


"""simple docstring"""


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


A  :     List[str]    					=    "true"


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=82					,				_UpperCamelCase=16   ):

			'''simple docstring'''




			set_seed(42   )
			__lowerCAmelCase       					=    RegressionModel()
			__lowerCAmelCase       					=    deepcopy(_UpperCamelCase   )
			__lowerCAmelCase       					=    RegressionDataset(length=_UpperCamelCase   )
			__lowerCAmelCase       					=    DataLoader(_UpperCamelCase					,				batch_size=_UpperCamelCase   )
			model.to(accelerator.device   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    accelerator.prepare(_UpperCamelCase					,				_UpperCamelCase   )
			return model, ddp_model, dataloader


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=False   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"   )
			__lowerCAmelCase       					=    load_dataset("glue"					,				"mrpc"					,				split="validation"   )

			def tokenize_function(_UpperCamelCase   ):
						__lowerCAmelCase       					=    tokenizer(examples["sentence1"]					,				examples["sentence2"]					,				truncation=_UpperCamelCase					,				max_length=_UpperCamelCase   )
						return outputs

			with accelerator.main_process_first():
						__lowerCAmelCase       					=    dataset.map(
						    _UpperCamelCase					,				batched=_UpperCamelCase					,				remove_columns=["idx", "sentence1", "sentence2"]					,				)

			__lowerCAmelCase       					=    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=128					,				return_tensors="pt"   )

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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    Accelerator(dispatch_batches=_UpperCamelCase					,				split_batches=_UpperCamelCase   )
			__lowerCAmelCase       					=    get_dataloader(_UpperCamelCase					,				not dispatch_batches   )
			__lowerCAmelCase       					=    AutoModelForSequenceClassification.from_pretrained(
			    "hf-internal-testing/mrpc-bert-base-cased"					,				return_dict=_UpperCamelCase   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    accelerator.prepare(_UpperCamelCase					,				_UpperCamelCase   )
			return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			for batch in dataloader:
						__lowerCAmelCase					,			__lowerCAmelCase       					=    batch.values()
						with torch.no_grad():
									__lowerCAmelCase       					=    model(_UpperCamelCase   )
									__lowerCAmelCase					,			__lowerCAmelCase       					=    accelerator.gather_for_metrics((logit, target)   )
									logits_and_targets.append((logit, target)   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    [], []
			for logit, targ in logits_and_targets:
						logits.append(_UpperCamelCase   )
						targs.append(_UpperCamelCase   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    torch.cat(_UpperCamelCase   ), torch.cat(_UpperCamelCase   )
			return logits, targs


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase=82					,				_UpperCamelCase=False					,				_UpperCamelCase=False					,				_UpperCamelCase=16   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    get_basic_setup(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    generate_predictions(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )
			assert (
			    len(_UpperCamelCase   ) == num_samples
			), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase   )}"


def _lowerCamelCase			(	_UpperCamelCase = False					,				_UpperCamelCase = False   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    evaluate.load("glue"					,				"mrpc"   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    get_mrpc_setup(_UpperCamelCase					,				_UpperCamelCase   )
			# First do baseline
			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    setup["no"]
			model.to(_UpperCamelCase   )
			model.eval()
			for batch in dataloader:
						batch.to(_UpperCamelCase   )
						with torch.inference_mode():
									__lowerCAmelCase       					=    model(**_UpperCamelCase   )
						__lowerCAmelCase       					=    outputs.logits.argmax(dim=-1   )
						metric.add_batch(predictions=_UpperCamelCase					,				references=batch["labels"]   )
			__lowerCAmelCase       					=    metric.compute()

			# Then do distributed
			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    setup["ddp"]
			model.eval()
			for batch in dataloader:
						with torch.inference_mode():
									__lowerCAmelCase       					=    model(**_UpperCamelCase   )
						__lowerCAmelCase       					=    outputs.logits.argmax(dim=-1   )
						__lowerCAmelCase       					=    batch["labels"]
						__lowerCAmelCase					,			__lowerCAmelCase       					=    accelerator.gather_for_metrics((preds, references)   )
						metric.add_batch(predictions=_UpperCamelCase					,				references=_UpperCamelCase   )
			__lowerCAmelCase       					=    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 _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    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]:
									__lowerCAmelCase       					=    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**"   )
			__lowerCAmelCase       					=    Accelerator()
			test_torch_metrics(_UpperCamelCase					,				512   )
			accelerator.state._reset_state()


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			main()


if __name__ == "__main__":
	main()








"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    psutil.Process()
										__lowerCAmelCase       					=    False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    -1

										while True:
													__lowerCAmelCase       					=    max(self.process.memory_info().rss					,     self.cpu_memory_peak			)

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													if not self.peak_monitoring:
																break

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    threading.Thread(target=self.peak_monitor			)
										__lowerCAmelCase       					=    True
										self.thread.start()



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    False
										self.thread.join()
										return self.cpu_memory_peak
A  :     Any    					=    PeakCPUMemory()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    torch.cuda.memory_allocated(_UpperCamelCase   )
			torch.cuda.reset_peak_memory_stats()

			return measures


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase       					=    (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    (torch.cuda.memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20
						__lowerCAmelCase       					=    (torch.cuda.max_memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20

			return measures


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			print(f"{description}:"   )
			print(f"- Time: {measures['time']:.2f}s"   )
			for i in range(torch.cuda.device_count()   ):
						print(f"- GPU {i} allocated: {measures[str(_UpperCamelCase   )]:.2f}MiB"   )
						__lowerCAmelCase       					=    measures[f"{i}-peak"]
						print(f"- GPU {i} peak: {peak:.2f}MiB"   )
			print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"   )
			print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"   )




