





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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

_UpperCamelCase    =	32







def  lowerCAmelCase__(		lowercase    :      Accelerator				,       lowercase    :      int = 16			)	->      Union[str, Any]:
  __snake_case				:			str            =    AutoTokenizer.from_pretrained("bert-base-cased"			)
  __snake_case				:			int            =    load_dataset("glue"				,       "mrpc"			)

  def tokenize_function(lowercase    :      List[Any]			):
    # max_length=None => use the model max length (it's actually the default)
    __snake_case				:			Optional[Any]            =    tokenizer(examples["sentence1"]				,       examples["sentence2"]				,       truncation=lowercase				,       max_length=lowercase			)
    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				:			Dict            =    datasets.map(
        lowercase				,       batched=lowercase				,       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(lowercase    :      int			):
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      __snake_case				:			Any            =    16
    elif accelerator.mixed_precision != "no":
      __snake_case				:			Tuple            =    8
    else:
      __snake_case				:			int            =    None

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

  # Instantiate dataloaders.
  __snake_case				:			Optional[int]            =    DataLoader(
      tokenized_datasets["train"]				,       shuffle=lowercase				,       collate_fn=lowercase				,       batch_size=lowercase			)
  __snake_case				:			Tuple            =    DataLoader(
      tokenized_datasets["validation"]				,       shuffle=lowercase				,       collate_fn=lowercase				,       batch_size=lowercase			)

  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

     _UpperCamelCase    =	mocked_dataloaders  # noqa: F811







def  lowerCAmelCase__(		lowercase    :      Optional[int]				,       lowercase    :      Dict			)	->      List[Any]:
  # For testing only
  if os.environ.get("TESTING_MOCKED_DATALOADERS"				,       lowercase			) == "1":
    __snake_case				:			str            =    2
  # Initialize accelerator
  __snake_case				:			Optional[Any]            =    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				:			Dict            =    config["lr"]
  __snake_case				:			Any            =    int(config["num_epochs"]			)
  __snake_case				:			str            =    int(config["seed"]			)
  __snake_case				:			List[Any]            =    int(config["batch_size"]			)

  __snake_case				:			Any            =    evaluate.load("glue"				,       "mrpc"			)

  # New Code #
  # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  # half each time. From this, we can calculate the number of gradient accumulation steps needed
  # and modify the Accelerator object as a result
  @find_executable_batch_size(starting_batch_size=int(lowercase			)			)
  def inner_training_loop(lowercase    :      Tuple			):
    # Since we need to modify the outside accelerator object, we need to bring it
    # to the local scope
    nonlocal accelerator

    # We can calculate the number of gradient accumulation steps based on the current
    # batch size vs the starting batch size
    __snake_case				:			Optional[Any]            =    observed_batch_size // batch_size

    # And then set it in the Accelerator directly:
    __snake_case				:			Optional[Any]            =    num_gradient_accumulation_steps

    # Next we need to free all of the stored model references in the Accelerator each time
    accelerator.free_memory()

    # And set the seed so our results are reproducable each reset
    set_seed(lowercase			)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __snake_case				:			Any            =    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,       return_dict=lowercase			)

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

    # Instantiate optimizer
    __snake_case				:			int            =    AdamW(params=model.parameters()				,       lr=lowercase			)
    __snake_case      ,				__snake_case				:			Union[str, Any]            =    get_dataloaders(lowercase				,       lowercase			)

    # Instantiate scheduler
    __snake_case				:			Tuple            =    get_linear_schedule_with_warmup(
        optimizer=lowercase				,       num_warmup_steps=100				,       num_training_steps=(len(lowercase			) * 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				:			Union[str, Any]            =    accelerator.prepare(
        lowercase				,       lowercase				,       lowercase				,       lowercase				,       lowercase			)

    # Now we train the model
    for epoch in range(lowercase			):
      model.train()
      for step, batch in enumerate(lowercase			):
        # And perform gradient accumulation
        with accelerator.accumulate(lowercase			):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device			)
          __snake_case				:			Union[str, Any]            =    model(**lowercase			)
          __snake_case				:			Any            =    outputs.loss
          accelerator.backward(lowercase			)
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

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

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

    # 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  lowerCAmelCase__(		)	->      Union[str, Any]:
  __snake_case				:			Tuple            =    argparse.ArgumentParser(description="Simple example of training script."			)
  parser.add_argument(
      "--mixed_precision"				,       type=lowercase				,       default=lowercase				,       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				:			Any            =    parser.parse_args()
  # New Code #
  # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  __snake_case				:			Dict            =    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
  training_function(lowercase				,       lowercase			)


if __name__ == "__main__":
     main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase    =	{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''DPRContextEncoderTokenizerFast''',
         '''DPRQuestionEncoderTokenizerFast''',
         '''DPRReaderTokenizerFast''',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DPRContextEncoder''',
         '''DPRPretrainedContextEncoder''',
         '''DPRPreTrainedModel''',
         '''DPRPretrainedQuestionEncoder''',
         '''DPRPretrainedReader''',
         '''DPRQuestionEncoder''',
         '''DPRReader''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase    =	[
         '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDPRContextEncoder''',
         '''TFDPRPretrainedContextEncoder''',
         '''TFDPRPretrainedQuestionEncoder''',
         '''TFDPRPretrainedReader''',
         '''TFDPRQuestionEncoder''',
         '''TFDPRReader''',
     ]


if TYPE_CHECKING:
     from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
     from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
     )

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_dpr_fast import (
              DPRContextEncoderTokenizerFast,
              DPRQuestionEncoderTokenizerFast,
              DPRReaderTokenizerFast,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpr import (
              DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPRContextEncoder,
              DPRPretrainedContextEncoder,
              DPRPreTrainedModel,
              DPRPretrainedQuestionEncoder,
              DPRPretrainedReader,
              DPRQuestionEncoder,
              DPRReader,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_dpr import (
              TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDPRContextEncoder,
              TFDPRPretrainedContextEncoder,
              TFDPRPretrainedQuestionEncoder,
              TFDPRPretrainedReader,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )

else:
     import sys

     _UpperCamelCase    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

