






'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      SCREAMING_SNAKE_CASE							=						None


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							=						{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE							=						{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

SCREAMING_SNAKE_CASE							=						'▁'

# Segments (not really needed)
SCREAMING_SNAKE_CASE							=						0
SCREAMING_SNAKE_CASE							=						1
SCREAMING_SNAKE_CASE							=						2
SCREAMING_SNAKE_CASE							=						3
SCREAMING_SNAKE_CASE							=						4
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      VOCAB_FILES_NAMES
      UpperCamelCase_             =      PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_             =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_             =      '''left'''
      UpperCamelCase_             =      XLNetTokenizer






      def __init__(			self      :		int  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		str=False  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		int="<s>"  ,   UpperCAmelCase      :		Optional[int]="</s>"  ,   UpperCAmelCase      :		str="<unk>"  ,   UpperCAmelCase      :		Optional[Any]="<sep>"  ,   UpperCAmelCase      :		Optional[int]="<pad>"  ,   UpperCAmelCase      :		Optional[Any]="<cls>"  ,   UpperCAmelCase      :		Dict="<mask>"  ,   UpperCAmelCase      :		int=["<eop>", "<eod>"]  ,   **UpperCAmelCase      :		List[Any]  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=AddedToken(UpperCAmelCase  ,   lstrip=UpperCAmelCase  ,   rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) else mask_token

       super().__init__(
           vocab_file=UpperCAmelCase  ,   tokenizer_file=UpperCAmelCase  ,   do_lower_case=UpperCAmelCase  ,   remove_space=UpperCAmelCase  ,   keep_accents=UpperCAmelCase  ,   bos_token=UpperCAmelCase  ,   eos_token=UpperCAmelCase  ,   unk_token=UpperCAmelCase  ,   sep_token=UpperCAmelCase  ,   pad_token=UpperCAmelCase  ,   cls_token=UpperCAmelCase  ,   mask_token=UpperCAmelCase  ,   additional_special_tokens=UpperCAmelCase  ,   **UpperCAmelCase  ,   )

       lowercase       : Tuple						=3
       lowercase       : Union[str, Any]						=do_lower_case
       lowercase       : Any						=remove_space
       lowercase       : int						=keep_accents
       lowercase       : int						=vocab_file
       lowercase       : Union[str, Any]						=False if not self.vocab_file else True






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=[self.sep_token_id]
       lowercase       : Optional[Any]						=[self.cls_token_id]
       if token_ids_a is None:
        return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		List[int]  ,   UpperCAmelCase      :		Optional[List[int]] = None		)	->	List[int]:


       '''simple docstring'''

       lowercase       : Optional[int]						=[self.sep_token_id]
       lowercase       : Union[str, Any]						=[2]

       if token_ids_a is None:
        return len(token_ids_a + sep		) * [0] + cls_segment_id
       return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		Optional[str] = None		)	->	Tuple[str]:


       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''		)

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase       : Dict						=os.path.join(
           UpperCAmelCase  ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase		):
        copyfile(self.vocab_file  ,   UpperCAmelCase		)

       return (out_vocab_file,)










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
#
########################################################################


a__      :     Any         =		16
a__      :     str         =		32

def      UpperCAmelCase_    (    _UpperCAmelCase							:Accelerator  ,						_UpperCAmelCase							:int = 16				)	->				int:
     '''simple docstring'''


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

     def tokenize_function(_UpperCAmelCase							:Optional[Any]				):
          # max_length=None => use the model max length (it's actually the default)
          A_          =       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():
          A_          =       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
     A_          =       tokenized_datasets.rename_column('''label'''  ,						'''labels'''				)

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

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

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

      a__      :     Optional[Any]         =		mocked_dataloaders  # noqa: F811

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:Dict				)	->				Dict:
     '''simple docstring'''


     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,						_UpperCAmelCase				) == "1":
          A_          =       2
     # New Code #
     A_          =       int(args.gradient_accumulation_steps				)
     A_          =       int(args.local_sgd_steps				)
     # Initialize accelerator
     A_          =       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
     A_          =       config['''lr''']
     A_          =       int(config['''num_epochs''']				)
     A_          =       int(config['''seed''']				)
     A_          =       int(config['''batch_size''']				)

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

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

     # Instantiate optimizer
     A_          =       AdamW(params=model.parameters()  ,						lr=_UpperCAmelCase				)

     # Instantiate scheduler
     A_          =       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.
     A_   ,    A_   ,    A_   ,    A_   ,    A_          =       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				):
                         A_          =       model(**_UpperCAmelCase				)
                         A_          =       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():
                    A_          =       model(**_UpperCAmelCase				)
               A_          =       outputs.logits.argmax(dim=-1				)
               A_   ,    A_          =       accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
               metric.add_batch(
                   predictions=_UpperCAmelCase  ,						references=_UpperCAmelCase  ,						)

          A_          =       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       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.'''				)
     A_          =       parser.parse_args()
     A_          =       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_UpperCAmelCase  ,						_UpperCAmelCase				)


if __name__ == "__main__":
      main()



