

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    :	Optional[int]    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	List[str]=False    ,						UpperCAmelCase    :	Dict=1_0_2_4    ,						UpperCAmelCase    :	List[Any]=1_0_2_4    ,						UpperCAmelCase    :	str=3.6    ,						UpperCAmelCase    :	Dict=False    ,						)							->			List[Any]:
      __lowerCAmelCase:     Any     			=   tokenizer
      __lowerCAmelCase:     Union[str, Any]     			=   tokenizer.bos_token_id
      __lowerCAmelCase:     Any     			=   dataset
      __lowerCAmelCase:     Optional[Any]     			=   seq_length
      __lowerCAmelCase:     Optional[Any]     			=   0
      __lowerCAmelCase:     List[str]     			=   infinite
      __lowerCAmelCase:     Any     			=   0
      __lowerCAmelCase:     Union[str, Any]     			=   tokenized

      if self.tokenized:
       __lowerCAmelCase:     int     			=   seq_length * num_of_sequences
       __lowerCAmelCase:     List[Any]     			=   'input_ids'
      else:
       __lowerCAmelCase:     Union[str, Any]     			=   seq_length * chars_per_token * num_of_sequences
       __lowerCAmelCase:     Dict     			=   'content'


     def __iter__(							self    :	Dict      )							->			List[str]:
      __lowerCAmelCase:     List[str]     			=   iter(self.dataset      )
      __lowerCAmelCase:     Optional[Any]     			=   True
      while more_examples:
       __lowerCAmelCase			,							__lowerCAmelCase:     List[Any]     			=   [], 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:
          __lowerCAmelCase:     int     			=   iter(self.dataset      )
          self.epoch += 1
          logger.info(F'''Dataset epoch: {self.epoch}'''      )
         else:
          __lowerCAmelCase:     int     			=   False
          break
       if self.tokenized:
        __lowerCAmelCase:     Tuple     			=   buffer
       else:
        __lowerCAmelCase:     str     			=   self.tokenizer(UpperCAmelCase    ,						truncation=UpperCAmelCase      )['input_ids']
       __lowerCAmelCase:     Union[str, Any]     			=   []
       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      ):
        __lowerCAmelCase:     str     			=   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    :	int    ,						UpperCAmelCase    :	Any=1_0_0_0      )							->			Optional[int]:
      return ShufflerIterDataPipe(self    ,						buffer_size=UpperCAmelCase      )







def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					str:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]	)					->					str:

 """simple docstring"""






 __lowerCAmelCase:     str     			=   {'streaming': True}
 __lowerCAmelCase:     Dict     			=   load_dataset(args.dataset_name_train					,      split='train'					,      **SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[Any]     			=   train_data.shuffle(buffer_size=args.shuffle_buffer					,      seed=args.seed	)
 __lowerCAmelCase:     Optional[Any]     			=   load_dataset(args.dataset_name_valid					,      split='train'					,      **SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[Any]     			=   ConstantLengthDataset(
     SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      infinite=SCREAMING_SNAKE_CASE					,      seq_length=args.seq_length					,      tokenized=args.tokenized	)
 __lowerCAmelCase:     Optional[Any]     			=   ConstantLengthDataset(
     SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      infinite=SCREAMING_SNAKE_CASE					,      seq_length=args.seq_length					,      tokenized=args.tokenized	)
 __lowerCAmelCase:     Optional[int]     			=   train_dataset.shuffle(buffer_size=args.shuffle_buffer	)
 __lowerCAmelCase:     str     			=   DataLoader(SCREAMING_SNAKE_CASE					,      batch_size=args.train_batch_size					,      shuffle=SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[str]     			=   DataLoader(SCREAMING_SNAKE_CASE					,      batch_size=args.valid_batch_size	)
 return train_dataloader, eval_dataloader







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)					->					List[str]:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    Any	)					->					str:

 """simple docstring"""






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







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Any	)					->					Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   accelerator.unwrap_model(SCREAMING_SNAKE_CASE	).config
 __lowerCAmelCase:     List[str]     			=   4 if args.gradient_checkpointing else 3
 __lowerCAmelCase:     List[Any]     			=   args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
 __lowerCAmelCase:     str     			=   24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
 __lowerCAmelCase:     Optional[int]     			=   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))
 )
 __lowerCAmelCase:     int     			=   flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
 return tflops







def _a				( SCREAMING_SNAKE_CASE	:    List[Any]	)					->					int:

 """simple docstring"""






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


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

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

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

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

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

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

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

 """simple docstring"""






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


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

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








from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

      self.register_modules(vae=UpperCAmelCase    ,						text_encoder=UpperCAmelCase    ,						tokenizer=UpperCAmelCase    ,						unet=UpperCAmelCase    ,						scheduler=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

         __lowerCAmelCase:     List[Any]     			=   (mix_factor * latents) + (
             1 - mix_factor
         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
         __lowerCAmelCase:     Optional[Any]     			=   torch.cat([input] * 2      )

        else:  # content generation phase
         __lowerCAmelCase:     str     			=   torch.cat([latents] * 2      )

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





