

import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


a_     :    Dict     		=   16
a_     :    int     		=   32


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Accelerator		,				_UpperCamelCase		:	DatasetDict		,				_UpperCamelCase		:	List[int]		,				_UpperCamelCase		:	List[int]		,				_UpperCamelCase		:	int = 16						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      AutoTokenizer.from_pretrained('bert-base-cased'						)
 SCREAMING_SNAKE_CASE		    =      DatasetDict(
     {
         'train': dataset['train'].select(_UpperCamelCase						),
         'validation': dataset['train'].select(_UpperCamelCase						),
         'test': dataset['validation'],
     }						)

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

 def collate_fn(_UpperCamelCase		:	Dict						):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE		    =      1_28 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(
      _UpperCamelCase		,				padding='longest'		,				max_length=_UpperCamelCase		,				pad_to_multiple_of=_UpperCamelCase		,				return_tensors='pt'		,				)

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

 SCREAMING_SNAKE_CASE		    =      DataLoader(
     tokenized_datasets['test']		,				shuffle=_UpperCamelCase		,				collate_fn=_UpperCamelCase		,				batch_size=_UpperCamelCase						)

 return train_dataloader, eval_dataloader, test_dataloader


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[Any]						)		->				List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 # Download the dataset
 SCREAMING_SNAKE_CASE		    =      load_dataset('glue'		,				'mrpc'						)
 # Create our splits
 SCREAMING_SNAKE_CASE		    =      StratifiedKFold(n_splits=int(args.num_folds						)						)
 # Initialize accelerator
 SCREAMING_SNAKE_CASE		    =      Accelerator(cpu=args.cpu		,				mixed_precision=args.mixed_precision						)
 # 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']						)

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

 # 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

 set_seed(_UpperCamelCase						)

 # New Code #
 # Create our folds:
 SCREAMING_SNAKE_CASE		    =      kfold.split(np.zeros(datasets['train'].num_rows						)		,				datasets['train']['label']						)
 SCREAMING_SNAKE_CASE		    =      []
 # Iterate over them
 for i, (train_idxs, valid_idxs) in enumerate(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      get_fold_dataloaders(
      _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				)
  # 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=_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).
  SCREAMING_SNAKE_CASE		    =      model.to(accelerator.device						)

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

  # Instantiate scheduler
  SCREAMING_SNAKE_CASE		    =      get_linear_schedule_with_warmup(
      optimizer=_UpperCamelCase		,				num_warmup_steps=1_00		,				num_training_steps=(len(_UpperCamelCase						) * 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(
      _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						)
    SCREAMING_SNAKE_CASE		    =      model(**_UpperCamelCase						)
    SCREAMING_SNAKE_CASE		    =      outputs.loss
    SCREAMING_SNAKE_CASE		    =      loss / gradient_accumulation_steps
    accelerator.backward(_UpperCamelCase						)
    if step % gradient_accumulation_steps == 0:
     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():
     SCREAMING_SNAKE_CASE		    =      model(**_UpperCamelCase						)
    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=_UpperCamelCase		,				references=_UpperCamelCase		,				)

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

  # New Code #
  # We also run predictions on the test set at the very end
  SCREAMING_SNAKE_CASE		    =      []
  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():
    SCREAMING_SNAKE_CASE		    =      model(**_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      outputs.logits
   SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      accelerator.gather_for_metrics((predictions, batch['labels'])						)
   fold_predictions.append(predictions.cpu()						)
   if i == 0:
    # We need all of the test predictions
    test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
  test_predictions.append(torch.cat(_UpperCamelCase		,				dim=0						)						)
  # We now need to release all our memory and get rid of the current model, optimizer, etc
  accelerator.free_memory()
 # New Code #
 # Finally we check the accuracy of our folded results:
 SCREAMING_SNAKE_CASE		    =      torch.cat(_UpperCamelCase		,				dim=0						)
 SCREAMING_SNAKE_CASE		    =      torch.stack(_UpperCamelCase		,				dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
 SCREAMING_SNAKE_CASE		    =      metric.compute(predictions=_UpperCamelCase		,				references=_UpperCamelCase						)
 accelerator.print('Average test metrics from all folds:'		,				_UpperCamelCase						)


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


 SCREAMING_SNAKE_CASE		    =      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.'		,				)
 parser.add_argument('--cpu'		,				action='store_true'		,				help='If passed, will train on the CPU.'						)
 # New Code #
 parser.add_argument('--num_folds'		,				type=_UpperCamelCase		,				default=3		,				help='The number of splits to perform across the dataset'						)
 SCREAMING_SNAKE_CASE		    =      parser.parse_args()
 SCREAMING_SNAKE_CASE		    =      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(_UpperCamelCase		,				_UpperCamelCase						)


if __name__ == "__main__":
      main()




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_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(snake_case__				,     lstrip=snake_case__				,     rstrip=snake_case__     ) if isinstance(snake_case__				,     snake_case__     ) else mask_token

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

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]
     if token_ids_a is None:
      return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'     )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [self.cls_token_id]

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




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

     return (out_vocab_file,)

