




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

lowerCAmelCase_					    = 16
lowerCAmelCase_					    = 32



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = 16      )     ->	Optional[Any]:
  lowerCAmelCase__				:  Tuple		       =     AutoTokenizer.from_pretrained('''bert-base-cased'''      )
  lowerCAmelCase__				:  Dict		       =     load_dataset('''glue'''	,      '''mrpc'''      )

  def tokenize_function(UpperCamelCase      ):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase__				:  List[str]		       =     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():
    lowerCAmelCase__				:  Any		       =     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
  lowerCAmelCase__				:  int		       =     tokenized_datasets.rename_column('''label'''	,      '''labels'''      )

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

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

  # Instantiate dataloaders.
  lowerCAmelCase__				:  int		       =     DataLoader(
      tokenized_datasets['''train''']	,      shuffle=UpperCamelCase	,      collate_fn=UpperCamelCase	,      batch_size=UpperCamelCase      )
  lowerCAmelCase__				:  Tuple		       =     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

     lowerCAmelCase_					    = mocked_dataloaders  # noqa: F811



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Any:
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,      UpperCamelCase      ) == "1":
    lowerCAmelCase__				:  Union[str, 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__				:  Optional[int]		       =     Accelerator(
        cpu=args.cpu	,      mixed_precision=args.mixed_precision	,      log_with='''all'''	,      project_dir=args.project_dir      )
  else:
    lowerCAmelCase__				:  Tuple		       =     Accelerator(cpu=args.cpu	,      mixed_precision=args.mixed_precision      )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase__				:  Optional[Any]		       =     config['''lr''']
  lowerCAmelCase__				:  List[Any]		       =     int(config['''num_epochs''']      )
  lowerCAmelCase__				:  Optional[Any]		       =     int(config['''seed''']      )
  lowerCAmelCase__				:  Dict		       =     int(config['''batch_size''']      )
  set_seed(UpperCamelCase      )

  lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[Any]		       =     get_dataloaders(UpperCamelCase	,      UpperCamelCase      )
  lowerCAmelCase__				:  Optional[int]		       =     evaluate.load('''glue'''	,      '''mrpc'''      )

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

  # Instantiate optimizer
  lowerCAmelCase__				:  Optional[int]		       =     AdamW(params=model.parameters()	,      lr=UpperCamelCase      )

  # Instantiate scheduler
  lowerCAmelCase__				:  List[Any]		       =     get_linear_schedule_with_warmup(
      optimizer=UpperCamelCase	,      num_warmup_steps=100	,      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.
  lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     accelerator.prepare(
      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )

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

  # Now we train the model
  for epoch in range(UpperCamelCase      ):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      lowerCAmelCase__				:  str		       =     0
    for step, batch in enumerate(UpperCamelCase      ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device      )
      lowerCAmelCase__				:  List[str]		       =     model(**UpperCamelCase      )
      lowerCAmelCase__				:  List[Any]		       =     outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      lowerCAmelCase__				:  List[str]		       =     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` (the default).
      batch.to(accelerator.device      )
      with torch.no_grad():
        lowerCAmelCase__				:  Union[str, Any]		       =     model(**UpperCamelCase      )
      lowerCAmelCase__				:  Optional[Any]		       =     outputs.logits.argmax(dim=-1      )
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Any		       =     accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
      metric.add_batch(
          predictions=UpperCamelCase	,      references=UpperCamelCase	,      )

    lowerCAmelCase__				:  List[str]		       =     metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}:"""	,      UpperCamelCase      )

    # 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(UpperCamelCase      ),
              '''epoch''': epoch,
          }	,      step=UpperCamelCase	,      )

    # 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      __lowerCAmelCase			(					)     ->	Any:
  lowerCAmelCase__				:  List[Any]		       =     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.'''      )
  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=UpperCamelCase	,      default='''logs'''	,      help='''Location on where to store experiment tracking logs` and relevent project information'''	,      )
  lowerCAmelCase__				:  int		       =     parser.parse_args()
  lowerCAmelCase__				:  Dict		       =     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(UpperCamelCase	,      UpperCamelCase      )


if __name__ == "__main__":
     main()







import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)

