


'''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        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  List[str]:
					lowercase						:		int   					=		data


				def __iter__(     self							)    ->  Optional[int]:
					for element in self.data:
						yield element




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




def 	_A  (       A							,A							,A							,A = False							)  ->				Dict:
	if iterable:
		lowercase						:		List[Any]   					=		DummyIterableDataset(torch.as_tensor(range(A							)							)							)
	else:
		lowercase						:		List[str]   					=		TensorDataset(torch.as_tensor(range(A							)							)							)

	lowercase						:		Union[str, Any]   					=		DataLoader(A							,batch_size=A							)
	lowercase						:		Optional[int]   					=		accelerator.prepare(A							)

	return dl




def 	_A  (       A							,A							,A							,A							,A							,)  ->				Optional[Any]:
	lowercase						:		Dict   					=		create_dataloader(accelerator=A							,dataset_size=A							,batch_size=A							)

	lowercase						:		Optional[int]   					=		[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  (       )  ->				Dict:
	lowercase						:		str   					=		create_accelerator()

	# without padding, we would expect a different number of batches
	verify_dataloader_batch_sizes(
	    A							,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(
	    A							,dataset_size=7							,batch_size=2							,process_0_expected_batch_sizes=[2, 2]							,process_1_expected_batch_sizes=[2, 2]							,)




def 	_A  (       )  ->				Union[str, Any]:
	lowercase						:		Dict   					=		create_accelerator(even_batches=A							)

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

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




def 	_A  (       )  ->				Optional[Any]:
	lowercase						:		Any   					=		create_accelerator(even_batches=A							)

	lowercase						:		List[Any]   					=		torch.nn.Linear(1							,1							)
	lowercase						:		List[str]   					=		accelerator.prepare(A							)

	lowercase						:		List[Any]   					=		create_dataloader(A							,dataset_size=3							,batch_size=1							)

	lowercase						:		List[Any]   					=		[]
	with accelerator.join_uneven_inputs([ddp_model]							):
		for batch_idx, batch in enumerate(A							):
			lowercase						:		Tuple   					=		ddp_model(batch[0].float()							)
			lowercase						:		Optional[int]   					=		output.sum()
			loss.backward()
			batch_idxs.append(A							)

	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  (       A							)  ->				Optional[Any]:
	with warnings.catch_warnings(record=A							) as w:
		with accelerator.join_uneven_inputs([Mock()]							):
			pass

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




def 	_A  (       )  ->				int:
	lowercase						:		List[str]   					=		True
	lowercase						:		Dict   					=		False
	lowercase						:		Union[str, Any]   					=		create_accelerator(even_batches=A							)
	lowercase						:		Optional[int]   					=		torch.nn.Linear(1							,1							)
	lowercase						:		Dict   					=		accelerator.prepare(A							)
	lowercase						:		List[str]   					=		create_dataloader(A							,dataset_size=3							,batch_size=1							)
	lowercase						:		Any   					=		create_dataloader(A							,dataset_size=3							,batch_size=1							)

	with accelerator.join_uneven_inputs([ddp_model]							,even_batches=A							):
		lowercase						:		Optional[int]   					=		train_dl.batch_sampler.even_batches
		lowercase						:		List[Any]   					=		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  (       )  ->				Optional[int]:
	lowercase						:		Optional[Any]   					=		True
	lowercase						:		List[str]   					=		False
	lowercase						:		str   					=		create_accelerator(even_batches=A							)
	lowercase						:		Tuple   					=		torch.nn.Linear(1							,1							)
	lowercase						:		str   					=		accelerator.prepare(A							)
	create_dataloader(A							,dataset_size=3							,batch_size=1							,iterable=A							)
	lowercase						:		str   					=		create_dataloader(A							,dataset_size=3							,batch_size=1							)

	with warnings.catch_warnings():
		warnings.filterwarnings("ignore"							)
		try:
			with accelerator.join_uneven_inputs([ddp_model]							,even_batches=A							):
				lowercase						:		Any   					=		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  (       )  ->				Any:
	lowercase						:		Tuple   					=		create_accelerator()
	lowercase						:		Any   					=		torch.nn.Linear(1							,1							)
	lowercase						:		str   					=		accelerator.prepare(A							)
	create_dataloader(A							,dataset_size=3							,batch_size=1							,iterable=A							)

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

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




def 	_A  (       )  ->				Optional[int]:
	lowercase						:		Any   					=		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"							)
	lowercase						:		List[Any]   					=		accelerator.state.distributed_type
	lowercase						:		List[str]   					=		DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(A							)
	lowercase						:		Optional[Any]   					=		original_state


if __name__ == "__main__":
					main()






'''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
#
########################################################################


lowerCAmelCase   : Tuple             =				1_6
lowerCAmelCase   : Optional[int]             =				3_2




def 	_A  (       A							,A = 1_6							)  ->				Union[str, Any]:
	lowercase						:		List[Any]   					=		AutoTokenizer.from_pretrained("bert-base-cased"							)
	lowercase						:		Any   					=		load_dataset("glue"							,"mrpc"							)

	def tokenize_function(A							):
		# max_length=None => use the model max length (it's actually the default)
		lowercase						:		List[str]   					=		tokenizer(examples["sentence1"]							,examples["sentence2"]							,truncation=A							,max_length=A							)
		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():
		lowercase						:		List[str]   					=		datasets.map(
		    A							,batched=A							,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
	lowercase						:		Optional[int]   					=		tokenized_datasets.rename_column("label"							,"labels"							)

	def collate_fn(A							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		lowercase						:		Tuple   					=		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			lowercase						:		Any   					=		1_6
		elif accelerator.mixed_precision != "no":
			lowercase						:		List[Any]   					=		8
		else:
			lowercase						:		List[Any]   					=		None

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

	# Instantiate dataloaders.
	lowercase						:		Any   					=		DataLoader(
	    tokenized_datasets["train"]							,shuffle=A							,collate_fn=A							,batch_size=A							)
	lowercase						:		List[str]   					=		DataLoader(
	    tokenized_datasets["validation"]							,shuffle=A							,collate_fn=A							,batch_size=A							)

	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   : List[Any]             =				mocked_dataloaders  # noqa: F811




def 	_A  (       A							,A							)  ->				str:
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,A							) == "1":
		lowercase						:		Optional[Any]   					=		2
	# New Code #
	lowercase						:		List[str]   					=		int(args.gradient_accumulation_steps							)
	lowercase						:		Union[str, Any]   					=		int(args.local_sgd_steps							)
	# Initialize accelerator
	lowercase						:		Union[str, Any]   					=		Accelerator(
	    cpu=args.cpu							,mixed_precision=args.mixed_precision							,gradient_accumulation_steps=A							)
	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
	lowercase						:		Dict   					=		config["lr"]
	lowercase						:		List[Any]   					=		int(config["num_epochs"]							)
	lowercase						:		int   					=		int(config["seed"]							)
	lowercase						:		Dict   					=		int(config["batch_size"]							)

	lowercase						:		List[str]   					=		evaluate.load("glue"							,"mrpc"							)

	set_seed(A							)
	lowercase					,		lowercase						:		Any   					=		get_dataloaders(A							,A							)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowercase						:		Optional[int]   					=		AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,return_dict=A							)

	# 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).
	lowercase						:		Optional[int]   					=		model.to(accelerator.device							)

	# Instantiate optimizer
	lowercase						:		Tuple   					=		AdamW(params=model.parameters()							,lr=A							)

	# Instantiate scheduler
	lowercase						:		Union[str, Any]   					=		get_linear_schedule_with_warmup(
	    optimizer=A							,num_warmup_steps=1_0_0							,num_training_steps=(len(A							) * 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.
	lowercase					,		lowercase					,		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		accelerator.prepare(
	    A							,A							,A							,A							,A							)

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

		model.eval()
		for step, batch in enumerate(A							):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device							)
			with torch.no_grad():
				lowercase						:		Dict   					=		model(**A							)
			lowercase						:		Tuple   					=		outputs.logits.argmax(dim=-1							)
			lowercase					,		lowercase						:		str   					=		accelerator.gather_for_metrics((predictions, batch["labels"])							)
			metric.add_batch(
			    predictions=A							,references=A							,)

		lowercase						:		Any   					=		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''							,A							)




def 	_A  (       )  ->				Dict:
	lowercase						:		Optional[int]   					=		argparse.ArgumentParser(description="Simple example of training script."							)
	parser.add_argument(
	    "--mixed_precision"							,type=A							,default=A							,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=A							,default=1							,help="The number of minibatches to be ran before gradients are accumulated."							,)
	parser.add_argument(
	    "--local_sgd_steps"							,type=A							,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."							)
	lowercase						:		Optional[Any]   					=		parser.parse_args()
	lowercase						:		Union[str, Any]   					=		{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(A							,A							)


if __name__ == "__main__":
					main()


