


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							:					List[str]     						=     16
A							:					Tuple     						=     32





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase = 16 )						->	Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		AutoTokenizer.from_pretrained("bert-base-cased" )
						__snake_case				=		load_dataset("glue"      ,	"mrpc" )

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

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

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

						# Instantiate dataloaders.
						__snake_case				=		DataLoader(
						    tokenized_datasets["train"]      ,	shuffle=_lowerCAmelCase      ,	collate_fn=_lowerCAmelCase      ,	batch_size=_lowerCAmelCase )
						__snake_case				=		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

	A							:					int     						=     mocked_dataloaders  # noqa: F811





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						if os.environ.get("TESTING_MOCKED_DATALOADERS"      ,	_lowerCAmelCase ) == "1":
												__snake_case				=		2
						# New Code #
						__snake_case				=		int(args.gradient_accumulation_steps )
						__snake_case				=		int(args.local_sgd_steps )
						# Initialize accelerator
						__snake_case				=		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
						__snake_case				=		config["lr"]
						__snake_case				=		int(config["num_epochs"] )
						__snake_case				=		int(config["seed"] )
						__snake_case				=		int(config["batch_size"] )

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

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

						# Instantiate optimizer
						__snake_case				=		AdamW(params=model.parameters()      ,	lr=_lowerCAmelCase )

						# Instantiate scheduler
						__snake_case				=		get_linear_schedule_with_warmup(
						    optimizer=_lowerCAmelCase      ,	num_warmup_steps=100      ,	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.
						__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		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 ):
																														__snake_case				=		model(**_lowerCAmelCase )
																														__snake_case				=		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():
																								__snake_case				=		model(**_lowerCAmelCase )
																		__snake_case				=		outputs.logits.argmax(dim=-1 )
																		__snake_case		,					__snake_case				=		accelerator.gather_for_metrics((predictions, batch["labels"]) )
																		metric.add_batch(
																		    predictions=_lowerCAmelCase      ,	references=_lowerCAmelCase      ,	)

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





def 	_lowerCAmelCase       (    )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		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." )
						__snake_case				=		parser.parse_args()
						__snake_case				=		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_lowerCAmelCase      ,	_lowerCAmelCase )


if __name__ == "__main__":
	main()










from __future__ import annotations

from collections.abc import Iterator






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	None:




											'''simple docstring'''

											__snake_case				=		value
											__snake_case				=		None
											__snake_case				=		None






class     UpperCamelCase:


					def __init__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Node						)			->	None:




											'''simple docstring'''

											__snake_case				=		tree


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Node | None						)			->	int:




											'''simple docstring'''

											if node is None:
																	return 0
											return node.value + (
											    self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
											)






					def __iter__(						self	:		int						)			->	Iterator[int]:




											'''simple docstring'''

											yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





