

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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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							:  Dict            =     16
lowerCAmelCase							:  str            =     32







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

						def tokenize_function(_UpperCAmelCase   ):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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():
												SCREAMING_SNAKE_CASE_:			Any  		=	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
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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_:			Optional[int]  		=	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_:			Any  		=	16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_:			int  		=	8
												else:
																		SCREAMING_SNAKE_CASE_:			int  		=	None

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_:			Tuple  		=	DataLoader(
						    tokenized_datasets["train"]    ,     shuffle=lowerCAmelCase__    ,     collate_fn=lowerCAmelCase__    ,     batch_size=lowerCAmelCase__   )
						SCREAMING_SNAKE_CASE_:			Any  		=	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

						lowerCAmelCase							:  Tuple            =     mocked_dataloaders  # noqa: F811







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						# For testing only
						if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,     lowerCAmelCase__   ) == "1":
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2
						# New Code #
						SCREAMING_SNAKE_CASE_:			int  		=	int(args.gradient_accumulation_steps   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	int(args.local_sgd_steps   )
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_:			Dict  		=	Accelerator(
						    cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     gradient_accumulation_steps=lowerCAmelCase__   )
						if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
												raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"   )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_:			int  		=	config["lr"]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	int(config["num_epochs"]   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	int(config["seed"]   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	int(config["batch_size"]   )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	evaluate.load("glue"    ,     "mrpc"   )

						set_seed(lowerCAmelCase__   )
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	get_dataloaders(lowerCAmelCase__    ,     lowerCAmelCase__   )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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).
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	model.to(accelerator.device   )

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_:			Tuple  		=	AdamW(params=model.parameters()    ,     lr=lowerCAmelCase__   )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_linear_schedule_with_warmup(
						    optimizer=lowerCAmelCase__    ,     num_warmup_steps=1_00    ,     num_training_steps=(len(lowerCAmelCase__   ) * 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.
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[Any]  		=	accelerator.prepare(
						    lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__   )

						# Now we train the model
						for epoch in range(lowerCAmelCase__   ):
												model.train()
												with LocalSGD(
												    accelerator=lowerCAmelCase__    ,     model=lowerCAmelCase__    ,     local_sgd_steps=lowerCAmelCase__    ,     enabled=local_sgd_steps is not None   ) as local_sgd:
																		for step, batch in enumerate(lowerCAmelCase__   ):
																								# 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(lowerCAmelCase__   ):
																														SCREAMING_SNAKE_CASE_:			List[Any]  		=	model(**lowerCAmelCase__   )
																														SCREAMING_SNAKE_CASE_:			List[Any]  		=	output.loss
																														accelerator.backward(lowerCAmelCase__   )
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

												model.eval()
												for step, batch in enumerate(lowerCAmelCase__   ):
																		# 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_:			List[Any]  		=	model(**lowerCAmelCase__   )
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	outputs.logits.argmax(dim=-1   )
																		SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			int  		=	accelerator.gather_for_metrics((predictions, batch["labels"])   )
																		metric.add_batch(
																		    predictions=lowerCAmelCase__    ,     references=lowerCAmelCase__    ,     )

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







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			int  		=	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."    ,     )
						# New Code #
						parser.add_argument(
						    "--gradient_accumulation_steps"    ,     type=lowerCAmelCase__    ,     default=1    ,     help="The number of minibatches to be ran before gradients are accumulated."    ,     )
						parser.add_argument(
						    "--local_sgd_steps"    ,     type=lowerCAmelCase__    ,     default=8    ,     help="Number of local SGD steps or None to disable local SGD"   )
						parser.add_argument("--cpu"    ,     action="store_true"    ,     help="If passed, will train on the CPU."   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	parser.parse_args()
						SCREAMING_SNAKE_CASE_:			int  		=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(lowerCAmelCase__    ,     lowerCAmelCase__   )


if __name__ == "__main__":
						main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]            =						{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Optional[int]            =						[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						lowerCAmelCase							:  Any            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






