






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


a_							=    16
a_							=    32


def        _a  (     UpperCamelCase_					:				Accelerator    ,   UpperCamelCase_					:				int = 16   ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						AutoTokenizer.from_pretrained("bert-base-cased"   )
						lowerCAmelCase__       =						load_dataset("glue"    ,   "mrpc"   )

						def tokenize_function(UpperCamelCase_					:				Optional[int]   ):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__       =						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():
												lowerCAmelCase__       =						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
						lowerCAmelCase__       =						tokenized_datasets.rename_column("label"    ,   "labels"   )

						def collate_fn(UpperCamelCase_					:				Dict   ):
												# 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(
												    UpperCamelCase_    ,   padding="longest"    ,   max_length=UpperCamelCase_    ,   pad_to_multiple_of=UpperCamelCase_    ,   return_tensors="pt"    ,   )

						# Instantiate dataloaders.
						lowerCAmelCase__       =						DataLoader(
						    tokenized_datasets["train"]    ,   shuffle=UpperCamelCase_    ,   collate_fn=UpperCamelCase_    ,   batch_size=UpperCamelCase_   )
						lowerCAmelCase__       =						DataLoader(
						    tokenized_datasets["validation"]    ,   shuffle=UpperCamelCase_    ,   collate_fn=UpperCamelCase_    ,   batch_size=UpperCamelCase_   )

						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

	a_							=    mocked_dataloaders  # noqa: F811


def        _a  (     UpperCamelCase_					:				Tuple    ,   UpperCamelCase_					:				Any   ) ->   List[str]:
						"""simple docstring"""







						if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,   UpperCamelCase_   ) == "1":
												lowerCAmelCase__       =						2
						# New Code #
						lowerCAmelCase__       =						int(args.gradient_accumulation_steps   )
						# Initialize accelerator
						lowerCAmelCase__       =						Accelerator(
						    cpu=args.cpu    ,   mixed_precision=args.mixed_precision    ,   gradient_accumulation_steps=UpperCamelCase_   )
						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
						lowerCAmelCase__       =						config["lr"]
						lowerCAmelCase__       =						int(config["num_epochs"]   )
						lowerCAmelCase__       =						int(config["seed"]   )
						lowerCAmelCase__       =						int(config["batch_size"]   )

						lowerCAmelCase__       =						evaluate.load("glue"    ,   "mrpc"   )

						set_seed(UpperCamelCase_   )
						lowerCAmelCase__      ,							lowerCAmelCase__       =						get_dataloaders(UpperCamelCase_    ,   UpperCamelCase_   )
						# 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=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).
						lowerCAmelCase__       =						model.to(accelerator.device   )

						# Instantiate optimizer
						lowerCAmelCase__       =						AdamW(params=model.parameters()    ,   lr=UpperCamelCase_   )

						# Instantiate scheduler
						lowerCAmelCase__       =						get_linear_schedule_with_warmup(
						    optimizer=UpperCamelCase_    ,   num_warmup_steps=100    ,   num_training_steps=(len(UpperCamelCase_   ) * 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.
						lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						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   )
																		# 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(UpperCamelCase_   ):
																								lowerCAmelCase__       =						model(**UpperCamelCase_   )
																								lowerCAmelCase__       =						output.loss
																								accelerator.backward(UpperCamelCase_   )
																								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():
																								lowerCAmelCase__       =						model(**UpperCamelCase_   )
																		lowerCAmelCase__       =						outputs.logits.argmax(dim=-1   )
																		lowerCAmelCase__      ,							lowerCAmelCase__       =						accelerator.gather_for_metrics((predictions, batch["labels"])   )
																		metric.add_batch(
																		    predictions=UpperCamelCase_    ,   references=UpperCamelCase_    ,   )

												lowerCAmelCase__       =						metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"    ,   UpperCamelCase_   )


def        _a  (     ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						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."    ,   )
						# New Code #
						parser.add_argument(
						    "--gradient_accumulation_steps"    ,   type=UpperCamelCase_    ,   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."   )
						lowerCAmelCase__       =						parser.parse_args()
						lowerCAmelCase__       =						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(UpperCamelCase_    ,   UpperCamelCase_   )


if __name__ == "__main__":
	main()














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ['''PLBartTokenizer''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''PLBartForCausalLM''',
	    '''PLBartForConditionalGeneration''',
	    '''PLBartForSequenceClassification''',
	    '''PLBartModel''',
	    '''PLBartPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)






