"""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
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
#
########################################################################


_A   					=    1_6
_A   					=    3_2




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase = 16       ):
				SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("bert-base-cased"       )
				SCREAMING_SNAKE_CASE__		  =				load_dataset("glue"  ,					"mrpc"       )

				def tokenize_function(__UpperCAmelCase       ):
								# max_length=None => use the model max length (it's actually the default)
								SCREAMING_SNAKE_CASE__		  =				tokenizer(examples["sentence1"]  ,					examples["sentence2"]  ,					truncation=__lowercase  ,					max_length=__lowercase       )
								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__		  =				datasets.map(
								    __lowercase  ,					batched=__lowercase  ,					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__		  =				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__		  =				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":
												SCREAMING_SNAKE_CASE__		  =				16
								elif accelerator.mixed_precision != "no":
												SCREAMING_SNAKE_CASE__		  =				8
								else:
												SCREAMING_SNAKE_CASE__		  =				None

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

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

				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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       ):




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

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

				set_seed(__lowercase       )
				SCREAMING_SNAKE_CASE__		  =				get_dataloaders(__lowercase  ,					__lowercase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				SCREAMING_SNAKE_CASE__		  =				AutoModelForSequenceClassification.from_pretrained("bert-base-cased"  ,					return_dict=__lowercase       )

				# 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__		  =				model.to(accelerator.device       )

				# Instantiate optimizer
				SCREAMING_SNAKE_CASE__		  =				AdamW(params=model.parameters()  ,					lr=__lowercase       )

				# Instantiate scheduler
				SCREAMING_SNAKE_CASE__		  =				get_linear_schedule_with_warmup(
				    optimizer=__lowercase  ,					num_warmup_steps=100  ,					num_training_steps=(len(__lowercase       ) * 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__		  =				accelerator.prepare(
				    __lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase  ,					__lowercase       )

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

								model.eval()
								for step, batch in enumerate(__lowercase       ):
												# 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__		  =				model(**__lowercase       )
												SCREAMING_SNAKE_CASE__		  =				outputs.logits.argmax(dim=-1       )
												SCREAMING_SNAKE_CASE__		  =				accelerator.gather_for_metrics((predictions, batch["labels"])       )
												metric.add_batch(
												    predictions=__lowercase  ,					references=__lowercase  ,					)

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




def 						SCREAMING_SNAKE_CASE					(  ):
				SCREAMING_SNAKE_CASE__		  =				argparse.ArgumentParser(description="Simple example of training script."       )
				parser.add_argument(
				    "--mixed_precision"  ,					type=__lowercase  ,					default=__lowercase  ,					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=__lowercase  ,					default=1  ,					help="The number of minibatches to be ran before gradients are accumulated."  ,					)
				parser.add_argument(
				    "--local_sgd_steps"  ,					type=__lowercase  ,					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__		  =				parser.parse_args()
				SCREAMING_SNAKE_CASE__		  =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(__lowercase  ,					__lowercase       )


if __name__ == "__main__":
						main()





"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A   					=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    [
						    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'VanForImageClassification',
						    'VanModel',
						    'VanPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

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




