




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


_lowerCamelCase			:	Optional[Any]							 =     16
_lowerCamelCase			:	Tuple							 =     32







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_ = 16		)     -> List[str]:



       """simple docstring"""

       A__   				=					AutoTokenizer.from_pretrained('''bert-base-cased'''		)
       A__   				=					load_dataset('''glue'''				,       '''mrpc'''		)

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

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

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

       # Instantiate dataloaders.
       A__   				=					DataLoader(
           tokenized_datasets['''train''']				,       shuffle=__A				,       collate_fn=__A				,       batch_size=__A		)
       A__   				=					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

  _lowerCamelCase			:	Any							 =     mocked_dataloaders  # noqa: F811







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

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

       A__   				=					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=__A		)
       def inner_training_loop(lowercase_		):
              # 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(__A		)

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              A__   				=					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).
              A__   				=					model.to(accelerator.device		)

              # Instantiate optimizer
              A__   				=					AdamW(params=model.parameters()				,       lr=__A		)
              A__   				=					get_dataloaders(__A				,       __A		)

              # Instantiate scheduler
              A__   				=					get_linear_schedule_with_warmup(
                  optimizer=__A				,       num_warmup_steps=100				,       num_training_steps=(len(__A		) * 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.
              A__   				=					accelerator.prepare(
                  __A				,       __A				,       __A				,       __A				,       __A		)

              # Now we train the model
              for epoch in range(__A		):
                     model.train()
                     for step, batch in enumerate(__A		):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device		)
                            A__   				=					model(**__A		)
                            A__   				=					outputs.loss
                            accelerator.backward(__A		)
                            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`.
                            batch.to(accelerator.device		)
                            with torch.no_grad():
                                   A__   				=					model(**__A		)
                            A__   				=					outputs.logits.argmax(dim=-1		)
                            A__   				=					accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
                            metric.add_batch(
                                predictions=__A				,       references=__A				,       )

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

                      # 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 SCREAMING_SNAKE_CASE       ( )     -> List[str]:



       """simple docstring"""

       A__   				=					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.'''		)
       A__   				=					parser.parse_args()
       A__   				=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(__A				,       __A		)


if __name__ == "__main__":
  main()







from typing import TYPE_CHECKING

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


_lowerCamelCase			:	Union[str, Any]							 =     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	Optional[Any]							 =     ["""DPTFeatureExtractor"""]
  _lowerCamelCase			:	int							 =     ["""DPTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase			:	Optional[int]							 =     [
      """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPTForDepthEstimation""",
      """DPTForSemanticSegmentation""",
      """DPTModel""",
      """DPTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_dpt import DPTFeatureExtractor
    from .image_processing_dpt import DPTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpt import (
        DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPTForDepthEstimation,
        DPTForSemanticSegmentation,
        DPTModel,
        DPTPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase			:	Any							 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


