


"""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_       							=    16
a_       							=    32






def      a__ (				__lowercase     ,			__lowercase = 16   )	->	Dict:
				_A					  =			AutoTokenizer.from_pretrained("bert-base-cased"   )
				_A					  =			load_dataset("glue"     ,			"mrpc"   )

				def tokenize_function(__lowercase   ):
								# max_length=None => use the model max length (it's actually the default)
								_A					  =			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():
								_A					  =			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
				_A					  =			tokenized_datasets.rename_column("label"     ,			"labels"   )

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

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

				# Instantiate dataloaders.
				_A					  =			DataLoader(
				    tokenized_datasets["train"]     ,			shuffle=__lowercase     ,			collate_fn=__lowercase     ,			batch_size=__lowercase   )
				_A					  =			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      a__ (				__lowercase     ,			__lowercase   )	->	Optional[int]:
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,			__lowercase   ) == "1":
								_A					  =			2
				# New Code #
				_A					  =			int(args.gradient_accumulation_steps   )
				_A					  =			int(args.local_sgd_steps   )
				# Initialize accelerator
				_A					  =			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
				_A					  =			config["lr"]
				_A					  =			int(config["num_epochs"]   )
				_A					  =			int(config["seed"]   )
				_A					  =			int(config["batch_size"]   )

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

				set_seed(__lowercase   )
				_A  ,		_A					  =			get_dataloaders(__lowercase     ,			__lowercase   )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_A					  =			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).
				_A					  =			model.to(accelerator.device   )

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

				# Instantiate scheduler
				_A					  =			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.
				_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			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   ):
																				_A					  =			model(**__lowercase   )
																				_A					  =			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():
																_A					  =			model(**__lowercase   )
												_A					  =			outputs.logits.argmax(dim=-1   )
												_A  ,		_A					  =			accelerator.gather_for_metrics((predictions, batch["labels"])   )
												metric.add_batch(
												    predictions=__lowercase     ,			references=__lowercase     ,			)

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






def      a__ (				)	->	Dict:
				_A					  =			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."   )
				_A					  =			parser.parse_args()
				_A					  =			{"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


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)