





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
# and perform 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)
#
# 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				:       Tuple	   =				16
_UpperCAmelCase				:       Optional[int]	   =				32


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = 16 )			->     Optional[int]:
    lowerCamelCase__    :      Optional[Any]					 =		AutoTokenizer.from_pretrained('bert-base-cased' )
    lowerCamelCase__    :      List[str]					 =		load_dataset('glue'						,	'mrpc' )

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

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

    # Instantiate dataloaders.
    lowerCamelCase__    :      Optional[int]					 =		DataLoader(
        tokenized_datasets['train']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase )
    lowerCamelCase__    :      Optional[int]					 =		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

    _UpperCAmelCase				:       str	   =				mocked_dataloaders  # noqa: F811


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     List[Any]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'						,	_UpperCAmelCase ) == "1":
        lowerCamelCase__    :      Optional[int]					 =		2
    # New Code #
    lowerCamelCase__    :      int					 =		int(args.gradient_accumulation_steps )
    # Initialize accelerator
    lowerCamelCase__    :      Dict					 =		Accelerator(
        cpu=args.cpu						,	mixed_precision=args.mixed_precision						,	gradient_accumulation_steps=_UpperCAmelCase )
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`' )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCamelCase__    :      Any					 =		config['lr']
    lowerCamelCase__    :      Union[str, Any]					 =		int(config['num_epochs'] )
    lowerCamelCase__    :      Optional[int]					 =		int(config['seed'] )
    lowerCamelCase__    :      List[Any]					 =		int(config['batch_size'] )

    lowerCamelCase__    :      Optional[Any]					 =		evaluate.load('glue'						,	'mrpc' )

    set_seed(_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		get_dataloaders(_UpperCAmelCase						,	_UpperCAmelCase )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__    :      Optional[int]					 =		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__    :      Tuple					 =		model.to(accelerator.device )

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

    # Instantiate scheduler
    lowerCamelCase__    :      int					 =		get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase						,	num_warmup_steps=100						,	num_training_steps=(len(_UpperCAmelCase ) * 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.
    lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		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 )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(_UpperCAmelCase ):
                lowerCamelCase__    :      Any					 =		model(**_UpperCAmelCase )
                lowerCamelCase__    :      str					 =		output.loss
                accelerator.backward(_UpperCAmelCase )
                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__    :      Optional[Any]					 =		model(**_UpperCAmelCase )
            lowerCamelCase__    :      List[Any]					 =		outputs.logits.argmax(dim=-1 )
            lowerCamelCase__ ,							lowerCamelCase__    :      List[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 )


def        SCREAMING_SNAKE_CASE      (       )			->     Dict:
    lowerCamelCase__    :      Optional[int]					 =		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.'						,	)
    # New Code #
    parser.add_argument(
        '--gradient_accumulation_steps'						,	type=_UpperCAmelCase						,	default=1						,	help='The number of minibatches to be ran before gradients are accumulated.'						,	)
    parser.add_argument('--cpu'						,	action='store_true'						,	help='If passed, will train on the CPU.' )
    lowerCamelCase__    :      Optional[Any]					 =		parser.parse_args()
    lowerCamelCase__    :      Tuple					 =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(_UpperCAmelCase						,	_UpperCAmelCase )


if __name__ == "__main__":
    main()












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





