"""simple docstring"""







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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


__SCREAMING_SNAKE_CASE    	=16
__SCREAMING_SNAKE_CASE    	=32



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     Tuple = 16      ):
     lowercase_	:				Dict													=	AutoTokenizer.from_pretrained('bert-base-cased'      )
     lowercase_	:				Tuple													=	load_dataset('glue'     ,			'mrpc'      )

     def tokenize_function(__SCREAMING_SNAKE_CASE							:     str      ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_	:				Tuple													=	tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=_snake_case     ,			max_length=_snake_case      )
          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():
          lowercase_	:				List[str]													=	datasets.map(
              _snake_case     ,			batched=_snake_case     ,			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
     lowercase_	:				Any													=	tokenized_datasets.rename_column('label'     ,			'labels'      )

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

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

     # Instantiate dataloaders.
     lowercase_	:				Union[str, Any]													=	DataLoader(
         tokenized_datasets['train']     ,			shuffle=_snake_case     ,			collate_fn=_snake_case     ,			batch_size=_snake_case      )
     lowercase_	:				int													=	DataLoader(
         tokenized_datasets['validation']     ,			shuffle=_snake_case     ,			collate_fn=_snake_case     ,			batch_size=_snake_case      )

     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

       __SCREAMING_SNAKE_CASE    	=mocked_dataloaders  # noqa: F811



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Dict      ):
     # For testing only
     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,			_snake_case      ) == "1":
          lowercase_	:				Optional[int]													=	2
     # Initialize accelerator
     lowercase_	:				int													=	Accelerator(cpu=args.cpu     ,			mixed_precision=args.mixed_precision      )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_	:				List[str]													=	config["""lr"""]
     lowercase_	:				Union[str, Any]													=	int(config['num_epochs']      )
     lowercase_	:				List[str]													=	int(config['seed']      )
     lowercase_	:				Optional[Any]													=	int(config['batch_size']      )

     lowercase_	:				Union[str, Any]													=	evaluate.load('glue'     ,			'mrpc'      )

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

     set_seed(_snake_case      )
     lowercase_	:				List[Any]													=	get_dataloaders(_snake_case     ,			_snake_case      )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_	:				List[Any]													=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,			return_dict=_snake_case      )

     # 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).
     lowercase_	:				Dict													=	model.to(accelerator.device      )

     # Instantiate optimizer
     lowercase_	:				Dict													=	AdamW(params=model.parameters()     ,			lr=_snake_case      )

     # Instantiate scheduler
     lowercase_	:				Dict													=	get_linear_schedule_with_warmup(
         optimizer=_snake_case     ,			num_warmup_steps=1_00     ,			num_training_steps=(len(_snake_case      ) * 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.
     lowercase_	:				str													=	accelerator.prepare(
         _snake_case     ,			_snake_case     ,			_snake_case     ,			_snake_case     ,			_snake_case      )

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

          model.eval()
          lowercase_	:				Optional[int]													=	0
          for step, batch in enumerate(_snake_case      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    lowercase_	:				List[str]													=	model(**_snake_case      )
               lowercase_	:				Optional[int]													=	outputs.logits.argmax(dim=-1      )
               lowercase_	:				List[str]													=	accelerator.gather((predictions, batch['labels'])      )
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(_snake_case      ) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         lowercase_	:				Any													=	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                         lowercase_	:				Optional[Any]													=	references[: len(eval_dataloader.dataset      ) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=_snake_case     ,			references=_snake_case     ,			)

          lowercase_	:				Optional[int]													=	metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''     ,			_snake_case      )



def 			lowercase__(       ):
     lowercase_	:				str													=	argparse.ArgumentParser(description='Simple example of training script.'      )
     parser.add_argument(
         '--mixed_precision'     ,			type=_snake_case     ,			default=_snake_case     ,			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.'      )
     lowercase_	:				Dict													=	parser.parse_args()
     lowercase_	:				List[str]													=	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(_snake_case     ,			_snake_case      )


if __name__ == "__main__":
       main()



"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




