






"""simple docstring"""







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
# and perform gradient accumulation
#
# 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            =	16
_lowerCAmelCase            =	32


def      UpperCamelCase       ( _A  ,				_A = 16 )     ->       Optional[Any]:
				lowercase    :      Dict       = AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowercase    :      int       = load_dataset("""glue"""  ,				"""mrpc""" )

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

				def collate_fn(_A ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase    :      Dict       = 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":
												lowercase    :      str       = 16
								elif accelerator.mixed_precision != "no":
												lowercase    :      List[str]       = 8
								else:
												lowercase    :      Optional[int]       = None

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

				# Instantiate dataloaders.
				lowercase    :      Optional[int]       = DataLoader(
				    tokenized_datasets["""train"""]  ,				shuffle=_A  ,				collate_fn=_A  ,				batch_size=_A )
				lowercase    :      Tuple       = DataLoader(
				    tokenized_datasets["""validation"""]  ,				shuffle=_A  ,				collate_fn=_A  ,				batch_size=_A )

				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

				_lowerCAmelCase            =	mocked_dataloaders  # noqa: F811


def      UpperCamelCase       ( _A  ,				_A )     ->       Optional[Any]:
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,				_A ) == "1":
								lowercase    :      Optional[Any]       = 2
				# New Code #
				lowercase    :      Optional[Any]       = int(args.gradient_accumulation_steps )
				# Initialize accelerator
				lowercase    :      int       = Accelerator(
				    cpu=args.cpu  ,				mixed_precision=args.mixed_precision  ,				gradient_accumulation_steps=_A )
				if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
								raise NotImplementedError(
								    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`""" )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowercase    :      Any       = config["""lr"""]
				lowercase    :      Tuple       = int(config["""num_epochs"""] )
				lowercase    :      Union[str, Any]       = int(config["""seed"""] )
				lowercase    :      Tuple       = int(config["""batch_size"""] )

				lowercase    :      str       = evaluate.load("""glue"""  ,				"""mrpc""" )

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

				# 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).
				lowercase    :      Optional[Any]       = model.to(accelerator.device )

				# Instantiate optimizer
				lowercase    :      Any       = AdamW(params=model.parameters()  ,				lr=_A )

				# Instantiate scheduler
				lowercase    :      int       = get_linear_schedule_with_warmup(
				    optimizer=_A  ,				num_warmup_steps=100  ,				num_training_steps=(len(_A ) * num_epochs)  ,				)

				# 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.
				lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase    :      List[str]       = accelerator.prepare(
				    _A  ,				_A  ,				_A  ,				_A  ,				_A )

				# Now we train the model
				for epoch in range(_A ):
								model.train()
								for step, batch in enumerate(_A ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												# New code #
												# We use the new `accumulate` context manager to perform gradient accumulation
												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
												with accelerator.accumulate(_A ):
																lowercase    :      Tuple       = model(**_A )
																lowercase    :      int       = output.loss
																accelerator.backward(_A )
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								lowercase    :      List[str]       = metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""  ,				_A )


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Union[str, Any]       = argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""  ,				type=_A  ,				default=_A  ,				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."""  ,				)
				# New Code #
				parser.add_argument(
				    """--gradient_accumulation_steps"""  ,				type=_A  ,				default=1  ,				help="""The number of minibatches to be ran before gradients are accumulated."""  ,				)
				parser.add_argument("""--cpu"""  ,				action="""store_true"""  ,				help="""If passed, will train on the CPU.""" )
				lowercase    :      int       = parser.parse_args()
				lowercase    :      str       = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_A  ,				_A )


if __name__ == "__main__":
				main()








"""simple docstring"""







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


_lowerCAmelCase            =	logging.get_logger(__name__)


@dataclass
class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(		self  :Union[str, Any]			, **__magic_name__  :int				)      ->Any:
										for deprecated_arg in self.deprecated_args:
														if deprecated_arg in kwargs:
																		lowercase    :      int       = deprecated_arg[3:]
																		setattr(self			, __magic_name__			, not kwargs.pop(__magic_name__				)				)
																		logger.warning(
																		    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																		    f""" {positive_arg}={kwargs[positive_arg]}"""				)

										lowercase    :      List[str]       = kwargs.pop("""torchscript"""			, self.torchscript				)
										lowercase    :      Any       = kwargs.pop("""torch_xla_tpu_print_metrics"""			, self.torch_xla_tpu_print_metrics				)
										lowercase    :      Union[str, Any]       = kwargs.pop("""fp16_opt_level"""			, self.fpaa_opt_level				)
										super().__init__(**__magic_name__				)

						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Trace the models using torchscript"""}     )
						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}     )
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    default="""O1"""						,						metadata={
						        """help""": (
						            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
						            """See details at https://nvidia.github.io/apex/amp.html"""
						        )
						    }						,						)






						@cached_property
						def      __snake_case      (		self  :Optional[int]				)      ->Tuple["torch.device", int]:
										requires_backends(self			, ["""torch"""]				)
										logger.info("""PyTorch: setting up devices"""				)
										if not self.cuda:
														lowercase    :      Optional[Any]       = torch.device("""cpu"""				)
														lowercase    :      int       = 0
										elif is_torch_tpu_available():
														lowercase    :      Any       = xm.xla_device()
														lowercase    :      List[str]       = 0
										else:
														lowercase    :      int       = torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)
														lowercase    :      Any       = torch.cuda.device_count()
										return device, n_gpu






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										return is_torch_tpu_available() and self.tpu






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->int:
										requires_backends(self			, ["""torch"""]				)
										# TODO(PVP): currently only single GPU is supported
										return torch.cuda.current_device()






						@property
						def      __snake_case      (		self  :Optional[int]				)      ->"torch.device":
										requires_backends(self			, ["""torch"""]				)
										return self._setup_devices[0]






						@property
						def      __snake_case      (		self  :Optional[Any]				)      ->str:
										requires_backends(self			, ["""torch"""]				)
										return self._setup_devices[1]






						@property
						def      __snake_case      (		self  :int				)      ->List[str]:
										return self.n_gpu > 0
