

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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


UpperCamelCase__	:   Any								=     16
UpperCamelCase__	:   Union[str, Any]								=     32


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		int							):





    """simple docstring"""


    if args.with_tracking:
        SCREAMING_SNAKE_CASE_						=    Accelerator(
            cpu=args.cpu			, mixed_precision=args.mixed_precision			, log_with='all'			, project_dir=args.project_dir							)
    else:
        SCREAMING_SNAKE_CASE_						=    Accelerator(cpu=args.cpu			, mixed_precision=args.mixed_precision							)

    if hasattr(args.checkpointing_steps			, 'isdigit'							):
        if args.checkpointing_steps == "epoch":
            SCREAMING_SNAKE_CASE_						=    args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            SCREAMING_SNAKE_CASE_						=    int(args.checkpointing_steps							)
        else:
            raise ValueError(
                f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""							)
    else:
        SCREAMING_SNAKE_CASE_						=    None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE_						=    config['lr']
    SCREAMING_SNAKE_CASE_						=    int(config['num_epochs']							)
    SCREAMING_SNAKE_CASE_						=    int(config['seed']							)
    SCREAMING_SNAKE_CASE_						=    int(config['batch_size']							)

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        SCREAMING_SNAKE_CASE_						=    os.path.split(_SCREAMING_SNAKE_CASE							)[-1].split('.'							)[0]
        accelerator.init_trackers(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    AutoTokenizer.from_pretrained('bert-base-cased'							)
    SCREAMING_SNAKE_CASE_						=    load_dataset('glue'			, 'mrpc'							)
    SCREAMING_SNAKE_CASE_						=    evaluate.load('glue'			, 'mrpc'							)

    def tokenize_function(_SCREAMING_SNAKE_CASE       :		Union[str, Any]							):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE_						=    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
    # starting with the main process first:
    with accelerator.main_process_first():
        SCREAMING_SNAKE_CASE_						=    datasets.map(
            _SCREAMING_SNAKE_CASE			, batched=_SCREAMING_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
    SCREAMING_SNAKE_CASE_						=    tokenized_datasets.rename_column('label'			, 'labels'							)

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

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

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

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

    set_seed(_SCREAMING_SNAKE_CASE							)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    SCREAMING_SNAKE_CASE_						=    AutoModelForSequenceClassification.from_pretrained('bert-base-cased'			, return_dict=_SCREAMING_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).
    SCREAMING_SNAKE_CASE_						=    model.to(accelerator.device							)

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE_						=    AdamW(params=model.parameters()			, lr=_SCREAMING_SNAKE_CASE							)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE_						=    get_linear_schedule_with_warmup(
        optimizer=_SCREAMING_SNAKE_CASE			, num_warmup_steps=100			, num_training_steps=(len(_SCREAMING_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.
    SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    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
    SCREAMING_SNAKE_CASE_						=    0
    # We also need to keep track of the stating epoch so files are named properly
    SCREAMING_SNAKE_CASE_						=    0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""							)
            accelerator.load_state(args.resume_from_checkpoint							)
            SCREAMING_SNAKE_CASE_						=    os.path.basename(args.resume_from_checkpoint							)
        else:
            # Get the most recent checkpoint
            SCREAMING_SNAKE_CASE_						=    [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
            dirs.sort(key=os.path.getctime							)
            SCREAMING_SNAKE_CASE_						=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        SCREAMING_SNAKE_CASE_						=    os.path.splitext(_SCREAMING_SNAKE_CASE							)[0]

        if "epoch" in training_difference:
            SCREAMING_SNAKE_CASE_						=    int(training_difference.replace('epoch_'			, ''							)							) + 1
            SCREAMING_SNAKE_CASE_						=    None
        else:
            SCREAMING_SNAKE_CASE_						=    int(training_difference.replace('step_'			, ''							)							)
            SCREAMING_SNAKE_CASE_						=    resume_step // len(_SCREAMING_SNAKE_CASE							)
            resume_step -= starting_epoch * len(_SCREAMING_SNAKE_CASE							)

    # Now we train the model
    for epoch in range(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        model.train()
        if args.with_tracking:
            SCREAMING_SNAKE_CASE_						=    0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            SCREAMING_SNAKE_CASE_						=    accelerator.skip_first_batches(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            SCREAMING_SNAKE_CASE_						=    train_dataloader
        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							)
            SCREAMING_SNAKE_CASE_						=    model(**_SCREAMING_SNAKE_CASE							)
            SCREAMING_SNAKE_CASE_						=    outputs.loss
            SCREAMING_SNAKE_CASE_						=    loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(_SCREAMING_SNAKE_CASE							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
                SCREAMING_SNAKE_CASE_						=    f"""step_{overall_step}"""
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        SCREAMING_SNAKE_CASE_						=    os.path.join(args.output_dir			, _SCREAMING_SNAKE_CASE							)
                    accelerator.save_state(_SCREAMING_SNAKE_CASE							)

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

        SCREAMING_SNAKE_CASE_						=    metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""			, _SCREAMING_SNAKE_CASE							)
        if args.with_tracking:
            accelerator.log(
                {
                    'accuracy': eval_metric['accuracy'],
                    'f1': eval_metric['f1'],
                    'train_loss': total_loss.item() / len(_SCREAMING_SNAKE_CASE							),
                    'epoch': epoch,
                }			, step=_SCREAMING_SNAKE_CASE			, )

        if checkpointing_steps == "epoch":
            SCREAMING_SNAKE_CASE_						=    f"""epoch_{epoch}"""
            if args.output_dir is not None:
                SCREAMING_SNAKE_CASE_						=    os.path.join(args.output_dir			, _SCREAMING_SNAKE_CASE							)
            accelerator.save_state(_SCREAMING_SNAKE_CASE							)

    if args.with_tracking:
        accelerator.end_training()


def        _UpperCAmelCase							(	):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    argparse.ArgumentParser(description='Simple example of training script.'							)
    parser.add_argument(
        '--mixed_precision'			, type=_SCREAMING_SNAKE_CASE			, default=_SCREAMING_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.'							)
    parser.add_argument(
        '--checkpointing_steps'			, type=_SCREAMING_SNAKE_CASE			, default=_SCREAMING_SNAKE_CASE			, help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			, )
    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(
        '--with_tracking'			, action='store_true'			, help='Whether to load in all available experiment trackers from the environment and use them for logging.'			, )
    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(
        '--project_dir'			, type=_SCREAMING_SNAKE_CASE			, default='logs'			, help='Location on where to store experiment tracking logs` and relevent project information'			, )
    SCREAMING_SNAKE_CASE_						=    parser.parse_args()
    SCREAMING_SNAKE_CASE_						=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       main()





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__	:   List[str]								=     get_tests_dir("fixtures/spiece.model")



@require_sentencepiece
@require_tokenizers
class    __snake_case	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						Union[str, Any]    			=	ErnieMTokenizer
  __lowerCAmelCase  :						Union[str, Any]    			=	False
  __lowerCAmelCase  :						Optional[int]    			=	True
  __lowerCAmelCase  :						List[str]    			=	False
  __lowerCAmelCase  :						List[str]    			=	False

  def     lowerCAmelCase__  (       self):
      super().setUp()

      # We have a SentencePiece fixture for testing
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')
      tokenizer.save_pretrained(self.tmpdirname)

  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    'this is a test'
      SCREAMING_SNAKE_CASE_						=    'this is a test'
      return input_text, output_text

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    '<pad>'
      SCREAMING_SNAKE_CASE_						=    0

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

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    list(self.get_tokenizer().get_vocab().keys())

      self.assertEqual(vocab_keys[0]    ,		'<pad>')
      self.assertEqual(vocab_keys[1]    ,		'<unk>')
      self.assertEqual(vocab_keys[-1]    ,		'▁eloquent')
      self.assertEqual(len(_A)    ,		30000)

  def     lowerCAmelCase__  (       self):
      self.assertEqual(self.get_tokenizer().vocab_size    ,		30000)

  def     lowerCAmelCase__  (       self):
      if not self.test_rust_tokenizer:
          return

      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()
      SCREAMING_SNAKE_CASE_						=    self.get_rust_tokenizer()

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

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize(_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.tokenize(_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode(_A    ,		add_special_tokens=_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.encode(_A    ,		add_special_tokens=_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    self.get_rust_tokenizer()
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode(_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.encode(_A)
      self.assertListEqual(_A    ,		_A)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		do_lower_case=_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('This is a test')
      self.assertListEqual(_A    ,		['▁this', '▁is', '▁a', '▁test'])

      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)    ,		[48, 25, 21, 1289])

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('I was born in 92000, and this is falsé.')
      # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
      self.assertListEqual(
          _A    ,		['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'])
      SCREAMING_SNAKE_CASE_						=    tokenizer.convert_tokens_to_ids(_A)
      self.assertListEqual(_A    ,		[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])

      SCREAMING_SNAKE_CASE_						=    tokenizer.convert_ids_to_tokens(_A)
      self.assertListEqual(
          _A    ,		['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']    ,		)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('sequence builders')
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('multi-sequence build')

      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A    ,		_A)

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
          tokenizer.sep_token_id
      ] + text_a + [tokenizer.sep_token_id]


  @slow
  def     lowerCAmelCase__  (       self):
      # fmt: off
      SCREAMING_SNAKE_CASE_						=    {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 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, 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], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1]]}
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_A    ,		model_name='susnato/ernie-m-base_pytorch'    ,		sequences=[
              'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
              'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
              'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
              'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
              'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
              'conditioning on both left and right context in all layers.',
              'The quick brown fox jumps over the lazy dog.',
          ]    ,		)


