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


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

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

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case												=							False


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

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCAmelCase				)
               _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase				)

          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					generator.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          assert np.abs(image - new_image				).sum() < 1e-5, "Models don't have the same forward pass"





     def 				_UpperCAmelCase   (   self				)							->				Dict:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					'''cyberpunk 2077'''
          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt=__UpperCAmelCase     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=50     ,					output_type='''numpy'''     ,					).images

          _a     						=					image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a     						=					np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]				)

          assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2