


"""simple docstring"""







import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class       __A				(       __a			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A						)					->     str:
     a 		=params

     a 		=np.array(a_						)
     a 		=np.array([len(a_						) for t in data]						)

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()


 def __getitem__(		self ,    __A						)					->     Dict:
     return (self.token_ids[index], self.lengths[index])


 def __len__(		self						)					->     Any:
     return len(self.lengths						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     assert len(self.token_ids						) == len(self.lengths						)
     assert all(self.lengths[i] == len(self.token_ids[i]						) for i in range(len(self.lengths						)						)						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		=self.params.max_model_input_size
     a 		=self.lengths > max_len
     logger.info(f'''Splitting {sum(a_						)} too long sequences.'''						)

     def divide_chunks(__A ,    __A						):
         return [l[i : i + n] for i in range(0 ,    len(a_						) ,    a_						)]

     a 		=[]
     a 		=[]
     if self.params.mlm:
         a 		=self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
     else:
         a 		=self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

     for seq_, len_ in zip(self.token_ids ,    self.lengths						):
         assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
         if len_ <= max_len:
             new_tok_ids.append(seq_						)
             new_lengths.append(len_						)
         else:
             a 		=[]
             for sub_s in divide_chunks(seq_ ,    max_len - 2						):
                 if sub_s[0] != cls_id:
                     a 		=np.insert(a_ ,    0 ,    a_						)
                 if sub_s[-1] != sep_id:
                     a 		=np.insert(a_ ,    len(a_						) ,    a_						)
                 assert len(a_						) <= max_len
                 assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                 sub_seqs.append(a_						)

             new_tok_ids.extend(a_						)
             new_lengths.extend([len(a_						) for l in sub_seqs]						)

     a 		=np.array(a_						)
     a 		=np.array(a_						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=len(self						)
     a 		=self.lengths > 11
     a 		=self.token_ids[indices]
     a 		=self.lengths[indices]
     a 		=len(self						)
     logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:



     if "unk_token" not in self.params.special_tok_ids:
         return
     else:
         a 		=self.params.special_tok_ids["""unk_token"""]
     a 		=len(self						)
     a 		=np.array([np.count_nonzero(a == unk_token_id						) for a in self.token_ids]						)
     a 		=(unk_occs / self.lengths) < 0.5
     a 		=self.token_ids[indices]
     a 		=self.lengths[indices]
     a 		=len(self						)
     logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:



     if not self.params.is_master:
         return
     logger.info(f'''{len(self						)} sequences'''						)
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     Optional[Any]:
     a 		=[t[0] for t in batch]
     a 		=[t[1] for t in batch]
     assert len(a_						) == len(a_						)

     # Max for paddings
     a 		=max(a_						)

     # Pad token ids
     if self.params.mlm:
         a 		=self.params.special_tok_ids["""pad_token"""]
     else:
         a 		=self.params.special_tok_ids["""unk_token"""]
     a 		=[list(t.astype(a_						)						) + [pad_idx] * (max_seq_len_ - len(a_						)) for t in token_ids]
     assert len(tk_						) == len(a_						)
     assert all(len(a_						) == max_seq_len_ for t in tk_						)

     a 		=torch.tensor(tk_						)  # (bs, max_seq_len_)
     a 		=torch.tensor(a_						)  # (bs)
     return tk_t, lg_t





"""simple docstring"""


import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_						=				16
A_						=				32


def 					UpperCAmelCase__       (snake_case__							:					Accelerator		,   snake_case__							:					int = 16    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  AutoTokenizer.from_pretrained("""bert-base-cased"""    )
  _snake_case							:		Any								=  load_dataset("""glue"""		,   """mrpc"""    )

  def tokenize_function(snake_case__							:					Any    ):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case							:		Any								=  tokenizer(examples["""sentence1"""]		,   examples["""sentence2"""]		,   truncation=snake_case__		,   max_length=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():
    _snake_case							:		List[Any]								=  datasets.map(
        snake_case__		,   batched=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
  _snake_case							:		int								=  tokenized_datasets.rename_column("""label"""		,   """labels"""    )

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

    return tokenizer.pad(
        snake_case__		,   padding="""longest"""		,   max_length=snake_case__		,   pad_to_multiple_of=snake_case__		,   return_tensors="""pt"""		,   )

  # Instantiate dataloaders.
  _snake_case							:		Optional[int]								=  DataLoader(
      tokenized_datasets["""train"""]		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__    )
  _snake_case							:		Dict								=  DataLoader(
      tokenized_datasets["""validation"""]		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__    )

  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_						=				mocked_dataloaders  # noqa: F811


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any    ):
  """simple docstring"""

  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,   snake_case__    ) == "1":
    _snake_case							:		List[Any]								=  2
  # Initialize accelerator
  _snake_case							:		str								=  Accelerator(cpu=args.cpu		,   mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _snake_case							:		Tuple								=  config["""lr"""]
  _snake_case							:		str								=  int(config["""num_epochs"""]    )
  _snake_case							:		Union[str, Any]								=  int(config["""seed"""]    )
  _snake_case							:		Union[str, Any]								=  int(config["""batch_size"""]    )

  _snake_case							:		List[str]								=  evaluate.load("""glue"""		,   """mrpc"""    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=snake_case__    )
  def inner_training_loop(snake_case__							:					Union[str, Any]    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(snake_case__    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _snake_case							:		List[Any]								=  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,   return_dict=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).
    _snake_case							:		Tuple								=  model.to(accelerator.device    )

    # Instantiate optimizer
    _snake_case							:		str								=  AdamW(params=model.parameters()		,   lr=snake_case__    )
    _snake_case		,					_snake_case							:		Optional[int]								=  get_dataloaders(snake_case__		,   snake_case__    )

    # Instantiate scheduler
    _snake_case							:		str								=  get_linear_schedule_with_warmup(
        optimizer=snake_case__		,   num_warmup_steps=1_00		,   num_training_steps=(len(snake_case__    ) * 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.
    _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  accelerator.prepare(
        snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )

    # Now we train the model
    for epoch in range(snake_case__    ):
      model.train()
      for step, batch in enumerate(snake_case__    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        _snake_case							:		int								=  model(**snake_case__    )
        _snake_case							:		str								=  outputs.loss
        accelerator.backward(snake_case__    )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(snake_case__    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        with torch.no_grad():
          _snake_case							:		int								=  model(**snake_case__    )
        _snake_case							:		Optional[Any]								=  outputs.logits.argmax(dim=-1    )
        _snake_case		,					_snake_case							:		Tuple								=  accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
        metric.add_batch(
            predictions=snake_case__		,   references=snake_case__		,   )

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  argparse.ArgumentParser(description="""Simple example of training script."""    )
  parser.add_argument(
      """--mixed_precision"""		,   type=snake_case__		,   default=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."""    )
  _snake_case							:		Dict								=  parser.parse_args()
  _snake_case							:		int								=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(snake_case__		,   snake_case__    )


if __name__ == "__main__":
     main()


