




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

__magic_name__  :  Union[str, Any]					=       16
__magic_name__  :  Any					=       32
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = 16				):
  lowerCAmelCase__										=      AutoTokenizer.from_pretrained('''bert-base-cased'''				)
  lowerCAmelCase__										=      load_dataset('''glue'''   , '''mrpc'''				)

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

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

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

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

  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

      __magic_name__  :  Any					=       mocked_dataloaders  # noqa: F811
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   , __lowerCAmelCase				) == "1":
    lowerCAmelCase__										=      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__										=      Accelerator(
        cpu=args.cpu   , mixed_precision=args.mixed_precision   , log_with='''all'''   , project_dir=args.project_dir				)
  else:
    lowerCAmelCase__										=      Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision				)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase__										=      config['''lr''']
  lowerCAmelCase__										=      int(config['''num_epochs''']				)
  lowerCAmelCase__										=      int(config['''seed''']				)
  lowerCAmelCase__										=      int(config['''batch_size''']				)
  set_seed(__lowerCAmelCase				)

  lowerCAmelCase__				,      lowerCAmelCase__										=      get_dataloaders(__lowerCAmelCase   , __lowerCAmelCase				)
  lowerCAmelCase__										=      evaluate.load('''glue'''   , '''mrpc'''				)

  # If the batch size is too big we use gradient accumulation
  lowerCAmelCase__										=      1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowerCAmelCase__										=      batch_size // MAX_GPU_BATCH_SIZE
    lowerCAmelCase__										=      MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCAmelCase__										=      AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   , return_dict=__lowerCAmelCase				)

  # 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__										=      model.to(accelerator.device				)

  # Instantiate optimizer
  lowerCAmelCase__										=      AdamW(params=model.parameters()   , lr=__lowerCAmelCase				)

  # Instantiate scheduler
  lowerCAmelCase__										=      get_linear_schedule_with_warmup(
      optimizer=__lowerCAmelCase   , num_warmup_steps=1_00   , num_training_steps=(len(__lowerCAmelCase				) * 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__										=      accelerator.prepare(
      __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)

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

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

    model.eval()
    for step, batch in enumerate(__lowerCAmelCase				):
      # 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__										=      model(**__lowerCAmelCase				)
      lowerCAmelCase__										=      outputs.logits.argmax(dim=-1				)
      lowerCAmelCase__				,      lowerCAmelCase__										=      accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
      metric.add_batch(
          predictions=__lowerCAmelCase   , references=__lowerCAmelCase   , )

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

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

    # 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 		a_							( ):
  lowerCAmelCase__										=      argparse.ArgumentParser(description='''Simple example of training script.'''				)
  parser.add_argument(
      '''--mixed_precision'''   , type=__lowerCAmelCase   , default=__lowerCAmelCase   , 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=__lowerCAmelCase   , default='''logs'''   , help='''Location on where to store experiment tracking logs` and relevent project information'''   , )
  lowerCAmelCase__										=      parser.parse_args()
  lowerCAmelCase__										=      {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(__lowerCAmelCase   , __lowerCAmelCase				)


if __name__ == "__main__":
      main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  Union[str, Any]					=       {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  str					=       [
          """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AltCLIPPreTrainedModel""",
          """AltCLIPModel""",
          """AltCLIPTextModel""",
          """AltCLIPVisionModel""",
      ]


if TYPE_CHECKING:
      from .configuration_altclip import (
          ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AltCLIPConfig,
          AltCLIPTextConfig,
          AltCLIPVisionConfig,
      )
      from .processing_altclip import AltCLIPProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_altclip import (
                ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                AltCLIPModel,
                AltCLIPPreTrainedModel,
                AltCLIPTextModel,
                AltCLIPVisionModel,
            )


else:
      import sys

      __magic_name__  :  str					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

