



'''simple docstring'''





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 how to perform Cross Validation,
# 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase = 16      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[int]											= AutoTokenizer.from_pretrained("""bert-base-cased"""      )
    lowerCAmelCase__			:  Optional[int]											= DatasetDict(
        {
            """train""": dataset["""train"""].select(UpperCamelCase      ),
            """validation""": dataset["""train"""].select(UpperCamelCase      ),
            """test""": dataset["""validation"""],
        }      )

    def tokenize_function(UpperCamelCase      ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__			:  List[Any]											= 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__			:  Tuple											= 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__			:  Tuple											= 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__			:  List[Any]											= 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__			:  Dict											= 16
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__			:  int											= 8
        else:
            lowerCAmelCase__			:  Optional[int]											= None

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

    # Instantiate dataloaders.
    lowerCAmelCase__			:  List[str]											= DataLoader(
        tokenized_datasets["""train"""]							,   shuffle=UpperCamelCase							,   collate_fn=UpperCamelCase							,   batch_size=UpperCamelCase      )
    lowerCAmelCase__			:  Dict											= DataLoader(
        tokenized_datasets["""validation"""]							,   shuffle=UpperCamelCase							,   collate_fn=UpperCamelCase							,   batch_size=UpperCamelCase      )

    lowerCAmelCase__			:  List[Any]											= DataLoader(
        tokenized_datasets["""test"""]							,   shuffle=UpperCamelCase							,   collate_fn=UpperCamelCase							,   batch_size=UpperCamelCase      )

    return train_dataloader, eval_dataloader, test_dataloader
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  str											= []
    # Download the dataset
    lowerCAmelCase__			:  Optional[Any]											= load_dataset("""glue"""							,   """mrpc"""      )
    # Create our splits
    lowerCAmelCase__			:  str											= StratifiedKFold(n_splits=int(args.num_folds      )      )
    # Initialize accelerator
    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__			:  Optional[int]											= config["""lr"""]
    lowerCAmelCase__			:  List[Any]											= int(config["""num_epochs"""]      )
    lowerCAmelCase__			:  str											= int(config["""seed"""]      )
    lowerCAmelCase__			:  Union[str, Any]											= int(config["""batch_size"""]      )

    lowerCAmelCase__			:  Union[str, Any]											= evaluate.load("""glue"""							,   """mrpc"""      )

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

    set_seed(UpperCamelCase      )

    # New Code #
    # Create our folds:
    lowerCAmelCase__			:  str											= kfold.split(np.zeros(datasets["""train"""].num_rows      )							,   datasets["""train"""]["""label"""]      )
    lowerCAmelCase__			:  Any											= []
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase      ):
        lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= get_fold_dataloaders(
            UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        lowerCAmelCase__			:  List[str]											= 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__			:  int											= model.to(accelerator.device      )

        # Instantiate optimizer
        lowerCAmelCase__			:  Union[str, Any]											= AdamW(params=model.parameters()							,   lr=UpperCamelCase      )

        # Instantiate scheduler
        lowerCAmelCase__			:  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__			:  Tuple											= accelerator.prepare(
            UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

        # Now we train the model
        for epoch in range(UpperCamelCase      ):
            model.train()
            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__			:  Optional[int]											= model(**UpperCamelCase      )
                lowerCAmelCase__			:  Optional[int]											= outputs.loss
                lowerCAmelCase__			:  Any											= 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`.
                batch.to(accelerator.device      )
                with torch.no_grad():
                    lowerCAmelCase__			:  List[Any]											= model(**UpperCamelCase      )
                lowerCAmelCase__			:  int											= outputs.logits.argmax(dim=-1      )
                lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
                metric.add_batch(
                    predictions=UpperCamelCase							,   references=UpperCamelCase							,   )

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

        # New Code #
        # We also run predictions on the test set at the very end
        lowerCAmelCase__			:  Union[str, Any]											= []
        for step, batch in enumerate(UpperCamelCase      ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device      )
            with torch.no_grad():
                lowerCAmelCase__			:  List[str]											= model(**UpperCamelCase      )
            lowerCAmelCase__			:  Any											= outputs.logits
            lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
            fold_predictions.append(predictions.cpu()      )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()      )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(UpperCamelCase							,   dim=0      )      )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    lowerCAmelCase__			:  List[Any]											= torch.cat(UpperCamelCase							,   dim=0      )
    lowerCAmelCase__			:  str											= torch.stack(UpperCamelCase							,   dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
    lowerCAmelCase__			:  Optional[int]											= metric.compute(predictions=UpperCamelCase							,   references=UpperCamelCase      )
    accelerator.print("""Average test metrics from all folds:"""							,   UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  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."""      )
    # New Code #
    parser.add_argument("""--num_folds"""							,   type=UpperCamelCase							,   default=3							,   help="""The number of splits to perform across the dataset"""      )
    lowerCAmelCase__			:  List[str]											= parser.parse_args()
    lowerCAmelCase__			:  Optional[Any]											= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(UpperCamelCase							,   UpperCamelCase      )


if __name__ == "__main__":
 main()







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase										=     {
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase										=     [
     '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Blip2Model''',
     '''Blip2QFormerModel''',
     '''Blip2PreTrainedModel''',
     '''Blip2ForConditionalGeneration''',
     '''Blip2VisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

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


