



"""simple docstring"""


import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


__A   :							List[str]										=   16
__A   :							Any										=   32







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Accelerator					,     _SCREAMING_SNAKE_CASE   :						int = 16					,     _SCREAMING_SNAKE_CASE   :						str = "bert-base-cased"				):






				'''simple docstring'''



				_UpperCAmelCase					=    AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    load_dataset('''glue'''					,     '''mrpc'''				)

				def tokenize_function(_SCREAMING_SNAKE_CASE   :						List[str]				):
								# max_length=None => use the model max length (it's actually the default)
								_UpperCAmelCase					=    tokenizer(examples['''sentence1''']					,     examples['''sentence2''']					,     truncation=_SCREAMING_SNAKE_CASE					,     max_length=_SCREAMING_SNAKE_CASE				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_UpperCAmelCase					=    datasets.map(
				    _SCREAMING_SNAKE_CASE					,     batched=_SCREAMING_SNAKE_CASE					,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,     load_from_cache_file=_SCREAMING_SNAKE_CASE				)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_UpperCAmelCase					=    tokenized_datasets.rename_column('''label'''					,     '''labels'''				)

				def collate_fn(_SCREAMING_SNAKE_CASE   :						str				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(_SCREAMING_SNAKE_CASE					,     padding='''max_length'''					,     max_length=128					,     return_tensors='''pt'''				)
								return tokenizer.pad(_SCREAMING_SNAKE_CASE					,     padding='''longest'''					,     return_tensors='''pt'''				)

				# Instantiate dataloaders.
				_UpperCAmelCase					=    DataLoader(
				    tokenized_datasets['''train''']					,     shuffle=_SCREAMING_SNAKE_CASE					,     collate_fn=_SCREAMING_SNAKE_CASE					,     batch_size=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    DataLoader(
				    tokenized_datasets['''validation''']					,     shuffle=_SCREAMING_SNAKE_CASE					,     collate_fn=_SCREAMING_SNAKE_CASE					,     batch_size=_SCREAMING_SNAKE_CASE				)

				return train_dataloader, eval_dataloader







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				model.eval()
				_UpperCAmelCase					=    0
				for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device				)
								with torch.no_grad():
												_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    outputs.logits.argmax(dim=-1				)
								# It is slightly faster to call this once, than multiple times
								_UpperCAmelCase		,						_UpperCAmelCase					=    accelerator.gather(
								    (predictions, batch['''labels'''])				)  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(_SCREAMING_SNAKE_CASE				) - 1:
																_UpperCAmelCase					=    predictions[: len(eval_dataloader.dataset				) - samples_seen]
																_UpperCAmelCase					=    references[: len(eval_dataloader.dataset				) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=_SCREAMING_SNAKE_CASE					,     references=_SCREAMING_SNAKE_CASE					,     )

				_UpperCAmelCase					=    metric.compute()
				return eval_metric["accuracy"]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_UpperCAmelCase					=    config['''lr''']
				_UpperCAmelCase					=    int(config['''num_epochs''']				)
				_UpperCAmelCase					=    int(config['''seed''']				)
				_UpperCAmelCase					=    int(config['''batch_size''']				)
				_UpperCAmelCase					=    args.model_name_or_path

				set_seed(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    get_dataloaders(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_UpperCAmelCase					=    AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE					,     return_dict=_SCREAMING_SNAKE_CASE				)

				# Instantiate optimizer
				_UpperCAmelCase					=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_UpperCAmelCase					=    optimizer_cls(params=model.parameters()					,     lr=_SCREAMING_SNAKE_CASE				)

				if accelerator.state.deepspeed_plugin is not None:
								_UpperCAmelCase					=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								_UpperCAmelCase					=    1
				_UpperCAmelCase					=    (len(_SCREAMING_SNAKE_CASE				) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_UpperCAmelCase					=    get_linear_schedule_with_warmup(
								    optimizer=_SCREAMING_SNAKE_CASE					,     num_warmup_steps=0					,     num_training_steps=_SCREAMING_SNAKE_CASE					,     )
				else:
								_UpperCAmelCase					=    DummyScheduler(_SCREAMING_SNAKE_CASE					,     total_num_steps=_SCREAMING_SNAKE_CASE					,     warmup_num_steps=0				)

				# 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.
				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    accelerator.prepare(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				# We need to keep track of how many total steps we have iterated over
				_UpperCAmelCase					=    0
				# We also need to keep track of the stating epoch so files are named properly
				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    evaluate.load('''glue'''					,     '''mrpc'''				)
				_UpperCAmelCase					=    num_epochs

				if args.partial_train_epoch is not None:
								_UpperCAmelCase					=    args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint				)
								_UpperCAmelCase					=    args.resume_from_checkpoint.split('''epoch_'''				)[1]
								_UpperCAmelCase					=    ''''''
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_UpperCAmelCase					=    int(_SCREAMING_SNAKE_CASE				) + 1
								_UpperCAmelCase					=    evaluation_loop(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								accelerator.print('''resumed checkpoint performance:'''					,     _SCREAMING_SNAKE_CASE				)
								accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''					,     lr_scheduler.get_lr()[0]				)
								accelerator.print('''resumed optimizers\'s lr:'''					,     optimizer.param_groups[0]['''lr''']				)
								with open(os.path.join(args.output_dir					,     f'state_{starting_epoch-1}.json'				)					,     '''r'''				) as f:
												_UpperCAmelCase					=    json.load(_SCREAMING_SNAKE_CASE				)
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				_UpperCAmelCase					=    {}
				for epoch in range(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
								model.train()
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
												_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    outputs.loss
												_UpperCAmelCase					=    loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_UpperCAmelCase					=    f'epoch_{epoch}'
								_UpperCAmelCase					=    os.path.join(args.output_dir					,     _SCREAMING_SNAKE_CASE				)
								accelerator.save_state(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    evaluation_loop(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    accuracy
								_UpperCAmelCase					=    lr_scheduler.get_lr()[0]
								_UpperCAmelCase					=    optimizer.param_groups[0]['''lr''']
								_UpperCAmelCase					=    epoch
								_UpperCAmelCase					=    overall_step
								accelerator.print(f'epoch {epoch}:'					,     _SCREAMING_SNAKE_CASE				)

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir					,     f'state_{epoch}.json'				)					,     '''w'''				) as f:
																json.dump(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''				)
				parser.add_argument(
				    '''--model_name_or_path'''					,     type=_SCREAMING_SNAKE_CASE					,     default='''bert-base-cased'''					,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,     required=_SCREAMING_SNAKE_CASE					,     )
				parser.add_argument(
				    '''--output_dir'''					,     type=_SCREAMING_SNAKE_CASE					,     default='''.'''					,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''					,     )
				parser.add_argument(
				    '''--resume_from_checkpoint'''					,     type=_SCREAMING_SNAKE_CASE					,     default=_SCREAMING_SNAKE_CASE					,     help='''If the training should continue from a checkpoint folder.'''					,     )
				parser.add_argument(
				    '''--partial_train_epoch'''					,     type=_SCREAMING_SNAKE_CASE					,     default=_SCREAMING_SNAKE_CASE					,     help='''If passed, the training will stop after this number of epochs.'''					,     )
				parser.add_argument(
				    '''--num_epochs'''					,     type=_SCREAMING_SNAKE_CASE					,     default=2					,     help='''Number of train epochs.'''					,     )
				_UpperCAmelCase					=    parser.parse_args()
				_UpperCAmelCase					=    {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

				training_function(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					main()








"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    inspect.getfile(accelerate.test_utils   )
							_UpperCAmelCase					=    os.path.sep.join(
							    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']   )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCAmelCase					=    test_metrics



			@require_cpu
			def  lowercase__						(		self     :    Any   )->int:
							debug_launcher(self.test_metrics.main				,   num_processes=1   )



			@require_cpu
			def  lowercase__						(		self     :    List[str]   )->List[str]:
							debug_launcher(self.test_metrics.main   )



			@require_single_gpu
			def  lowercase__						(		self     :    List[Any]   )->Dict:
							self.test_metrics.main()



			@require_multi_gpu
			def  lowercase__						(		self     :    str   )->int:
							print(F'Found {torch.cuda.device_count()} devices.'   )
							_UpperCAmelCase					=    ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1   ):
											execute_subprocess_async(__UpperCamelCase				,   env=os.environ.copy()   )



