code: 
  
  
  '''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  UpperCAmelCase (       lowerCAmelCase_):
  
   def __init__( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	Optional[Any]=False,					a_ :	Optional[Any]=1024,					a_ :	List[Any]=1024,					a_ :	Dict=3.6,					a_ :	Tuple=False,					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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 :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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(lowerCamelCase_					)[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(lowerCamelCase_,					truncation=lowerCamelCase_					)["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(lowerCamelCase_					),					self.seq_length					):
            UpperCamelCase__ 						=						all_token_ids[i : i + self.seq_length]
            if len(lowerCamelCase_					) == self.seq_length:
               self.current_size += 1
               yield torch.tensor(lowerCamelCase_					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	Optional[Any]=1000					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return ShufflerIterDataPipe(self,					buffer_size=lowerCamelCase_					)
  
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Dict   )		->					Dict:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						args.model_ckpt.split("/"   )[-1]
     UpperCamelCase__ 						=						logging.getLogger(__name__   )
     UpperCamelCase__ 						=						Path(args.save_dir   ) / "log/"
     log_dir.mkdir(exist_ok=a_   )
     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(a_    ,		vars(a_   )   )
        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__(		_UpperCamelCase      :				Tuple   )		->					Dict:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						{"streaming": True}
     UpperCamelCase__ 						=						load_dataset(args.dataset_name_train    ,		split="train"    ,		**a_   )
     UpperCamelCase__ 						=						train_data.shuffle(buffer_size=args.shuffle_buffer    ,		seed=args.seed   )
     UpperCamelCase__ 						=						load_dataset(args.dataset_name_valid    ,		split="train"    ,		**a_   )
     UpperCamelCase__ 						=						ConstantLengthDataset(
         a_    ,		a_    ,		infinite=a_    ,		seq_length=args.seq_length    ,		tokenized=args.tokenized   )
     UpperCamelCase__ 						=						ConstantLengthDataset(
         a_    ,		a_    ,		infinite=a_    ,		seq_length=args.seq_length    ,		tokenized=args.tokenized   )
     UpperCamelCase__ 						=						train_dataset.shuffle(buffer_size=args.shuffle_buffer   )
     UpperCamelCase__ 						=						DataLoader(a_    ,		batch_size=args.train_batch_size    ,		shuffle=a_   )
     UpperCamelCase__ 						=						DataLoader(a_    ,		batch_size=args.valid_batch_size   )
     return train_dataloader, eval_dataloader
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   )		->					Dict:
  
  
  
  
  
     '''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(a_   )
        else:
           params_with_wd.append(a_   )
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				List[str]   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     logger.info(F'Step {step}: {metrics}'   )
     if accelerator.is_main_process:
        accelerator.log(a_    ,		a_   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				List[str]   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						accelerator.unwrap_model(a_   ).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 / (1_6.0 * 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__(		_UpperCamelCase      :				List[Any]   )		->					Dict:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     model.eval()
     UpperCamelCase__ 						=						[]
     for step, batch in enumerate(a_   ):
        with torch.no_grad():
           UpperCamelCase__ 						=						model(a_    ,		labels=a_   )
        UpperCamelCase__ 						=						outputs.loss.repeat(args.valid_batch_size   )
        losses.append(accelerator.gather(a_   )   )
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
           break
     UpperCamelCase__ 						=						torch.cat(a_   )
     UpperCamelCase__ 						=						losses[: eval_dataloader.dataset.current_size].mean()
     try:
        UpperCamelCase__ 						=						torch.exp(a_   )
     except OverflowError:
        UpperCamelCase__ 						=						float("inf"   )
     return loss.item(), perplexity.item()
  
  
  # Settings
  __lowercase:		Optional[int]							=     HfArgumentParser(TrainingArguments)
  __lowercase:		Union[str, Any]							=     parser.parse_args()
  
  # Accelerator
  __lowercase:		Any							=     ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
  __lowercase:		Optional[int]							=     Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
  __lowercase:		str							=     {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  __lowercase:		Optional[Any]							=     Namespace(**vars(args), **acc_state)
  __lowercase:		Dict							=     accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
         __lowercase:		Optional[Any]							=     Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  __lowercase	,__lowercase:		Optional[int]							=     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
  __lowercase:		str							=     AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
         model.gradient_checkpointing_enable()
  __lowercase:		Optional[Any]							=     AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  __lowercase	,__lowercase:		Optional[Any]							=     create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  __lowercase:		Union[str, Any]							=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  __lowercase:		Any							=     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__(		)		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  __lowercase	,__lowercase	,__lowercase	,__lowercase:		Optional[int]							=     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)
                __lowercase:		Any							=     os.path.basename(args.resume_from_checkpoint)
         else:
                # Get the most recent checkpoint
                __lowercase:		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)
                __lowercase:		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
         __lowercase:		Optional[int]							=     os.path.splitext(path)[0]
         __lowercase:		List[str]							=     int(training_difference.replace("step_", ""))
  
  # Train model
  model.train()
  __lowercase:		Any							=     0
  __lowercase:		Optional[Any]							=     time.time()
  __lowercase:		Optional[Any]							=     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
         __lowercase:		Union[str, Any]							=     model(batch, labels=batch, use_cache=False).loss
         __lowercase:		Union[str, Any]							=     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()})
         __lowercase:		List[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:
                __lowercase:		Union[str, Any]							=     get_lr()
                accelerator.backward(loss)
                accelerator.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
                __lowercase:		Dict							=     time.time() - t_start
                __lowercase:		int							=     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,
                    },
                )
                __lowercase:		Optional[int]							=     time.time()
                __lowercase:		str							=     0
                completed_steps += 1
         if step % args.save_checkpoint_steps == 0:
                logger.info("Evaluating and saving model checkpoint")
                __lowercase	,__lowercase:		List[Any]							=     evaluate(args)
                log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
                accelerator.wait_for_everyone()
                __lowercase:		Optional[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=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")
  __lowercase	,__lowercase:		Any							=     evaluate(args)
  log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
  accelerator.wait_for_everyone()
  __lowercase:		int							=     accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  __lowercase:		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="final model")
style_context: 
  
  
  '''simple docstring'''
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  
  @require_vision
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						tempfile.mkdtemp()
  
      UpperCamelCase__ 						=						CLIPImageProcessor()
      UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)
  
      UpperCamelCase__ 						=						GitProcessor(a_,					a_					)
  
      processor.save_pretrained(self.tmpdirname					)
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      shutil.rmtree(self.tmpdirname					)
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]
  
      UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      return image_inputs
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)
  
      UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
      UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)
  
      UpperCamelCase__ 						=						GitProcessor.from_pretrained(
          self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)
  
      self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer,					a_					)
  
      self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor,					a_					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
      UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)
  
      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
  
      UpperCamelCase__ 						=						processor(text=a_					)
  
      UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)
  
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
  
      # test if it raises when no input is passed
      with pytest.raises(a_					):
         processor()
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
      UpperCamelCase__ 						=						processor.batch_decode(a_					)
      UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)
  
      self.assertListEqual(a_,					a_					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)