




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  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      def __init__(       self   :	int	,    snake_case   :	Union[str, Any]	,    snake_case   :	Optional[int]	,    snake_case   :	Optional[Any]=False	,    snake_case   :	Optional[Any]=1024	,    snake_case   :	Optional[int]=1024	,    snake_case   :	int=3.6	,    snake_case   :	Optional[int]=False	,    ):
          __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   :	List[str]			):
          __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(snake_case			)[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(snake_case	,    truncation=snake_case			)['''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(snake_case			)	,    self.seq_length			):
                  __UpperCamelCase											=  all_token_ids[i : i + self.seq_length]
                  if len(snake_case			) == self.seq_length:
                      self.current_size += 1
                      yield torch.tensor(snake_case			)







      def  snake_case		(       self   :	Tuple	,    snake_case   :	List[str]=1000			):
          return ShufflerIterDataPipe(self	,    buffer_size=snake_case			)







def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Optional[int]:

    """simple docstring"""


    __UpperCamelCase											=  args.model_ckpt.split('''/''' )[-1]
    __UpperCamelCase											=  logging.getLogger(__name__ )
    __UpperCamelCase											=  Path(args.save_dir ) / '''log/'''
    log_dir.mkdir(exist_ok=lowercase_ )
    __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(lowercase_   ,       vars(lowercase_ ) )
        __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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Optional[Any]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"] )    ->		List[str]:

    """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(lowercase_ )
        else:
            params_with_wd.append(lowercase_ )
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Any:

    """simple docstring"""


    __UpperCamelCase											=  accelerator.unwrap_model(lowercase_ ).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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Tuple:

    """simple docstring"""


    model.eval()
    __UpperCamelCase											=  []
    for step, batch in enumerate(lowercase_ ):
        with torch.no_grad():
            __UpperCamelCase											=  model(lowercase_   ,       labels=lowercase_ )
        __UpperCamelCase											=  outputs.loss.repeat(args.valid_batch_size )
        losses.append(accelerator.gather(lowercase_ ) )
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    __UpperCamelCase											=  torch.cat(lowercase_ )
    __UpperCamelCase											=  losses[: eval_dataloader.dataset.current_size].mean()
    try:
        __UpperCamelCase											=  torch.exp(lowercase_ )
    except OverflowError:
        __UpperCamelCase											=  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    __SCREAMING_SNAKE_CASE				(					)    ->		Any:

    """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")












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 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   RePaintPipeline
      lowerCAmelCase__     :	Optional[int]      =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      lowerCAmelCase__     :	Tuple      =   PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      lowerCAmelCase__     :	List[str]      =   IMAGE_INPAINTING_BATCH_PARAMS
      lowerCAmelCase__     :	Dict      =   False







      def  snake_case		(       self   :	Optional[Any]			):
          torch.manual_seed(0			)
          torch.manual_seed(0			)
          __UpperCamelCase											=  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''')	,    )
          __UpperCamelCase											=  RePaintScheduler()
          __UpperCamelCase											=  {'''unet''': unet, '''scheduler''': scheduler}
          return components







      def  snake_case		(       self   :	int	,    snake_case   :	str	,    snake_case   :	List[Any]=0			):
          if str(snake_case			).startswith('''mps'''			):
              __UpperCamelCase											=  torch.manual_seed(snake_case			)
          else:
              __UpperCamelCase											=  torch.Generator(device=snake_case			).manual_seed(snake_case			)
          __UpperCamelCase											=  np.random.RandomState(snake_case			).standard_normal((1, 3, 32, 32)			)
          __UpperCamelCase											=  torch.from_numpy(snake_case			).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  (image > 0).to(device=snake_case	,    dtype=torch.floataa			)
          __UpperCamelCase											=  {
              '''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  snake_case		(       self   :	str			):
          __UpperCamelCase											=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCamelCase											=  self.get_dummy_components()
          __UpperCamelCase											=  RePaintPipeline(**snake_case			)
          __UpperCamelCase											=  sd_pipe.to(snake_case			)
          sd_pipe.set_progress_bar_config(disable=snake_case			)

          __UpperCamelCase											=  self.get_dummy_inputs(snake_case			)
          __UpperCamelCase											=  sd_pipe(**snake_case			).images
          __UpperCamelCase											=  image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCamelCase											=  np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]			)

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







      @skip_mps
      def  snake_case		(       self   :	str			):
          return super().test_save_load_local()







      @unittest.skip('''non-deterministic pipeline'''			)
      def  snake_case		(       self   :	Any			):
          return super().test_inference_batch_single_identical()







      @skip_mps
      def  snake_case		(       self   :	List[Any]			):
          return super().test_dict_tuple_outputs_equivalent()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_save_load_optional_components()







      @skip_mps
      def  snake_case		(       self   :	List[str]			):
          return super().test_attention_slicing_forward_pass()







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

      """simple docstring"""

      def  snake_case		(       self   :	Dict			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256.png'''			)
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''			)
          __UpperCamelCase											=  load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
              '''repaint/celeba_hq_256_result.npy'''			)

          __UpperCamelCase											=  '''google/ddpm-ema-celebahq-256'''
          __UpperCamelCase											=  UNetaDModel.from_pretrained(snake_case			)
          __UpperCamelCase											=  RePaintScheduler.from_pretrained(snake_case			)

          __UpperCamelCase											=  RePaintPipeline(unet=snake_case	,    scheduler=snake_case			).to(snake_case			)
          repaint.set_progress_bar_config(disable=snake_case			)
          repaint.enable_attention_slicing()

          __UpperCamelCase											=  torch.manual_seed(0			)
          __UpperCamelCase											=  repaint(
              snake_case	,    snake_case	,    num_inference_steps=250	,    eta=0.0	,    jump_length=10	,    jump_n_sample=10	,    generator=snake_case	,    output_type='''np'''	,    )
          __UpperCamelCase											=  output.images[0]

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






