
import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
       from tests.trainer.test_trainer import (  # noqa
           RegressionModelConfig,
           RegressionPreTrainedModel,
           get_regression_trainer,
       )


set_seed(42)

_UpperCamelCase:					List[Any]     =get_tests_dir('fixtures')
_UpperCamelCase:					Any     =os.path.join(dirname(get_tests_dir()))
_UpperCamelCase:					Dict     =dirname(os.path.abspath(__file__))

# default torch.distributed port
_UpperCamelCase:					Optional[int]     ='10999'

_UpperCamelCase:					Optional[int]     ='t5-small'

# *** Working Models ***
_UpperCamelCase:					List[Any]     ='hf-internal-testing/tiny-albert'
_UpperCamelCase:					Dict     ='sshleifer/bart-tiny-random'
_UpperCamelCase:					Union[str, Any]     ='hf-internal-testing/tiny-bert'
_UpperCamelCase:					Dict     ='hf-internal-testing/tiny-random-bigbird_pegasus'
_UpperCamelCase:					List[str]     ='hf-internal-testing/tiny-random-big_bird'
_UpperCamelCase:					Any     ='hf-internal-testing/tiny-random-blenderbot'
_UpperCamelCase:					Optional[int]     ='bigscience/bigscience-small-testing'
_UpperCamelCase:					Optional[Any]     ='hf-internal-testing/tiny-random-deberta'
_UpperCamelCase:					str     ='hf-internal-testing/tiny-random-deberta-v2'
_UpperCamelCase:					str     ='sshleifer/tiny-distilbert-base-cased'
_UpperCamelCase:					Union[str, Any]     ='hf-internal-testing/tiny-electra'
_UpperCamelCase:					int     ='hf-internal-testing/tiny-random-flaubert'
_UpperCamelCase:					Tuple     ='stas/tiny-wmt19-en-de'
_UpperCamelCase:					int     ='hf-internal-testing/tiny-random-funnel'
_UpperCamelCase:					Dict     ='sshleifer/tiny-gpt2'
_UpperCamelCase:					Tuple     ='hf-internal-testing/tiny-random-gptj'
_UpperCamelCase:					Optional[Any]     ='hf-internal-testing/tiny-random-gpt_neo'
_UpperCamelCase:					Any     ='hf-internal-testing/tiny-layoutlm'
_UpperCamelCase:					List[Any]     ='hf-internal-testing/tiny-random-led'
_UpperCamelCase:					int     ='hf-internal-testing/tiny-random-longformer'
_UpperCamelCase:					str     ='stas/tiny-m2m_100'  # hf tiny model is unsuitable
_UpperCamelCase:					List[Any]     ='sshleifer/tiny-marian-en-de'
_UpperCamelCase:					str     ='sshleifer/tiny-mbart'
_UpperCamelCase:					Tuple     ='hf-internal-testing/tiny-random-mobilebert'
_UpperCamelCase:					Union[str, Any]     ='hf-internal-testing/tiny-random-mpnet'
_UpperCamelCase:					str     ='stas/pegasus-cnn_dailymail-tiny-random'
_UpperCamelCase:					int     ='hf-internal-testing/tiny-random-prophetnet'
_UpperCamelCase:					Any     ='sshleifer/tiny-distilroberta-base'
_UpperCamelCase:					int     ='hf-internal-testing/tiny-random-squeezebert'
_UpperCamelCase:					Optional[Any]     ='patrickvonplaten/t5-tiny-random'
_UpperCamelCase:					Dict     ='hf-internal-testing/tiny-random-t5-v1.1'
_UpperCamelCase:					Tuple     ='hf-internal-testing/tiny-random-vit'
_UpperCamelCase:					Tuple     ='hf-internal-testing/tiny-xlm-roberta'
_UpperCamelCase:					Optional[Any]     ='sshleifer/tiny-xlnet-base-cased'


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_UpperCamelCase:					Optional[Any]     ='hf-internal-testing/tiny-random-mt5'
_UpperCamelCase:					Tuple     ='hf-internal-testing/tiny-random-camembert'
_UpperCamelCase:					Optional[int]     ='hf-internal-testing/tiny-random-openai-gpt'

# missing tokenizer files
_UpperCamelCase:					Union[str, Any]     ='hf-internal-testing/tiny-random-convbert'
_UpperCamelCase:					Union[str, Any]     ='hf-internal-testing/tiny-random-layoutlmv2'
_UpperCamelCase:					List[str]     ='hf-internal-testing/tiny-random-hubert'

# issues with tokenizer
_UpperCamelCase:					Union[str, Any]     ='hf-internal-testing/tiny-random-ctrl'
_UpperCamelCase:					List[str]     ='hf-internal-testing/tiny-random-transfo-xl'  # same as ctrl

# other issues with tiny models
_UpperCamelCase:					Tuple     ='hf-internal-testing/tiny-random-ibert'  # multiple issues with either mlm/qa/clas
_UpperCamelCase:					int     ='hf-internal-testing/tiny-random-reformer'  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_UpperCamelCase:					Optional[int]     ='hf-internal-testing/tiny-random-dpr'
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_UpperCamelCase:					Any     ='hf-internal-testing/tiny-random-rag'
# - "rag" research_projects
_UpperCamelCase:					Optional[Any]     =''
# - "luke" Entities classes - no plan to make such example
_UpperCamelCase:					str     ='hf-internal-testing/tiny-random-lxmert'
# - "lxmert" doesn't work with run_qa.py
_UpperCamelCase:					str     ='hf-internal-testing/tiny-random-clip'
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_UpperCamelCase:					Dict     ='hf-internal-testing/tiny-random-speech_to_text'
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_UpperCamelCase:					Optional[Any]     ='hf-internal-testing/tiny-random-tapas'



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]=False    ):






  """simple docstring"""




  _lowerCAmelCase						=		min(2		,				get_gpu_count()    ) if distributed else 1
  _lowerCAmelCase						=		os.environ.get('DS_TEST_PORT'		,				__SCREAMING_SNAKE_CASE    )
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		f'''{FIXTURE_DIRECTORY}/tests_samples'''
  _lowerCAmelCase						=		f'''{data_dir_samples}/wmt_en_ro'''
  _lowerCAmelCase						=		f'''{data_dir_samples}/xsum'''
  _lowerCAmelCase						=		'\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

  # try to cover as many models as possible once (it's enough to run on one task per model)
  # but need a tiny model for each
  #
  # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
  _lowerCAmelCase						=		{
      'trans': [
          'bart',
          'fsmt',
          'm2m_100',
          'marian',
          'mbart',
          't5',
          't5_v1',
          # "mt5", missing model files
      ],
      'sum': [
          'pegasus',
      ],
      'clm': [
          'big_bird',
          'bigbird_pegasus',
          'blenderbot',
          'bloom',
          'gpt2',
          'gpt_neo',
          'gptj',
          'xlm-roberta',
          'prophetnet',
          # "camembert", missing model files
      ],
      'mlm': [
          'albert',
          'deberta',
          'deberta-v2',
          'distilbert',
          'electra',
          'flaubert',
          'funnel',
          'layoutlm',
          # "reformer", # multiple issues with either mlm/qa/clas
      ],
      'qa': [
          'led',
          'longformer',
          'mobilebert',
          'mpnet',
          'roberta',
          'squeezebert',
          # "convbert", # missing tokenizer files
          # "layoutlmv2", missing model files
      ],
      'clas': [
          'bert',
          'xlnet',
          # "hubert", # missing tokenizer files
          # "ibert", # multiple issues with either mlm/qa/clas
          # "transfo-xl", # tokenizer issues as ctrl
          # "ctrl", # tokenizer issues
          # "openai-gpt", missing model files
          # "tapas", multiple issues
      ],
      'img_clas': [
          'vit',
      ],
  }

  _lowerCAmelCase						=		f'''{ROOT_DIRECTORY}/examples/pytorch'''

  _lowerCAmelCase						=		{
      'trans': f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
      'sum': f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
      'clm': f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
      'mlm': f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
      'qa': f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
      'clas': f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
      'img_clas': f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
  }

  _lowerCAmelCase						=		get_launcher(distributed=__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		{}
  for task, args in tasks.items():
    _lowerCAmelCase						=		args.split()
    for model in tasksamodels[task]:
      _lowerCAmelCase						=		globals()[f'''{model.upper().replace("-"		,				"_"    )}_TINY''']
      _lowerCAmelCase						=		f'''--model_name_or_path {model_name}'''.split()
      _lowerCAmelCase						=		launcher + args + args_model + args_main

      # # generation special case
      # if task == "gen":
      #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
      #     args_model += f"--model_type {model}".split()
      #     cmds[f"{task}_{model}"] = launcher + args + args_model
      # else:

  return cmds


_UpperCamelCase:					Dict     =make_task_cmds()

_UpperCamelCase:					Union[str, Any]     ='zero2'
_UpperCamelCase:					Tuple     ='zero3'

_UpperCamelCase:					Tuple     =[ZEROa, ZEROa]



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




  _lowerCAmelCase						=		parameterized.to_safe_name('_'.join(str(__SCREAMING_SNAKE_CASE    ) for x in param.args    )    )
  return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_UpperCamelCase:					Optional[Any]     =list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Tuple       )					->   Dict:
         # return a ready to run train cmd
         if task not in task_cmds:
           raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''       )

         _lowerCAmelCase						=		task_cmds[task]
         _lowerCAmelCase						=		F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

         _lowerCAmelCase						=		self.get_auto_remove_tmp_dir()
         _lowerCAmelCase						=		F'''--output_dir {output_dir}'''.split()

         cmd += args_ds + args_out

         return cmd, output_dir






       @parameterized.expand(_lowerCAmelCase  ,      name_func=_lowerCAmelCase       )
       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Union[str, Any]       )					->   Dict:
         # testing the ability to do a run followed by recovery of full fp32 weights

         _lowerCAmelCase	,       _lowerCAmelCase						=		self.get_task_cmd(_lowerCAmelCase  ,      _lowerCAmelCase       )

         # 1. generate the checkpoint
         cmd += "--save_steps 1".split()
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
         execute_subprocess_async(_lowerCAmelCase  ,      env=self.get_env()       )

         # 2. test that the fp32 weights get reconsolidated
         _lowerCAmelCase						=		F'''{output_dir}/checkpoint-1'''
         _lowerCAmelCase						=		F'''{chkpt_dir}/out.bin'''
         _lowerCAmelCase						=		F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
         # keep for quick debug
         # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
         subprocess.check_call(_lowerCAmelCase  ,      shell=_lowerCAmelCase       )
         assert os.path.exists(_lowerCAmelCase       ), F'''{recovered_model_path} was not found'''

         # possibly could also test that the resulting saved model is usable but given that we use
         # random models we won't know if it's any good


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
#
########################################################################

_UpperCamelCase:					Dict     =16
_UpperCamelCase:					Optional[int]     =32



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Accelerator		,				__SCREAMING_SNAKE_CASE					:  int = 16    ):






  """simple docstring"""




  _lowerCAmelCase						=		AutoTokenizer.from_pretrained('bert-base-cased'    )
  _lowerCAmelCase						=		load_dataset('glue'		,				'mrpc'    )

  def tokenize_function(__SCREAMING_SNAKE_CASE					:  str    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase						=		tokenizer(examples['sentence1']		,				examples['sentence2']		,				truncation=__SCREAMING_SNAKE_CASE		,				max_length=__SCREAMING_SNAKE_CASE    )
    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():
    _lowerCAmelCase						=		datasets.map(
        __SCREAMING_SNAKE_CASE		,				batched=__SCREAMING_SNAKE_CASE		,				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
  _lowerCAmelCase						=		tokenized_datasets.rename_column('label'		,				'labels'    )

  def collate_fn(__SCREAMING_SNAKE_CASE					:  Any    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowerCAmelCase						=		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":
      _lowerCAmelCase						=		16
    elif accelerator.mixed_precision != "no":
      _lowerCAmelCase						=		8
    else:
      _lowerCAmelCase						=		None

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

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

  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

       _UpperCamelCase:					int     =mocked_dataloaders  # noqa: F811



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,				__SCREAMING_SNAKE_CASE    ) == "1":
    _lowerCAmelCase						=		2
  # Initialize accelerator
  _lowerCAmelCase						=		Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase						=		config['lr']
  _lowerCAmelCase						=		int(config['num_epochs']    )
  _lowerCAmelCase						=		int(config['seed']    )
  _lowerCAmelCase						=		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":
      _lowerCAmelCase						=		args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						=		int(args.checkpointing_steps    )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
  else:
    _lowerCAmelCase						=		None

  set_seed(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase	,       _lowerCAmelCase						=		get_dataloaders(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		evaluate.load('glue'		,				'mrpc'    )

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

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

  # 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).
  _lowerCAmelCase						=		model.to(accelerator.device    )

  # Instantiate optimizer
  _lowerCAmelCase						=		AdamW(params=model.parameters()		,				lr=__SCREAMING_SNAKE_CASE    )

  # Instantiate scheduler
  _lowerCAmelCase						=		get_linear_schedule_with_warmup(
      optimizer=__SCREAMING_SNAKE_CASE		,				num_warmup_steps=100		,				num_training_steps=(len(__SCREAMING_SNAKE_CASE    ) * 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.
  _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase	,       _lowerCAmelCase						=		accelerator.prepare(
      __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  _lowerCAmelCase						=		0
  # We also need to keep track of the stating epoch so files are named properly
  _lowerCAmelCase						=		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    )
      _lowerCAmelCase						=		os.path.basename(args.resume_from_checkpoint    )
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase						=		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
      dirs.sort(key=os.path.getctime    )
      _lowerCAmelCase						=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase						=		os.path.splitext(__SCREAMING_SNAKE_CASE    )[0]

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

    # Now we train the model
  for epoch in range(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    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
      _lowerCAmelCase						=		accelerator.skip_first_batches(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _lowerCAmelCase						=		train_dataloader
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.loss
      _lowerCAmelCase						=		loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE    )
      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(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
        _lowerCAmelCase						=		f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
          accelerator.save_state(__SCREAMING_SNAKE_CASE    )

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

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

    # 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":
      _lowerCAmelCase						=		f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
      accelerator.save_state(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='If the training should continue from a checkpoint folder.'		,				)
  _lowerCAmelCase						=		parser.parse_args()
  _lowerCAmelCase						=		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()
