


"""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_     :   Optional[int]       							=    16
a_     :   Union[str, Any]       							=    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=__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():
								_A					  =			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
				_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(
								    __UpperCamelCase     ,			padding="longest"     ,			max_length=__UpperCamelCase     ,			pad_to_multiple_of=__UpperCamelCase     ,			return_tensors="pt"     ,			)

				# Instantiate dataloaders.
				_A					  =			DataLoader(
				    tokenized_datasets["train"]     ,			shuffle=__UpperCamelCase     ,			collate_fn=__UpperCamelCase     ,			batch_size=__UpperCamelCase   )
				_A					  =			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_     :   Any       							=    mocked_dataloaders  # noqa: F811






def      a__ (				__lowercase     ,			__lowercase   )	->	List[Any]:
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,			__UpperCamelCase   ) == "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=__UpperCamelCase   )
				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(__UpperCamelCase   )
				_A  ,		_A					  =			get_dataloaders(__UpperCamelCase     ,			__UpperCamelCase   )
				# 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=__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).
				_A					  =			model.to(accelerator.device   )

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

				# Instantiate scheduler
				_A					  =			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.
				_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			accelerator.prepare(
				    __UpperCamelCase     ,			__UpperCamelCase     ,			__UpperCamelCase     ,			__UpperCamelCase     ,			__UpperCamelCase   )

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

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

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






def      a__ (				)	->	List[Any]:
				_A					  =			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(
				    "--local_sgd_steps"     ,			type=__UpperCamelCase     ,			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(__UpperCamelCase     ,			__UpperCamelCase   )


if __name__ == "__main__":
		main()



"""simple docstring"""


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       List[Any]    ,   a__      :       Any		)       ->						Any:





						'''simple docstring'''







						_A					  =			data







		def __iter__(					self      :       List[str]		)       ->						str:





						'''simple docstring'''







						for element in self.data:
										yield element






def      a__ (				__lowercase=True   )	->	Tuple:
				_A					  =			Accelerator(even_batches=__lowercase   )
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = False   )	->	Union[str, Any]:
				if iterable:
								_A					  =			DummyIterableDataset(torch.as_tensor(range(__lowercase   )   )   )
				else:
								_A					  =			TensorDataset(torch.as_tensor(range(__lowercase   )   )   )

				_A					  =			DataLoader(__lowercase     ,			batch_size=__lowercase   )
				_A					  =			accelerator.prepare(__lowercase   )

				return dl






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			)	->	Dict:
				_A					  =			create_dataloader(accelerator=__lowercase     ,			dataset_size=__lowercase     ,			batch_size=__lowercase   )

				_A					  =			[len(batch[0]   ) for batch in dl]

				if accelerator.process_index == 0:
								assert batch_sizes == process_0_expected_batch_sizes
				elif accelerator.process_index == 1:
								assert batch_sizes == process_1_expected_batch_sizes






def      a__ (				)	->	List[str]:
				_A					  =			create_accelerator()

				# without padding, we would expect a different number of batches
				verify_dataloader_batch_sizes(
				    __lowercase     ,			dataset_size=3     ,			batch_size=1     ,			process_0_expected_batch_sizes=[1, 1]     ,			process_1_expected_batch_sizes=[1, 1]     ,			)

				# without padding, we would expect the same number of batches, but different sizes
				verify_dataloader_batch_sizes(
				    __lowercase     ,			dataset_size=7     ,			batch_size=2     ,			process_0_expected_batch_sizes=[2, 2]     ,			process_1_expected_batch_sizes=[2, 2]     ,			)






def      a__ (				)	->	List[Any]:
				_A					  =			create_accelerator(even_batches=__lowercase   )

				verify_dataloader_batch_sizes(
				    __lowercase     ,			dataset_size=3     ,			batch_size=1     ,			process_0_expected_batch_sizes=[1, 1]     ,			process_1_expected_batch_sizes=[1]     ,			)

				verify_dataloader_batch_sizes(
				    __lowercase     ,			dataset_size=7     ,			batch_size=2     ,			process_0_expected_batch_sizes=[2, 2]     ,			process_1_expected_batch_sizes=[2, 1]     ,			)






def      a__ (				)	->	int:
				_A					  =			create_accelerator(even_batches=__lowercase   )

				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )

				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				_A					  =			[]
				with accelerator.join_uneven_inputs([ddp_model]   ):
								for batch_idx, batch in enumerate(__lowercase   ):
												_A					  =			ddp_model(batch[0].float()   )
												_A					  =			output.sum()
												loss.backward()
												batch_idxs.append(__lowercase   )

				accelerator.wait_for_everyone()

				if accelerator.process_index == 0:
								assert batch_idxs == [0, 1]
				elif accelerator.process_index == 1:
								assert batch_idxs == [0]






def      a__ (				__lowercase   )	->	List[str]:
				with warnings.catch_warnings(record=__lowercase   ) as w:
								with accelerator.join_uneven_inputs([Mock()]   ):
												pass

								assert issubclass(w[-1].category     ,			__lowercase   )
								assert "only supported for multi-GPU" in str(w[-1].message   )






def      a__ (				)	->	Tuple:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
								_A					  =			train_dl.batch_sampler.even_batches
								_A					  =			valid_dl.batch_sampler.even_batches

				assert train_dl_overridden_value == overridden_even_batches
				assert valid_dl_overridden_value == overridden_even_batches
				assert train_dl.batch_sampler.even_batches == default_even_batches
				assert valid_dl.batch_sampler.even_batches == default_even_batches






def      a__ (				)	->	int:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with warnings.catch_warnings():
								warnings.filterwarnings("ignore"   )
								try:
												with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
																_A					  =			batch_dl.batch_sampler.even_batches
								except AttributeError:
												# ensure attribute error is not raised when processing iterable dl
												raise AssertionError

				assert batch_dl_overridden_value == overridden_even_batches
				assert batch_dl.batch_sampler.even_batches == default_even_batches






def      a__ (				)	->	Optional[Any]:
				_A					  =			create_accelerator()
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )

				with warnings.catch_warnings(record=__lowercase   ) as w:
								with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
												pass

								assert issubclass(w[-1].category     ,			__lowercase   )
								assert "only supported for map-style datasets" in str(w[-1].message   )






def      a__ (				)	->	Optional[Any]:
				_A					  =			create_accelerator()

				accelerator.print("Test that even_batches variable ensures uniform batches across processes"   )
				test_default_ensures_even_batch_sizes()

				accelerator.print("Run tests with even_batches disabled"   )
				test_can_disable_even_batches()

				accelerator.print("Test joining uneven inputs"   )
				test_can_join_uneven_inputs()

				accelerator.print("Test overriding even_batches when joining uneven inputs"   )
				test_join_can_override_even_batches()

				accelerator.print("Test overriding even_batches for mixed dataloader types"   )
				test_join_can_override_for_mixed_type_dataloaders()

				accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"   )
				test_join_raises_warning_for_iterable_when_overriding_even_batches()

				accelerator.print("Test join with non DDP distributed raises warning"   )
				_A					  =			accelerator.state.distributed_type
				_A					  =			DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(__lowercase   )
				_A					  =			original_state


if __name__ == "__main__":
		main()