






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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      :      Tuple             =       16
__snake_case      :      List[str]             =       32







def 		_UpperCamelCase				(      UpperCamelCase_    :   Accelerator      ,    UpperCamelCase_    :   int = 16						)					->				List[Any]:

						"""simple docstring"""



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

						def tokenize_function(UpperCamelCase_    :   Tuple						):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__        =  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():
												lowerCAmelCase__        =  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
						lowerCAmelCase__        =  tokenized_datasets.rename_column('label'      ,    'labels'						)

						def collate_fn(UpperCamelCase_    :   List[str]						):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowerCAmelCase__        =  128 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__        =  16
												elif accelerator.mixed_precision != "no":
																		lowerCAmelCase__        =  8
												else:
																		lowerCAmelCase__        =  None

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

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

						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

					__snake_case      :      Optional[int]             =       mocked_dataloaders  # noqa: F811







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Optional[Any]						)					->				Union[str, Any]:

						"""simple docstring"""



						if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,    UpperCamelCase_						) == "1":
												lowerCAmelCase__        =  2
						# New Code #
						lowerCAmelCase__        =  int(args.gradient_accumulation_steps						)
						lowerCAmelCase__        =  int(args.local_sgd_steps						)
						# Initialize accelerator
						lowerCAmelCase__        =  Accelerator(
						    cpu=args.cpu      ,    mixed_precision=args.mixed_precision      ,    gradient_accumulation_steps=UpperCamelCase_						)
						if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
												raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'						)
						# 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'						)

						set_seed(UpperCamelCase_						)
						lowerCAmelCase__  ,    lowerCAmelCase__        =  get_dataloaders(UpperCamelCase_      ,    UpperCamelCase_						)
						# 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=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).
						lowerCAmelCase__        =  model.to(accelerator.device						)

						# Instantiate optimizer
						lowerCAmelCase__        =  AdamW(params=model.parameters()      ,    lr=UpperCamelCase_						)

						# Instantiate scheduler
						lowerCAmelCase__        =  get_linear_schedule_with_warmup(
						    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    num_training_steps=(len(UpperCamelCase_						) * num_epochs)      ,    )

						# 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(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)

						# Now we train the model
						for epoch in range(UpperCamelCase_						):
												model.train()
												with LocalSGD(
												    accelerator=UpperCamelCase_      ,    model=UpperCamelCase_      ,    local_sgd_steps=UpperCamelCase_      ,    enabled=local_sgd_steps is not None						) as local_sgd:
																		for step, batch in enumerate(UpperCamelCase_						):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device						)
																								# New code #
																								# We use the new `accumulate` context manager to perform gradient accumulation
																								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																								with accelerator.accumulate(UpperCamelCase_						):
																														lowerCAmelCase__        =  model(**UpperCamelCase_						)
																														lowerCAmelCase__        =  output.loss
																														accelerator.backward(UpperCamelCase_						)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# LocalSGD-specific line
																														local_sgd.step()

												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():
																								lowerCAmelCase__        =  model(**UpperCamelCase_						)
																		lowerCAmelCase__        =  outputs.logits.argmax(dim=-1						)
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  accelerator.gather_for_metrics((predictions, batch['labels'])						)
																		metric.add_batch(
																		    predictions=UpperCamelCase_      ,    references=UpperCamelCase_      ,    )

												lowerCAmelCase__        =  metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_						)







def 		_UpperCamelCase				(      )					->				Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  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.'      ,    )
						# New Code #
						parser.add_argument(
						    '--gradient_accumulation_steps'      ,    type=UpperCamelCase_      ,    default=1      ,    help='The number of minibatches to be ran before gradients are accumulated.'      ,    )
						parser.add_argument(
						    '--local_sgd_steps'      ,    type=UpperCamelCase_      ,    default=8      ,    help='Number of local SGD steps or None to disable local SGD'						)
						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': 42, 'batch_size': 16}
						training_function(UpperCamelCase_      ,    UpperCamelCase_						)


if __name__ == "__main__":
					main()













import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Tuple						=   BarthezTokenizer
						_SCREAMING_SNAKE_CASE		:    int						=   BarthezTokenizerFast
						_SCREAMING_SNAKE_CASE		:    Dict						=   True
						_SCREAMING_SNAKE_CASE		:    Tuple						=   True






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

												lowerCAmelCase__        =  BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'				)
												tokenizer.save_pretrained(self.tmpdirname				)
												tokenizer.save_pretrained(self.tmpdirname       ,  legacy_format=_UpperCamelCase				)
												lowerCAmelCase__        =  tokenizer






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  '<pad>'
												lowerCAmelCase__        =  1

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase				)       ,  _UpperCamelCase				)
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase				)       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  list(self.get_tokenizer().get_vocab().keys()				)

												self.assertEqual(vocab_keys[0]       ,  '<s>'				)
												self.assertEqual(vocab_keys[1]       ,  '<pad>'				)
												self.assertEqual(vocab_keys[-1]       ,  '<mask>'				)
												self.assertEqual(len(_UpperCamelCase				)       ,  10_11_22				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.assertEqual(self.get_tokenizer().vocab_size       ,  10_11_22				)






						@require_torch
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
												lowerCAmelCase__        =  [0, 57, 30_18, 7_03_07, 91, 2]

												lowerCAmelCase__        =  self.tokenizer(
												    _UpperCamelCase       ,  max_length=len(_UpperCamelCase				)       ,  padding=_UpperCamelCase       ,  truncation=_UpperCamelCase       ,  return_tensors='pt'				)
												self.assertIsInstance(_UpperCamelCase       ,  _UpperCamelCase				)

												self.assertEqual((2, 6)       ,  batch.input_ids.shape				)
												self.assertEqual((2, 6)       ,  batch.attention_mask.shape				)
												lowerCAmelCase__        =  batch.input_ids.tolist()[0]
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if not self.test_rust_tokenizer:
																		return

												lowerCAmelCase__        =  self.get_tokenizer()
												lowerCAmelCase__        =  self.get_rust_tokenizer()

												lowerCAmelCase__        =  'I was born in 92000, and this is falsé.'

												lowerCAmelCase__        =  tokenizer.tokenize(_UpperCamelCase				)
												lowerCAmelCase__        =  rust_tokenizer.tokenize(_UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  tokenizer.encode(_UpperCamelCase       ,  add_special_tokens=_UpperCamelCase				)
												lowerCAmelCase__        =  rust_tokenizer.encode(_UpperCamelCase       ,  add_special_tokens=_UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)

												lowerCAmelCase__        =  self.get_rust_tokenizer()
												lowerCAmelCase__        =  tokenizer.encode(_UpperCamelCase				)
												lowerCAmelCase__        =  rust_tokenizer.encode(_UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# fmt: off
												lowerCAmelCase__        =  {'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
												# fmt: on

												# moussaKam/mbarthez is a french model. So we also use french texts.
												lowerCAmelCase__        =  [
												    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
												    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
												    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
												    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
												    'telles que la traduction et la synthèse de texte.',
												]

												self.tokenizer_integration_test_util(
												    expected_encoding=_UpperCamelCase       ,  model_name='moussaKam/mbarthez'       ,  revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,  sequences=_UpperCamelCase       ,  )





