

'''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
#
########################################################################


lowercase_     		= 16
lowercase_     		= 32



def 							UpperCamelCase__			(			a__			,      a__ = 1_6      ):


						'''simple docstring'''






						_lowerCAmelCase										=AutoTokenizer.from_pretrained('bert-base-cased'      )
						_lowerCAmelCase										=load_dataset('glue'			,      'mrpc'      )

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

						def collate_fn(a__      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_lowerCAmelCase										=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":
																		_lowerCAmelCase										=1_6
												elif accelerator.mixed_precision != "no":
																		_lowerCAmelCase										=8
												else:
																		_lowerCAmelCase										=None

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

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

						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

							lowercase_     		= mocked_dataloaders  # noqa: F811



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						if os.environ.get('TESTING_MOCKED_DATALOADERS'			,      a__      ) == "1":
												_lowerCAmelCase										=2
						# Initialize accelerator
						_lowerCAmelCase										=Accelerator(cpu=args.cpu			,      mixed_precision=args.mixed_precision      )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_lowerCAmelCase										=config['lr']
						_lowerCAmelCase										=int(config['num_epochs']      )
						_lowerCAmelCase										=int(config['seed']      )
						_lowerCAmelCase										=int(config['batch_size']      )

						_lowerCAmelCase										=evaluate.load('glue'			,      'mrpc'      )

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

						set_seed(a__      )
						_lowerCAmelCase						,				_lowerCAmelCase										=get_dataloaders(a__			,      a__      )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_lowerCAmelCase										=AutoModelForSequenceClassification.from_pretrained('bert-base-cased'			,      return_dict=a__      )

						# 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).
						_lowerCAmelCase										=model.to(accelerator.device      )

						# Instantiate optimizer
						_lowerCAmelCase										=AdamW(params=model.parameters()			,      lr=a__      )

						# Instantiate scheduler
						_lowerCAmelCase										=get_linear_schedule_with_warmup(
						    optimizer=a__			,      num_warmup_steps=1_0_0			,      num_training_steps=(len(a__      ) * 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.
						_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=accelerator.prepare(
						    a__			,      a__			,      a__			,      a__			,      a__      )

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

												model.eval()
												_lowerCAmelCase										=0
												for step, batch in enumerate(a__      ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device      )
																		with torch.no_grad():
																								_lowerCAmelCase										=model(**a__      )
																		_lowerCAmelCase										=outputs.logits.argmax(dim=-1      )
																		_lowerCAmelCase						,				_lowerCAmelCase										=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(a__      ) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														_lowerCAmelCase										=predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																														_lowerCAmelCase										=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=a__			,      references=a__			,      )

												_lowerCAmelCase										=metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''			,      a__      )



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser(description='Simple example of training script.'      )
						parser.add_argument(
						    '--mixed_precision'			,      type=a__			,      default=a__			,      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.'      )
						_lowerCAmelCase										=parser.parse_args()
						_lowerCAmelCase										={'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
						training_function(a__			,      a__      )


if __name__ == "__main__":
							main()






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





