





'''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_						(						lowerCAmelCase_						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=3.6     ,       _lowerCAmelCase=False     ,       )      ->							Tuple:
     _lowerCAmelCase		     = tokenizer
     _lowerCAmelCase		     = tokenizer.bos_token_id
     _lowerCAmelCase		     = dataset
     _lowerCAmelCase		     = seq_length
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = infinite
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = tokenized

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




 def __iter__(		self						)      ->							str:
     _lowerCAmelCase		     = iter(self.dataset						)
     _lowerCAmelCase		     = True
     while more_examples:
         _lowerCAmelCase		     = [], 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:
                     _lowerCAmelCase		     = iter(self.dataset						)
                     self.epoch += 1
                     logger.info(f'''Dataset epoch: {self.epoch}'''						)
                 else:
                     _lowerCAmelCase		     = False
                     break
         if self.tokenized:
             _lowerCAmelCase		     = buffer
         else:
             _lowerCAmelCase		     = self.tokenizer(_snake_case     ,       truncation=_snake_case						)['input_ids']
         _lowerCAmelCase		     = []
         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						):
             _lowerCAmelCase		     = 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     ,       _lowerCAmelCase=1000						)      ->							Optional[Any]:
     return ShufflerIterDataPipe(self     ,       buffer_size=_snake_case						)







def 		__a(SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = args.model_ckpt.split("/"      )[-1]
    _lowerCAmelCase		     = logging.getLogger(__name__      )
    _lowerCAmelCase		     = Path(args.save_dir      ) / 'log/'
    log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = 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		     = accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO      )
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        _lowerCAmelCase		     = ''
        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[Any]      ):



    '''simple docstring'''





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


def 		__a(SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):



    '''simple docstring'''





    _lowerCAmelCase		     = [], []
    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[Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]      ):



    '''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_					:   Any						,    SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Dict      ):



    '''simple docstring'''





    _lowerCAmelCase		     = accelerator.unwrap_model(SCREAMING_SNAKE_CASE_      ).config
    _lowerCAmelCase		     = 4 if args.gradient_checkpointing else 3
    _lowerCAmelCase		     = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    _lowerCAmelCase		     = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    _lowerCAmelCase		     = 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))
    )
    _lowerCAmelCase		     = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[Any]      ):



    '''simple docstring'''





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


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

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

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

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

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

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE			=							create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_SCREAMING_SNAKE_CASE			=							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_SCREAMING_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`.
_SCREAMING_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)
  _SCREAMING_SNAKE_CASE			=							os.path.basename(args.resume_from_checkpoint)
 else:
  # Get the most recent checkpoint
  _SCREAMING_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)
  _SCREAMING_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
 _SCREAMING_SNAKE_CASE			=							os.path.splitext(path)[0]
 _SCREAMING_SNAKE_CASE			=							int(training_difference.replace("step_", ""))

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


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							str    					=   "mt5"
 __lowerCamelCase  :							Dict    					=   ["past_key_values"]




 def __init__(		self     ,       _lowerCAmelCase=250112     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=64     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=8     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=6     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=128     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=1E-6     ,       _lowerCAmelCase=1.0     ,       _lowerCAmelCase="gated-gelu"     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase="T5Tokenizer"     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=0     ,       _lowerCAmelCase=1     ,       _lowerCAmelCase=0     ,       **_lowerCAmelCase     ,       )      ->							Optional[Any]:
     super().__init__(
         is_encoder_decoder=_lowerCAmelCase     ,       tokenizer_class=_lowerCAmelCase     ,       tie_word_embeddings=_lowerCAmelCase     ,       pad_token_id=_lowerCAmelCase     ,       eos_token_id=_lowerCAmelCase     ,       decoder_start_token_id=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = d_model
     _lowerCAmelCase		     = d_kv
     _lowerCAmelCase		     = d_ff
     _lowerCAmelCase		     = num_layers
     _lowerCAmelCase		     = (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase		     = num_heads
     _lowerCAmelCase		     = relative_attention_num_buckets
     _lowerCAmelCase		     = relative_attention_max_distance
     _lowerCAmelCase		     = dropout_rate
     _lowerCAmelCase		     = layer_norm_epsilon
     _lowerCAmelCase		     = initializer_factor
     _lowerCAmelCase		     = feed_forward_proj
     _lowerCAmelCase		     = use_cache

     _lowerCAmelCase		     = self.feed_forward_proj.split("-"						)
     _lowerCAmelCase		     = act_info[-1]
     _lowerCAmelCase		     = act_info[0] == "gated"

     if len(_lowerCAmelCase						) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase						) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
             "'gated-gelu' or 'relu'"						)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         _lowerCAmelCase		     = "gelu_new"




 @property
 def 							_snake_case				(		self						)      ->							Any:
     return self.d_model




 @property
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     return self.num_heads





 @property
 def 							_snake_case				(		self						)      ->							List[str]:
     return self.num_layers







class 				lowerCAmelCase_						(						__magic_name__						):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 							_snake_case				(		self						)      ->							Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase		     = {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase		     = "past_encoder_sequence + sequence"
         _lowerCAmelCase		     = {0: "batch"}
         _lowerCAmelCase		     = {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(_lowerCAmelCase     ,       direction="inputs"						)

     return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 							_snake_case				(		self						)      ->							int:
     return 13





 @property
 def 							_snake_case				(		self						)      ->							float:
     return 5E-4


