


'''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,
# specifically showcasing the experiment tracking capability,
# 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					:  Optional[int]											=      16
__A					:  List[str]											=      32







def 				UpperCamelCase_			(    A__					:   Accelerator							,    A__					:   int = 16					):
  '''simple docstring'''





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

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

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

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

  # Instantiate dataloaders.
  lowerCAmelCase_			:      Optional[Any]			=   DataLoader(
      tokenized_datasets["""train"""]							,    shuffle=A__							,    collate_fn=A__							,    batch_size=A__					)
  lowerCAmelCase_			:      Any			=   DataLoader(
      tokenized_datasets["""validation"""]							,    shuffle=A__							,    collate_fn=A__							,    batch_size=A__					)

  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					:  Dict											=      mocked_dataloaders  # noqa: F811







def 				UpperCamelCase_			(    A__					:   Optional[Any]							,    A__					:   Optional[int]					):
  '''simple docstring'''





  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,    A__					) == "1":
    lowerCAmelCase_			:      Any			=   2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    lowerCAmelCase_			:      Any			=   Accelerator(
        cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    log_with="""all"""							,    project_dir=args.project_dir					)
  else:
    lowerCAmelCase_			:      Dict			=   Accelerator(cpu=args.cpu							,    mixed_precision=args.mixed_precision					)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase_			:      str			=   config["""lr"""]
  lowerCAmelCase_			:      int			=   int(config["""num_epochs"""]					)
  lowerCAmelCase_			:      str			=   int(config["""seed"""]					)
  lowerCAmelCase_			:      str			=   int(config["""batch_size"""]					)
  set_seed(A__					)

  lowerCAmelCase_,							lowerCAmelCase_			:      str			=   get_dataloaders(A__							,    A__					)
  lowerCAmelCase_			:      List[str]			=   evaluate.load("""glue"""							,    """mrpc"""					)

  # If the batch size is too big we use gradient accumulation
  lowerCAmelCase_			:      Any			=   1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowerCAmelCase_			:      Dict			=   batch_size // MAX_GPU_BATCH_SIZE
    lowerCAmelCase_			:      Union[str, Any]			=   MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase_			:      Union[str, Any]			=   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,    return_dict=A__					)

  # 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).
  lowerCAmelCase_			:      Optional[int]			=   model.to(accelerator.device					)

  # Instantiate optimizer
  lowerCAmelCase_			:      Any			=   AdamW(params=model.parameters()							,    lr=A__					)

  # Instantiate scheduler
  lowerCAmelCase_			:      int			=   get_linear_schedule_with_warmup(
      optimizer=A__							,    num_warmup_steps=1_00							,    num_training_steps=(len(A__					) * 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.
  lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   accelerator.prepare(
      A__							,    A__							,    A__							,    A__							,    A__					)

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    lowerCAmelCase_			:      List[str]			=   os.path.split(A__					)[-1].split("""."""					)[0]
    accelerator.init_trackers(A__							,    A__					)

  # Now we train the model
  for epoch in range(A__					):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      lowerCAmelCase_			:      Any			=   0
    for step, batch in enumerate(A__					):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device					)
      lowerCAmelCase_			:      List[str]			=   model(**A__					)
      lowerCAmelCase_			:      Tuple			=   outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      lowerCAmelCase_			:      Any			=   loss / gradient_accumulation_steps
      accelerator.backward(A__					)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

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

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": eval_metric["""accuracy"""],
              """f1""": eval_metric["""f1"""],
              """train_loss""": total_loss.item() / len(A__					),
              """epoch""": epoch,
          }							,    step=A__							,    )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





  lowerCAmelCase_			:      Any			=   argparse.ArgumentParser(description="""Simple example of training script."""					)
  parser.add_argument(
      """--mixed_precision"""							,    type=A__							,    default=A__							,    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(
      """--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(
      """--project_dir"""							,    type=A__							,    default="""logs"""							,    help="""Location on where to store experiment tracking logs` and relevent project information"""							,    )
  lowerCAmelCase_			:      Optional[Any]			=   parser.parse_args()
  lowerCAmelCase_			:      Dict			=   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A__							,    A__					)


if __name__ == "__main__":
  main()







'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					['image_processor', 'tokenizer']
    lowercase    =					'AutoImageProcessor'
    lowercase    =					'AutoTokenizer'

    def __init__(   self    :  int    ,						lowerCamelCase    :  List[str]=None    ,						lowerCamelCase    :  Union[str, Any]=None    ,						**lowerCamelCase    :  str		)   ->  Tuple:
      lowerCAmelCase_			:      str			=   None
      if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""    ,						lowerCamelCase    ,						)
        lowerCAmelCase_			:      Tuple			=   kwargs.pop("""feature_extractor"""		)

      lowerCAmelCase_			:      Tuple			=   image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("""You need to specify an `image_processor`."""		)
      if tokenizer is None:
        raise ValueError("""You need to specify a `tokenizer`."""		)

      super().__init__(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   self.image_processor
      lowerCAmelCase_			:      Any			=   False

    def __call__(   self    :  List[Any]    ,						*lowerCamelCase    :  str    ,						**lowerCamelCase    :  Tuple		)   ->  Union[str, Any]:
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*lowerCamelCase    ,						**lowerCamelCase		)

      lowerCAmelCase_			:      Any			=   kwargs.pop("""images"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""text"""    ,						lowerCamelCase		)
      if len(lowerCamelCase		) > 0:
        lowerCAmelCase_			:      str			=   args[0]
        lowerCAmelCase_			:      Dict			=   args[1:]

      if images is None and text is None:
        raise ValueError("""You need to specify either an `images` or `text` input to process."""		)

      if images is not None:
        lowerCAmelCase_			:      Any			=   self.image_processor(lowerCamelCase    ,						*lowerCamelCase    ,						**lowerCamelCase		)
      if text is not None:
        lowerCAmelCase_			:      str			=   self.tokenizer(lowerCamelCase    ,						**lowerCamelCase		)

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowerCAmelCase_			:      Dict			=   encodings["""input_ids"""]
        return inputs

    def __lowercase   (   self    :  str    ,						*lowerCamelCase    :  Union[str, Any]    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.batch_decode(*lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  List[Any]    ,						*lowerCamelCase    :  Any    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.decode(*lowerCamelCase    ,						**lowerCamelCase		)

    @contextmanager
    def __lowercase   (   self    :  str		)   ->  Union[str, Any]:
      warnings.warn(
          """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
          """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
          """your images inputs, or in a separate call."""		)
      lowerCAmelCase_			:      List[str]			=   True
      lowerCAmelCase_			:      Optional[Any]			=   self.tokenizer
      yield
      lowerCAmelCase_			:      List[Any]			=   self.image_processor
      lowerCAmelCase_			:      List[str]			=   False

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  str=False    ,						lowerCamelCase    :  List[Any]=None		)   ->  Optional[int]:
      if added_vocab is None:
        lowerCAmelCase_			:      str			=   self.tokenizer.get_added_vocab()

      lowerCAmelCase_			:      Union[str, Any]			=   {}

      while tokens:
        lowerCAmelCase_			:      Dict			=   re.search(R"""<s_(.*?)>"""    ,						lowerCamelCase    ,						re.IGNORECASE		)
        if start_token is None:
          break
        lowerCAmelCase_			:      Tuple			=   start_token.group(1		)
        lowerCAmelCase_			:      Tuple			=   re.search(RF'</s_{key}>'    ,						lowerCamelCase    ,						re.IGNORECASE		)
        lowerCAmelCase_			:      Tuple			=   start_token.group()
        if end_token is None:
          lowerCAmelCase_			:      str			=   tokens.replace(lowerCamelCase    ,						""""""		)
        else:
          lowerCAmelCase_			:      List[str]			=   end_token.group()
          lowerCAmelCase_			:      Dict			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      int			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      Dict			=   re.search(F'{start_token_escaped}(.*?){end_token_escaped}'    ,						lowerCamelCase    ,						re.IGNORECASE		)
          if content is not None:
            lowerCAmelCase_			:      str			=   content.group(1		).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowerCAmelCase_			:      str			=   self.tokenajson(lowerCamelCase    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)
              if value:
                if len(lowerCamelCase		) == 1:
                  lowerCAmelCase_			:      List[Any]			=   value[0]
                lowerCAmelCase_			:      Optional[Any]			=   value
            else:  # leaf nodes
              lowerCAmelCase_			:      List[str]			=   []
              for leaf in content.split(R"""<sep/>"""		):
                lowerCAmelCase_			:      Union[str, Any]			=   leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowerCAmelCase_			:      Any			=   leaf[1:-2]  # for categorical special tokens
                output[key].append(lowerCamelCase		)
              if len(output[key]		) == 1:
                lowerCAmelCase_			:      Optional[Any]			=   output[key][0]

          lowerCAmelCase_			:      List[Any]			=   tokens[tokens.find(lowerCamelCase		) + len(lowerCamelCase		) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)

      if len(lowerCamelCase		):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}

    @property
    def __lowercase   (   self    :  Dict		)   ->  int:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor_class





    @property
    def __lowercase   (   self    :  Dict		)   ->  Optional[Any]:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor



