'''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
# and perform 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
#
########################################################################


lowercase__  :      Optional[int]        =					16
lowercase__  :      Optional[int]        =					32



def  __lowerCamelCase       (      _UpperCamelCase  :  Accelerator							,    _UpperCamelCase  :  int = 16					):


      '''simple docstring'''


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

      def tokenize_function(_UpperCamelCase  :  Tuple					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase_            =  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_            =  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_            =  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.
            UpperCAmelCase_            =  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":
                  UpperCAmelCase_            =  16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase_            =  8
            else:
                  UpperCAmelCase_            =  None

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

      # Instantiate dataloaders.
      UpperCAmelCase_            =  DataLoader(
          tokenized_datasets['''train''']							,    shuffle=_UpperCamelCase							,    collate_fn=_UpperCamelCase							,    batch_size=_UpperCamelCase					)
      UpperCAmelCase_            =  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

      lowercase__  :      int        =					mocked_dataloaders  # noqa: F811



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,    _UpperCamelCase					) == "1":
            UpperCAmelCase_            =  2
      # New Code #
      UpperCAmelCase_            =  int(args.gradient_accumulation_steps					)
      # Initialize accelerator
      UpperCAmelCase_            =  Accelerator(
          cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    gradient_accumulation_steps=_UpperCamelCase					)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''					)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase_            =  config['''lr''']
      UpperCAmelCase_            =  int(config['''num_epochs''']					)
      UpperCAmelCase_            =  int(config['''seed''']					)
      UpperCAmelCase_            =  int(config['''batch_size''']					)

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

      set_seed(_UpperCamelCase					)
      UpperCAmelCase_      ,		UpperCAmelCase_            =  get_dataloaders(_UpperCamelCase							,    _UpperCamelCase					)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCAmelCase_            =  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_            =  model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCAmelCase_            =  AdamW(params=model.parameters()							,    lr=_UpperCamelCase					)

      # Instantiate scheduler
      UpperCAmelCase_            =  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.
      UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  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					)
                  # 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					):
                        UpperCAmelCase_            =  model(**_UpperCamelCase					)
                        UpperCAmelCase_            =  output.loss
                        accelerator.backward(_UpperCamelCase					)
                        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_            =  model(**_UpperCamelCase					)
                  UpperCAmelCase_            =  outputs.logits.argmax(dim=-1					)
                  UpperCAmelCase_      ,		UpperCAmelCase_            =  accelerator.gather_for_metrics((predictions, batch['''labels'''])					)
                  metric.add_batch(
                      predictions=_UpperCamelCase							,    references=_UpperCamelCase							,    )

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



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  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('''--cpu'''							,    action='''store_true'''							,    help='''If passed, will train on the CPU.'''					)
      UpperCAmelCase_            =  parser.parse_args()
      UpperCAmelCase_            =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(_UpperCamelCase							,    _UpperCamelCase					)


if __name__ == "__main__":
      main()






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['''token_type_ids''']  )  ,							sum(tokens_p['''token_type_ids''']  )  )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )  ,							sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )





