


"""simple docstring"""



import argparse

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


__snake_case   			=    16
__snake_case   			=    32







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Union[str, Any] = 16 )	->						str:


    """simple docstring"""





    snake_case    :      int      					=    AutoTokenizer.from_pretrained("bert-base-cased" )
    snake_case    :      Union[str, Any]      					=    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    :      int      					=    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():
        snake_case    :      Any      					=    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
    snake_case    :      Dict      					=    tokenized_datasets.rename_column("label"						,							"labels" )

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

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

    # Instantiate dataloaders.
    snake_case    :      List[str]      					=    DataLoader(
        tokenized_datasets["train"]						,							shuffle=_UpperCAmelCase						,							collate_fn=_UpperCAmelCase						,							batch_size=_UpperCAmelCase						,							drop_last=_UpperCAmelCase )
    snake_case    :      str      					=    DataLoader(
        tokenized_datasets["validation"]						,							shuffle=_UpperCAmelCase						,							collate_fn=_UpperCAmelCase						,							batch_size=_UpperCAmelCase						,							drop_last=(accelerator.mixed_precision == "fp8")						,							)

    return train_dataloader, eval_dataloader







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any] )	->						Optional[Any]:


    """simple docstring"""





    snake_case    :      Union[str, 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    :      int      					=    config['lr']
    snake_case    :      Tuple      					=    int(config["num_epochs"] )
    snake_case    :      Union[str, Any]      					=    int(config["seed"] )
    snake_case    :      List[str]      					=    int(config["batch_size"] )

    snake_case    :      List[str]      					=    evaluate.load("glue"						,							"mrpc" )

    # If the batch size is too big we use gradient accumulation
    snake_case    :      Dict      					=    1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        snake_case    :      Optional[Any]      					=    batch_size // MAX_GPU_BATCH_SIZE
        snake_case    :      Any      					=    MAX_GPU_BATCH_SIZE

    set_seed(_UpperCAmelCase )
    snake_case    :      Dict      					=    get_dataloaders(_UpperCAmelCase						,							_UpperCAmelCase )
    # 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=_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).
    snake_case    :      Dict      					=    model.to(accelerator.device )
    # Instantiate optimizer
    snake_case    :      int      					=    AdamW(params=model.parameters()						,							lr=_UpperCAmelCase )

    # Instantiate scheduler
    snake_case    :      Dict      					=    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.

    snake_case    :      int      					=    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 )
            snake_case    :      Optional[int]      					=    model(**_UpperCAmelCase )
            snake_case    :      Any      					=    outputs.loss
            snake_case    :      Optional[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():
                snake_case    :      List[Any]      					=    model(**_UpperCAmelCase )
            snake_case    :      Any      					=    outputs.logits.argmax(dim=-1 )
            snake_case    :      Optional[int]      					=    accelerator.gather_for_metrics((predictions, batch["labels"]) )
            metric.add_batch(
                predictions=_UpperCAmelCase						,							references=_UpperCAmelCase						,							)

        snake_case    :      List[Any]      					=    metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'						,							_UpperCAmelCase )







def   __lowerCAmelCase						(						)	->						Optional[Any]:


    """simple docstring"""





    snake_case    :      str      					=    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." )
    snake_case    :      Tuple      					=    parser.parse_args()
    snake_case    :      Dict      					=    {'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





