



"""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__		(   __UpperCamelCase  ):



		def __init__(			self	:     Union[str, Any]   ,				snake_case	:     Dict   ,				snake_case	:     List[str]   ,				snake_case	:     List[str]=False   ,				snake_case	:     Optional[int]=1_024   ,				snake_case	:     List[str]=1_024   ,				snake_case	:     int=3.6   ,				snake_case	:     Optional[Any]=False   ,				)      ->    Union[str, 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	:     List[str]					)      ->    Any:






									'''simple docstring'''

									A    =       iter(self.dataset					)
									A    =       True
									while more_examples:
																A    =       [], 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:
																																					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(UpperCAmelCase__   ,				truncation=UpperCAmelCase__					)["""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(UpperCAmelCase__					)   ,				self.seq_length					):
																							A    =       all_token_ids[i : i + self.seq_length]
																							if len(UpperCAmelCase__					) == self.seq_length:
																														self.current_size += 1
																														yield torch.tensor(UpperCAmelCase__					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict=1_000					)      ->    Dict:






									'''simple docstring'''

									return ShufflerIterDataPipe(self   ,				buffer_size=UpperCAmelCase__					)



def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Dict:
							A    =       args.model_ckpt.split('/'      )[-1]
							A    =       logging.getLogger(__name__      )
							A    =       Path(args.save_dir      ) / """log/"""
							log_dir.mkdir(exist_ok=lowerCamelCase__      )
							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(lowerCamelCase__					,   vars(lowerCamelCase__      )      )
														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 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Dict:
							A    =       {"""streaming""": True}
							A    =       load_dataset(args.dataset_name_train					,   split='train'					,   **lowerCamelCase__      )
							A    =       train_data.shuffle(buffer_size=args.shuffle_buffer					,   seed=args.seed      )
							A    =       load_dataset(args.dataset_name_valid					,   split='train'					,   **lowerCamelCase__      )
							A    =       ConstantLengthDataset(
							    lowerCamelCase__					,   lowerCamelCase__					,   infinite=lowerCamelCase__					,   seq_length=args.seq_length					,   tokenized=args.tokenized      )
							A    =       ConstantLengthDataset(
							    lowerCamelCase__					,   lowerCamelCase__					,   infinite=lowerCamelCase__					,   seq_length=args.seq_length					,   tokenized=args.tokenized      )
							A    =       train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
							A    =       DataLoader(lowerCamelCase__					,   batch_size=args.train_batch_size					,   shuffle=lowerCamelCase__      )
							A    =       DataLoader(lowerCamelCase__					,   batch_size=args.valid_batch_size      )
							return train_dataloader, eval_dataloader

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      )			->				Any:
							A    =       [], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay      ):
																					params_without_wd.append(lowerCamelCase__      )
														else:
																					params_with_wd.append(lowerCamelCase__      )
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				Tuple:
							logger.info(f"""Step {step}: {metrics}"""      )
							if accelerator.is_main_process:
														accelerator.log(lowerCamelCase__					,   lowerCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Union[str, Any]:
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							A    =       accelerator.unwrap_model(lowerCamelCase__      ).config
							A    =       4 if args.gradient_checkpointing else 3
							A    =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							A    =       24 * 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 * (10**12))
							return tflops

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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a 			=							{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a 			=							[
     'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTV2ForImageClassification',
     'MobileViTV2ForSemanticSegmentation',
     'MobileViTV2Model',
     'MobileViTV2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 a 			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


