

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase							:  Union[str, Any]   	=						"""true"""







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=82				,	_UpperCAmelCase=16    ):
						set_seed(42    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       RegressionModel()
						SCREAMING_SNAKE_CASE_: List[str]  		=       deepcopy(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       RegressionDataset(length=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       DataLoader(_UpperCAmelCase				,	batch_size=_UpperCAmelCase    )
						model.to(accelerator.device    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						return model, ddp_model, dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: int  		=       load_dataset("glue"				,	"mrpc"				,	split="validation"    )

						def tokenize_function(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: int  		=       tokenizer(examples["sentence1"]				,	examples["sentence2"]				,	truncation=_UpperCAmelCase				,	max_length=_UpperCAmelCase    )
												return outputs

						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE_: int  		=       dataset.map(
												    _UpperCAmelCase				,	batched=_UpperCAmelCase				,	remove_columns=["idx", "sentence1", "sentence2"]				,	)

						SCREAMING_SNAKE_CASE_: Dict  		=       tokenized_datasets.rename_column("label"				,	"labels"    )

						def collate_fn(_UpperCAmelCase    ):
												if use_longest:
																		return tokenizer.pad(_UpperCAmelCase				,	padding="longest"				,	return_tensors="pt"    )
												return tokenizer.pad(_UpperCAmelCase				,	padding="max_length"				,	max_length=1_28				,	return_tensors="pt"    )

						return DataLoader(_UpperCAmelCase				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=16    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       Accelerator(dispatch_batches=_UpperCAmelCase				,	split_batches=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Dict  		=       get_dataloader(_UpperCAmelCase				,	not dispatch_batches    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       AutoModelForSequenceClassification.from_pretrained(
						    "hf-internal-testing/mrpc-bert-base-cased"				,	return_dict=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       accelerator.prepare(_UpperCAmelCase				,	_UpperCAmelCase    )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       []
						for batch in dataloader:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       batch.values()
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       accelerator.gather_for_metrics((logit, target)    )
																		logits_and_targets.append((logit, target)    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       [], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCAmelCase    )
												targs.append(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       torch.cat(_UpperCAmelCase    ), torch.cat(_UpperCAmelCase    )
						return logits, targs







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=82				,	_UpperCAmelCase=False				,	_UpperCAmelCase=False				,	_UpperCAmelCase=16    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       get_basic_setup(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       generate_predictions(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						assert (
						    len(_UpperCAmelCase    ) == num_samples
						), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCAmelCase    )}"







def 			A_      (    _UpperCAmelCase = False				,	_UpperCAmelCase = False    ):
						SCREAMING_SNAKE_CASE_: int  		=       evaluate.load("glue"				,	"mrpc"    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       get_mrpc_setup(_UpperCAmelCase				,	_UpperCAmelCase    )
						# First do baseline
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       setup["no"]
						model.to(_UpperCAmelCase    )
						model.eval()
						for batch in dataloader:
												batch.to(_UpperCAmelCase    )
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model(**_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: List[str]  		=       outputs.logits.argmax(dim=-1    )
												metric.add_batch(predictions=_UpperCAmelCase				,	references=batch["labels"]    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       metric.compute()

						# Then do distributed
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       setup["ddp"]
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(**_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       outputs.logits.argmax(dim=-1    )
												SCREAMING_SNAKE_CASE_: Any  		=       batch["labels"]
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       accelerator.gather_for_metrics((preds, references)    )
												metric.add_batch(predictions=_UpperCAmelCase				,	references=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]				,	distributed[key]    ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       Accelerator(split_batches=_UpperCAmelCase				,	dispatch_batches=_UpperCAmelCase    )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print("**Testing gather_for_metrics**"    )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"    )
																								test_mrpc(_UpperCAmelCase				,	_UpperCAmelCase    )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("**Test torch metrics**"    )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		SCREAMING_SNAKE_CASE_: int  		=       Accelerator(split_batches=_UpperCAmelCase				,	dispatch_batches=_UpperCAmelCase    )
																		if accelerator.is_local_main_process:
																								print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"    )
																		test_torch_metrics(_UpperCAmelCase				,	99    )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("**Test last batch is not dropped when perfectly divisible**"    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       Accelerator()
						test_torch_metrics(_UpperCAmelCase				,	5_12    )
						accelerator.state._reset_state()







def 			A_      (    _UpperCAmelCase    ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()









import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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							:  Optional[Any]   	=						16
lowerCAmelCase							:  List[str]   	=						32







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase = 16    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       AutoTokenizer.from_pretrained("bert-base-cased"    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       load_dataset("glue"				,	"mrpc"    )

						def tokenize_function(_UpperCAmelCase    ):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_: Any  		=       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():
												SCREAMING_SNAKE_CASE_: Tuple  		=       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
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       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_: List[str]  		=       1_28 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_: List[Any]  		=       16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       8
												else:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       None

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_: Dict  		=       DataLoader(
						    tokenized_datasets["train"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       DataLoader(
						    tokenized_datasets["validation"]				,	shuffle=_UpperCAmelCase				,	collate_fn=_UpperCAmelCase				,	batch_size=_UpperCAmelCase				,	drop_last=(accelerator.mixed_precision == "fp8")				,	)

						return train_dataloader, eval_dataloader







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_: str  		=       Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE_: int  		=       config["lr"]
						SCREAMING_SNAKE_CASE_: Any  		=       int(config["num_epochs"]    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       int(config["seed"]    )
						SCREAMING_SNAKE_CASE_: List[Any]  		=       int(config["batch_size"]    )

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

						# If the batch size is too big we use gradient accumulation
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												SCREAMING_SNAKE_CASE_: Tuple  		=       batch_size // MAX_GPU_BATCH_SIZE
												SCREAMING_SNAKE_CASE_: Dict  		=       MAX_GPU_BATCH_SIZE

						set_seed(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       get_dataloaders(_UpperCAmelCase				,	_UpperCAmelCase    )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE_: List[Any]  		=       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).
						SCREAMING_SNAKE_CASE_: Tuple  		=       model.to(accelerator.device    )
						# Instantiate optimizer
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       AdamW(params=model.parameters()				,	lr=_UpperCAmelCase    )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase				,	num_warmup_steps=1_00				,	num_training_steps=(len(_UpperCAmelCase    ) * num_epochs) // gradient_accumulation_steps				,	)

						# 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_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[int]  		=       accelerator.prepare(
						    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						# Now we train the model
						for epoch in range(_UpperCAmelCase    ):
												model.train()
												for step, batch in enumerate(_UpperCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		SCREAMING_SNAKE_CASE_: Tuple  		=       model(**_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.loss
																		SCREAMING_SNAKE_CASE_: Tuple  		=       loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												SCREAMING_SNAKE_CASE_: List[Any]  		=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"				,	_UpperCAmelCase    )







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Any  		=       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."				,	)
						parser.add_argument("--cpu"				,	action="store_true"				,	help="If passed, will train on the CPU."    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       parser.parse_args()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase				,	_UpperCAmelCase    )


if __name__ == "__main__":
						main()






