





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowercase     :			Optional[int]     						=	16
lowercase     :			Optional[Any]     						=	32


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase = 16       ):
       _UpperCamelCase														=    AutoTokenizer.from_pretrained('''bert-base-cased'''       )
       _UpperCamelCase														=    load_dataset('''glue'''     ,							'''mrpc'''       )

       def tokenize_function(lowerCAmelCase       ):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCamelCase														=    tokenizer(examples['''sentence1''']     ,							examples['''sentence2''']     ,							truncation=lowerCamelCase__     ,							max_length=lowerCamelCase__       )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              _UpperCamelCase														=    datasets.map(
                  lowerCamelCase__     ,							batched=lowerCamelCase__     ,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,							)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _UpperCamelCase														=    tokenized_datasets.rename_column('''label'''     ,							'''labels'''       )

       def collate_fn(lowerCAmelCase       ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _UpperCamelCase														=    128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _UpperCamelCase														=    16
              elif accelerator.mixed_precision != "no":
                     _UpperCamelCase														=    8
              else:
                     _UpperCamelCase														=    None

              return tokenizer.pad(
                  lowerCamelCase__     ,							padding='''longest'''     ,							max_length=lowerCamelCase__     ,							pad_to_multiple_of=lowerCamelCase__     ,							return_tensors='''pt'''     ,							)

       # Instantiate dataloaders.
       _UpperCamelCase														=    DataLoader(
           tokenized_datasets['''train''']     ,							shuffle=lowerCamelCase__     ,							collate_fn=lowerCamelCase__     ,							batch_size=lowerCamelCase__       )
       _UpperCamelCase														=    DataLoader(
           tokenized_datasets['''validation''']     ,							shuffle=lowerCamelCase__     ,							collate_fn=lowerCamelCase__     ,							batch_size=lowerCamelCase__       )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       lowercase     :			Dict     						=	mocked_dataloaders  # noqa: F811


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,							lowerCamelCase__       ) == "1":
              _UpperCamelCase														=    2
       # Initialize accelerator
       _UpperCamelCase														=    Accelerator(cpu=args.cpu     ,							mixed_precision=args.mixed_precision       )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _UpperCamelCase														=    config['''lr''']
       _UpperCamelCase														=    int(config['''num_epochs''']       )
       _UpperCamelCase														=    int(config['''seed''']       )
       _UpperCamelCase														=    int(config['''batch_size''']       )

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps     ,							'''isdigit'''       ):
              if args.checkpointing_steps == "epoch":
                     _UpperCamelCase														=    args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     _UpperCamelCase														=    int(args.checkpointing_steps       )
              else:
                     raise ValueError(
                         f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''       )
       else:
              _UpperCamelCase														=    None

       set_seed(lowerCamelCase__       )

       _UpperCamelCase							,		_UpperCamelCase														=    get_dataloaders(lowerCamelCase__     ,							lowerCamelCase__       )
       _UpperCamelCase														=    evaluate.load('''glue'''     ,							'''mrpc'''       )

       # If the batch size is too big we use gradient accumulation
       _UpperCamelCase														=    1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _UpperCamelCase														=    batch_size // MAX_GPU_BATCH_SIZE
              _UpperCamelCase														=    MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCamelCase														=    AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,							return_dict=lowerCamelCase__       )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       _UpperCamelCase														=    model.to(accelerator.device       )

       # Instantiate optimizer
       _UpperCamelCase														=    AdamW(params=model.parameters()     ,							lr=lowerCamelCase__       )

       # Instantiate scheduler
       _UpperCamelCase														=    get_linear_schedule_with_warmup(
           optimizer=lowerCamelCase__     ,							num_warmup_steps=100     ,							num_training_steps=(len(lowerCamelCase__       ) * num_epochs) // gradient_accumulation_steps     ,							)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    accelerator.prepare(
           lowerCamelCase__     ,							lowerCamelCase__     ,							lowerCamelCase__     ,							lowerCamelCase__     ,							lowerCamelCase__       )

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       _UpperCamelCase														=    0
       # We also need to keep track of the stating epoch so files are named properly
       _UpperCamelCase														=    0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       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       )
                     _UpperCamelCase														=    os.path.basename(args.resume_from_checkpoint       )
              else:
                     # Get the most recent checkpoint
                     _UpperCamelCase														=    [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime       )
                     _UpperCamelCase														=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              _UpperCamelCase														=    os.path.splitext(lowerCamelCase__       )[0]

              if "epoch" in training_difference:
                     _UpperCamelCase														=    int(training_difference.replace('''epoch_'''     ,							''''''       )       ) + 1
                     _UpperCamelCase														=    None
              else:
                     _UpperCamelCase														=    int(training_difference.replace('''step_'''     ,							''''''       )       )
                     _UpperCamelCase														=    resume_step // len(lowerCamelCase__       )
                     resume_step -= starting_epoch * len(lowerCamelCase__       )

    # Now we train the model
       for epoch in range(lowerCamelCase__     ,							lowerCamelCase__       ):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     _UpperCamelCase														=    accelerator.skip_first_batches(lowerCamelCase__     ,							lowerCamelCase__       )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     _UpperCamelCase														=    train_dataloader
              for step, batch in enumerate(lowerCamelCase__       ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device       )
                     _UpperCamelCase														=    model(**lowerCamelCase__       )
                     _UpperCamelCase														=    outputs.loss
                     _UpperCamelCase														=    loss / gradient_accumulation_steps
                     accelerator.backward(lowerCamelCase__       )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(lowerCamelCase__     ,							lowerCamelCase__       ):
                            _UpperCamelCase														=    f'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          _UpperCamelCase														=    os.path.join(args.output_dir     ,							lowerCamelCase__       )
                                   accelerator.save_state(lowerCamelCase__       )

              model.eval()
              for step, batch in enumerate(lowerCamelCase__       ):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device       )
                     with torch.no_grad():
                            _UpperCamelCase														=    model(**lowerCamelCase__       )
                     _UpperCamelCase														=    outputs.logits.argmax(dim=-1       )
                     _UpperCamelCase							,		_UpperCamelCase														=    accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
                     metric.add_batch(
                         predictions=lowerCamelCase__     ,							references=lowerCamelCase__     ,							)

              _UpperCamelCase														=    metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'''epoch {epoch}:'''     ,							lowerCamelCase__       )

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     _UpperCamelCase														=    f'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            _UpperCamelCase														=    os.path.join(args.output_dir     ,							lowerCamelCase__       )
                     accelerator.save_state(lowerCamelCase__       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    argparse.ArgumentParser(description='''Simple example of training script.'''       )
       parser.add_argument(
           '''--mixed_precision'''     ,							type=lowerCamelCase__     ,							default=lowerCamelCase__     ,							choices=['''no''', '''fp16''', '''bf16''', '''fp8''']     ,							help='''Whether to use mixed precision. Choose'''
           '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
           '''and an Nvidia Ampere GPU.'''     ,							)
       parser.add_argument('''--cpu'''     ,							action='''store_true'''     ,							help='''If passed, will train on the CPU.'''       )
       parser.add_argument(
           '''--checkpointing_steps'''     ,							type=lowerCamelCase__     ,							default=lowerCamelCase__     ,							help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''     ,							)
       parser.add_argument(
           '''--output_dir'''     ,							type=lowerCamelCase__     ,							default='''.'''     ,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''     ,							)
       parser.add_argument(
           '''--resume_from_checkpoint'''     ,							type=lowerCamelCase__     ,							default=lowerCamelCase__     ,							help='''If the training should continue from a checkpoint folder.'''     ,							)
       _UpperCamelCase														=    parser.parse_args()
       _UpperCamelCase														=    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowerCamelCase__     ,							lowerCamelCase__       )


if __name__ == "__main__":
       main()











"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


A     =      logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase__		:
		lowerCAmelCase_							:						str							=  field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						bool							=  field(default=UpperCamelCase ,metadata={"""help""": """Set this flag to use fast tokenization."""}  )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)



def 	lowerCAmelCase__      (  )			->				Optional[Any]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							A    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

							if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														A   ,	A   ,	A    =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
							else:
														A   ,	A   ,	A    =       parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir      )
							    and os.listdir(training_args.output_dir      )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'      )

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,   datefmt='%m/%d/%Y %H:%M:%S'					,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,   )
							logger.warning(
							    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,   training_args.local_rank					,   training_args.device					,   training_args.n_gpu					,   bool(training_args.local_rank != -1      )					,   training_args.fpaa					,   )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank      ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'					,   lowerCamelCase__      )

							# Prepare Question-Answering task
							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							A    =       AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   )
							A    =       AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   use_fast=lowerCamelCase__					,   )
							A    =       AutoModelForQuestionAnswering.from_pretrained(
							    model_args.model_name_or_path					,   from_tf=bool('.ckpt' in model_args.model_name_or_path      )					,   config=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )

							# Get datasets
							A    =       hasattr(model.config					,   'lang2id'      )
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir      )
							    if training_args.do_train
							    else None
							)
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   mode='dev'					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )
							    if training_args.do_eval
							    else None
							)

							# Data collator
							A    =       DataCollatorWithPadding(lowerCamelCase__					,   pad_to_multiple_of=8      ) if training_args.fpaa else None

							# Initialize our Trainer
							A    =       Trainer(
							    model=lowerCamelCase__					,   args=lowerCamelCase__					,   train_dataset=lowerCamelCase__					,   eval_dataset=lowerCamelCase__					,   data_collator=lowerCamelCase__					,   )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir      )

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()



