


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for param, grad_param in zip(model_a.parameters()      ,						model_b.parameters()	):
    if not param.requires_grad:
      continue
    if not did_step:
      # Grads should not be in sync
      assert (
          torch.allclose(param.grad      ,						grad_param.grad	) is False
      ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
    else:
      # Grads should be in sync
      assert (
          torch.allclose(param.grad      ,						grad_param.grad	) is True
      ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=True	):


  '''simple docstring'''

  model.train()
  lowerCamelCase :					Any    =				model(_UpperCAmelCase	)
  lowerCamelCase :					List[Any]    =				F.mse_loss(_UpperCAmelCase      ,						target.to(output.device	)	)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(_UpperCAmelCase	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  set_seed(42	)
  lowerCamelCase :					Tuple    =				RegressionModel()
  lowerCamelCase :					Optional[int]    =				deepcopy(_UpperCAmelCase	)
  lowerCamelCase :					Union[str, Any]    =				RegressionDataset(length=80	)
  lowerCamelCase :					Union[str, Any]    =				DataLoader(_UpperCAmelCase      ,						batch_size=16	)
  model.to(accelerator.device	)
  if sched:
    lowerCamelCase :					List[Any]    =				AdamW(params=model.parameters()      ,						lr=1E-3	)
    lowerCamelCase :					str    =				AdamW(params=ddp_model.parameters()      ,						lr=1E-3	)
    lowerCamelCase :					Union[str, Any]    =				LambdaLR(_UpperCAmelCase      ,						lr_lambda=lambda SCREAMING_SNAKE_CASE_	: epoch**0.65	)
    lowerCamelCase :					List[Any]    =				LambdaLR(_UpperCAmelCase      ,						lr_lambda=lambda SCREAMING_SNAKE_CASE_	: epoch**0.65	)
  # Make a copy of `model`
  if sched:
    lowerCamelCase :					Optional[int]    =				accelerator.prepare(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
  else:
    lowerCamelCase :					Tuple    =				accelerator.prepare(_UpperCAmelCase      ,						_UpperCAmelCase	)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[int]    =				get_training_setup(_UpperCAmelCase	)
  # Use a single batch
  lowerCamelCase :					Tuple    =				next(iter(_UpperCAmelCase	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    lowerCamelCase :					Tuple    =				accelerator.gather((ddp_input, ddp_target)	)
    lowerCamelCase :					Tuple    =				input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(_UpperCAmelCase	):
        step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    else:
      # Sync grads
      step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)

    # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
    check_model_parameters(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    for param, ddp_param in zip(model.parameters()      ,						ddp_model.parameters()	):
      if not param.requires_grad:
        continue
      assert torch.allclose(
          param.grad      ,						ddp_param.grad	), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

    # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
    lowerCamelCase :					Optional[Any]    =				ddp_input[torch.randperm(len(_UpperCAmelCase	)	)]






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				get_training_setup(_UpperCAmelCase	)
  # Use a single batch
  lowerCamelCase :					Optional[int]    =				next(iter(_UpperCAmelCase	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    lowerCamelCase :					str    =				accelerator.gather((ddp_input, ddp_target)	)
    lowerCamelCase :					Tuple    =				input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(_UpperCAmelCase	):
        step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    else:
      # Sync grads
      step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)

    # DDP model and model should only be in sync when not (iteration % 2 == 0)
    for param, ddp_param in zip(model.parameters()      ,						ddp_model.parameters()	):
      if not param.requires_grad:
        continue
      if iteration % 2 == 0:
        # Grads should not be in sync
        assert (
            torch.allclose(param.grad      ,						ddp_param.grad	) is False
        ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
      else:
        # Grads should be in sync
        assert (
            torch.allclose(param.grad      ,						ddp_param.grad	) is True
        ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
    lowerCamelCase :					Any    =				ddp_input[torch.randperm(len(_UpperCAmelCase	)	)]






def 		lowercase_(  SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				Accelerator(
      split_batches=_UpperCAmelCase      ,						dispatch_batches=_UpperCAmelCase      ,						gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  lowerCamelCase :					Union[str, Any]    =				get_training_setup(_UpperCAmelCase	)
  for iteration, batch in enumerate(_UpperCAmelCase	):
    lowerCamelCase :					Any    =				batch.values()
    # Gather the distributed inputs and targs for the base model
    lowerCamelCase :					Any    =				accelerator.gather((ddp_input, ddp_target)	)
    lowerCamelCase :					str    =				input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    # Do "gradient accumulation" (noop)
    with accelerator.accumulate(_UpperCAmelCase	):
      step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)

    # DDP model and model should only be in sync when not (iteration % 2 == 0)
    for param, ddp_param in zip(model.parameters()      ,						ddp_model.parameters()	):
      if not param.requires_grad:
        continue
      if ((iteration + 1) % 2 == 0) or (iteration == len(_UpperCAmelCase	) - 1):
        # Grads should be in sync
        assert (
            torch.allclose(param.grad      ,						ddp_param.grad	) is True
        ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
      else:
        # Grads should not be in sync
        assert (
            torch.allclose(param.grad      ,						ddp_param.grad	) is False
        ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
    lowerCamelCase :					Dict    =				ddp_input[torch.randperm(len(_UpperCAmelCase	)	)]
  GradientState._reset_state()






def 		lowercase_(  SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				Accelerator(
      split_batches=_UpperCAmelCase      ,						dispatch_batches=_UpperCAmelCase      ,						gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  lowerCamelCase :					Union[str, Any]    =				get_training_setup(_UpperCAmelCase      ,						_UpperCAmelCase	)
  for iteration, batch in enumerate(_UpperCAmelCase	):
    lowerCamelCase :					Optional[int]    =				batch.values()
    # Gather the distributed inputs and targs for the base model
    lowerCamelCase :					List[str]    =				accelerator.gather((ddp_input, ddp_target)	)
    lowerCamelCase :					int    =				input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    model.train()
    ddp_model.train()
    step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    opt.step()

    if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_UpperCAmelCase	)):
      if split_batches:
        sched.step()
      else:
        for _ in range(accelerator.num_processes	):
          sched.step()
    opt.zero_grad()
    # Perform gradient accumulation under wrapper
    with accelerator.accumulate(_UpperCAmelCase	):
      step_model(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
      ddp_opt.step()
      ddp_sched.step()
      ddp_opt.zero_grad()

    # Learning rates should be the same
    assert (
        opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
    ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
    lowerCamelCase :					Optional[Any]    =				(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_UpperCAmelCase	))
    if accelerator.num_processes > 1:
      check_model_parameters(_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase      ,						_UpperCAmelCase	)
    # Shuffle ddp_input on each iteration
    torch.manual_seed(1337 + iteration	)
  GradientState._reset_state()






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				Accelerator()

  lowerCamelCase :					Dict    =				RegressionDataset(length=80	)
  lowerCamelCase :					Optional[Any]    =				DataLoader(_UpperCAmelCase      ,						batch_size=16	)
  lowerCamelCase :					Any    =				RegressionDataset(length=96	)
  lowerCamelCase :					Optional[Any]    =				DataLoader(_UpperCAmelCase      ,						batch_size=16	)
  lowerCamelCase :					Optional[int]    =				accelerator.prepare(_UpperCAmelCase      ,						_UpperCAmelCase	)
  assert accelerator.gradient_state.active_dataloader is None
  for iteration, _ in enumerate(_UpperCAmelCase	):
    assert id(accelerator.gradient_state.active_dataloader	) == id(_UpperCAmelCase	)
    if iteration < len(_UpperCAmelCase	) - 1:
      assert not accelerator.gradient_state.end_of_dataloader
      if iteration == 1:
        for batch_num, _ in enumerate(_UpperCAmelCase	):
          assert id(accelerator.gradient_state.active_dataloader	) == id(_UpperCAmelCase	)
          if batch_num < len(_UpperCAmelCase	) - 1:
            assert not accelerator.gradient_state.end_of_dataloader
          else:
            assert accelerator.gradient_state.end_of_dataloader
    else:
      assert accelerator.gradient_state.end_of_dataloader
  assert accelerator.gradient_state.active_dataloader is None






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				Accelerator()
  lowerCamelCase :					int    =				accelerator.state
  if state.local_process_index == 0:
    print("**Test `accumulate` gradient accumulation with dataloader break**"	)
  test_dataloader_break()
  if state.distributed_type == DistributedType.NO:
    if state.local_process_index == 0:
      print("**Test NOOP `no_sync` context manager**"	)
    test_noop_sync(_UpperCAmelCase	)
  if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
    if state.local_process_index == 0:
      print("**Test Distributed `no_sync` context manager**"	)
    test_distributed_sync(_UpperCAmelCase	)
  if state.distributed_type == DistributedType.MULTI_GPU:
    for split_batch in [True, False]:
      for dispatch_batches in [True, False]:
        if state.local_process_index == 0:
          print(
              "**Test `accumulate` gradient accumulation, "      ,						f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""      ,						)
        test_gradient_accumulation(_UpperCAmelCase      ,						_UpperCAmelCase	)

    # Currently will break on torch 2.0 +, need to investigate why
  if is_torch_version("<"      ,						"2.0"	) or state.distributed_type == DistributedType.NO:
    if state.local_process_index == 0:
      print(
          "**Test `accumulate` gradient accumulation with optimizer and scheduler, "      ,						"`split_batches=False`, `dispatch_batches=False`**"      ,						)
    test_gradient_accumulation_with_opt_and_scheduler()
    if state.distributed_type == DistributedType.MULTI_GPU:
      for split_batch in [True, False]:
        for dispatch_batches in [True, False]:
          if not split_batch and not dispatch_batches:
            continue
          if state.local_process_index == 0:
            print(
                "**Test `accumulate` gradient accumulation with optimizer and scheduler, "      ,						f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""      ,						)
          test_gradient_accumulation_with_opt_and_scheduler(_UpperCAmelCase      ,						_UpperCAmelCase	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  main()


if __name__ == "__main__":
   main()






import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase							:  Optional[Any]   	=						16
lowerCAmelCase							:  List[str]   	=						32







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = 16    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       load_dataset("glue"				,	"mrpc"    )

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

						def collate_fn(_UpperCAmelCase    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE_: List[str]  		=       1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       8
												else:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       None

												return tokenizer.pad(
												    _UpperCAmelCase				,	padding="longest"				,	max_length=_UpperCAmelCase				,	pad_to_multiple_of=_UpperCAmelCase				,	return_tensors="pt"				,	)

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_: Dict  		=       DataLoader(
						    tokenized_datasets["train"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(
						    tokenized_datasets["validation"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=(accelerator.mixed_precision == "fp8")				,	)

						return train_dataloader, eval_dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_: str  		=       Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_: int  		=       config["lr"]
						SCREAMING_SNAKE_CASE_: Any  		=       int(config["num_epochs"]    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       int(config["seed"]    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       int(config["batch_size"]    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       evaluate.load("glue"				,	"mrpc"    )

						# If the batch size is too big we use gradient accumulation
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												SCREAMING_SNAKE_CASE_: Tuple  		=       batch_size // MAX_GPU_BATCH_SIZE
												SCREAMING_SNAKE_CASE_: Dict  		=       MAX_GPU_BATCH_SIZE

						set_seed(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       get_dataloaders(_UpperCAmelCase				,	_UpperCAmelCase    )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_: List[Any]  		=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,	return_dict=_UpperCAmelCase    )

						# 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).
						SCREAMING_SNAKE_CASE_: Tuple  		=       model.to(accelerator.device    )
						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       AdamW(params=model.parameters()				,	lr=_UpperCAmelCase    )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase				,	num_warmup_steps=1_00				,	num_training_steps=(len(_UpperCAmelCase    ) * 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.

						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(
						    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						# Now we train the model
						for epoch in range(_UpperCAmelCase    ):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.loss
																		SCREAMING_SNAKE_CASE_: Tuple  		=       loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												SCREAMING_SNAKE_CASE_: List[Any]  		=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"				,	_UpperCAmelCase    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Any  		=       argparse.ArgumentParser(description="Simple example of training script."    )
						parser.add_argument(
						    "--mixed_precision"				,	type=_UpperCAmelCase				,	default=_UpperCAmelCase				,	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."    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       parser.parse_args()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase				,	_UpperCAmelCase    )


if __name__ == "__main__":
						main()






