



'''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						(		__a):


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_=False				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_=3.6				,	lowerCAmelCase_=False				,	)			->   Optional[Any]:



      """simple docstring"""







      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]:



      """simple docstring"""







      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(lowerCAmelCase_)[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(lowerCAmelCase_				,	truncation=lowerCAmelCase_)["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(lowerCAmelCase_)				,	self.seq_length):
                a_									=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				,	lowerCAmelCase_=1_0_0_0)			->   Dict:



      """simple docstring"""







      return ShufflerIterDataPipe(self				,	buffer_size=lowerCAmelCase_)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.model_ckpt.split("/"							)[-1]
     a_									=logging.getLogger(__name__							)
     a_									=Path(args.save_dir							) / "log/"
     log_dir.mkdir(exist_ok=lowercase__							)
     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(lowercase__							,						vars(lowercase__							)							)
          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 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__=["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(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 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







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






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=accelerator.unwrap_model(lowercase__							).config
     a_									=4 if args.gradient_checkpointing else 3
     a_									=args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     a_									=2_4 * 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 / (16.0 * config_model.n_layer * config_model.n_embd))
     )
     a_									=flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
     return tflops






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







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


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

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

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

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

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

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

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


     '''simple docstring'''







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


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

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


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase											=      logging.get_logger(__name__)

lowercase											=      {
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 __magic_name__       :			str	=   "gpt2"
 __magic_name__       :			str	=   ["past_key_values"]
 __magic_name__       :			Dict	=   {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__( self				,	lowerCAmelCase_=5_0_2_5_7				,	lowerCAmelCase_=1_0_2_4				,	lowerCAmelCase_=7_6_8				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=1_2				,	lowerCAmelCase_=None				,	lowerCAmelCase_="gelu_new"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=1e-5				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_="cls_index"				,	lowerCAmelCase_=True				,	lowerCAmelCase_=None				,	lowerCAmelCase_=True				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=5_0_2_5_6				,	lowerCAmelCase_=5_0_2_5_6				,	lowerCAmelCase_=False				,	lowerCAmelCase_=False				,	**lowerCAmelCase_				,	)			->   Dict:



      """simple docstring"""







      a_									=vocab_size
      a_									=n_positions
      a_									=n_embd
      a_									=n_layer
      a_									=n_head
      a_									=n_inner
      a_									=activation_function
      a_									=resid_pdrop
      a_									=embd_pdrop
      a_									=attn_pdrop
      a_									=layer_norm_epsilon
      a_									=initializer_range
      a_									=summary_type
      a_									=summary_use_proj
      a_									=summary_activation
      a_									=summary_first_dropout
      a_									=summary_proj_to_labels
      a_									=scale_attn_weights
      a_									=use_cache
      a_									=scale_attn_by_inverse_layer_idx
      a_									=reorder_and_upcast_attn

      a_									=bos_token_id
      a_									=eos_token_id

      super().__init__(bos_token_id=lowerCAmelCase_				,	eos_token_id=lowerCAmelCase_				,	**lowerCAmelCase_)







class 						UpperCAmelCase						(		__a):


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = "default"				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = False				,	)			->   List[str]:



      """simple docstring"""







      super().__init__(lowerCAmelCase_				,	task=lowerCAmelCase_				,	patching_specs=lowerCAmelCase_				,	use_past=lowerCAmelCase_)
      if not getattr(self._config				,	"pad_token_id"				,	lowerCAmelCase_):
           # TODO: how to do that better?
           a_									=0




 @property
 def    lowercase_	( self)			->   Mapping[str, Mapping[int, str]]:



      """simple docstring"""







      a_									=OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
      if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase_				,	direction="inputs")
           a_									={0: "batch", 1: "past_sequence + sequence"}
      else:
           a_									={0: "batch", 1: "sequence"}

      return common_inputs




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return self._config.n_layer




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return self._config.n_head




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_ = -1				,	lowerCAmelCase_ = -1				,	lowerCAmelCase_ = False				,	lowerCAmelCase_ = None				,	)			->   Mapping[str, Any]:



      """simple docstring"""







      a_									=super(lowerCAmelCase_				,	self).generate_dummy_inputs(
          lowerCAmelCase_				,	batch_size=lowerCAmelCase_				,	seq_length=lowerCAmelCase_				,	is_pair=lowerCAmelCase_				,	framework=lowerCAmelCase_)

      # We need to order the input in the way they appears in the forward()
      a_									=OrderedDict({"input_ids": common_inputs["input_ids"]})

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
           else:
                import torch

                a_		,    a_									=common_inputs["input_ids"].shape
                # Not using the same length for past_key_values
                a_									=seqlen + 2
                a_									=(
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                a_									=[
                    (torch.zeros(lowerCAmelCase_), torch.zeros(lowerCAmelCase_)) for _ in range(self.num_layers)
                ]

      a_									=common_inputs["attention_mask"]
      if self.use_past:
           a_									=ordered_inputs["attention_mask"].dtype
           a_									=torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase_				,	lowerCAmelCase_				,	dtype=lowerCAmelCase_)]				,	dim=1)

      return ordered_inputs




 @property
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      return 1_3


