



'''simple docstring'''
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
#
########################################################################


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






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_ = 1_6							):
						_UpperCamelCase					:		Union[str, Any] 				=   AutoTokenizer.from_pretrained('bert-base-cased'							)
						_UpperCamelCase					:		str 				=   load_dataset('glue'  ,  'mrpc'							)

						def tokenize_function(UpperCAmelCase_							):
												# max_length=None => use the model max length (it's actually the default)
												_UpperCamelCase					:		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():
												_UpperCamelCase					:		List[str] 				=   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
						_UpperCamelCase					:		str 				=   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.
												_UpperCamelCase					:		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":
																		_UpperCamelCase					:		int 				=   1_6
												elif accelerator.mixed_precision != "no":
																		_UpperCamelCase					:		str 				=   8
												else:
																		_UpperCamelCase					:		str 				=   None

												return tokenizer.pad(
												    UpperCAmelCase_  ,  padding='longest'  ,  max_length=UpperCAmelCase_  ,  pad_to_multiple_of=UpperCAmelCase_  ,  return_tensors='pt'  ,  )

						# Instantiate dataloaders.
						_UpperCamelCase					:		List[str] 				=   DataLoader(
						    tokenized_datasets['train']  ,  shuffle=UpperCAmelCase_  ,  collate_fn=UpperCAmelCase_  ,  batch_size=UpperCAmelCase_  ,  drop_last=UpperCAmelCase_							)
						_UpperCamelCase					:		List[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
						_UpperCamelCase					:		Optional[Any] 				=   Accelerator(cpu=args.cpu  ,  mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCamelCase					:		Optional[int] 				=   config['lr']
						_UpperCamelCase					:		Optional[int] 				=   int(config['num_epochs']							)
						_UpperCamelCase					:		Tuple 				=   int(config['seed']							)
						_UpperCamelCase					:		int 				=   int(config['batch_size']							)

						_UpperCamelCase					:		Tuple 				=   evaluate.load('glue'  ,  'mrpc'							)

						# If the batch size is too big we use gradient accumulation
						_UpperCamelCase					:		List[Any] 				=   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												_UpperCamelCase					:		Union[str, Any] 				=   batch_size // MAX_GPU_BATCH_SIZE
												_UpperCamelCase					:		Union[str, Any] 				=   MAX_GPU_BATCH_SIZE

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

						# Instantiate scheduler
						_UpperCamelCase					:		str 				=   get_linear_schedule_with_warmup(
						    optimizer=UpperCAmelCase_  ,  num_warmup_steps=1_0_0  ,  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.

						_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase				,			_UpperCamelCase					:		Optional[Any] 				=   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							)
																		_UpperCamelCase					:		Optional[Any] 				=   model(**UpperCAmelCase_							)
																		_UpperCamelCase					:		Tuple 				=   outputs.loss
																		_UpperCamelCase					:		Any 				=   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():
																								_UpperCamelCase					:		int 				=   model(**UpperCAmelCase_							)
																		_UpperCamelCase					:		Union[str, Any] 				=   outputs.logits.argmax(dim=-1							)
																		_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   accelerator.gather_for_metrics((predictions, batch['labels'])							)
																		metric.add_batch(
																		    predictions=UpperCAmelCase_  ,  references=UpperCAmelCase_  ,  )

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






def A__    (     ):
						_UpperCamelCase					:		int 				=   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.'							)
						_UpperCamelCase					:		int 				=   parser.parse_args()
						_UpperCamelCase					:		List[str] 				=   {'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
						training_function(UpperCAmelCase_  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					main()



import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							ComputeEnvironment.AMAZON_SAGEMAKER
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							"ml.p3.2xlarge"
   lowerCAmelCase_														=							"accelerate_sagemaker_execution_role"
   lowerCAmelCase_														=							"hf-sm"
   lowerCAmelCase_														=							"us-east-1"
   lowerCAmelCase_														=							1
   lowerCAmelCase_														=							"accelerate-sagemaker-1"
   lowerCAmelCase_														=							"1.6"
   lowerCAmelCase_														=							"4.4"
   lowerCAmelCase_														=							"train.py"
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "False",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]
   lowerCAmelCase_														=							[
       "--model_name_or_path",
       "bert",
       "--do_train",
       "--do_test",
       "False",
       "--do_predict",
       "--epochs",
       "3",
       "--learning_rate",
       "5e-5",
       "--max_steps",
       "50.5",
   ]







class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
     assert isinstance(converted_args["""model_name_or_path"""]       ,	_lowercase      )
     assert isinstance(converted_args["""do_train"""]       ,	_lowercase      )
     assert isinstance(converted_args["""epochs"""]       ,	_lowercase      )
     assert isinstance(converted_args["""learning_rate"""]       ,	_lowercase      )
     assert isinstance(converted_args["""max_steps"""]       ,	_lowercase      )

     with pytest.raises(_lowercase      ):
       _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )
