






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a   :							str						= logging.get_logger(__name__)

__a   :							int						= {
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"swinv2"

     SCREAMING_SNAKE_CASE     =					{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }




     def __init__(					self							:      Optional[Any]			,						UpperCamelCase_							:      Dict=224			,						UpperCamelCase_							:      str=4			,						UpperCamelCase_							:      Optional[int]=3			,						UpperCamelCase_							:      Any=96			,						UpperCamelCase_							:      List[Any]=[2, 2, 6, 2]			,						UpperCamelCase_							:      int=[3, 6, 12, 24]			,						UpperCamelCase_							:      Optional[int]=7			,						UpperCamelCase_							:      int=4.0			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      List[str]=0.0			,						UpperCamelCase_							:      List[str]=0.0			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      Any="gelu"			,						UpperCamelCase_							:      Dict=False			,						UpperCamelCase_							:      Union[str, Any]=0.02			,						UpperCamelCase_							:      Optional[Any]=1e-5			,						UpperCamelCase_							:      Tuple=32			,						**UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



         super().__init__(**UpperCamelCase_					)

         __A       =						image_size
         __A       =						patch_size
         __A       =						num_channels
         __A       =						embed_dim
         __A       =						depths
         __A       =						len(UpperCamelCase_					)
         __A       =						num_heads
         __A       =						window_size
         __A       =						mlp_ratio
         __A       =						qkv_bias
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						drop_path_rate
         __A       =						hidden_act
         __A       =						use_absolute_embeddings
         __A       =						layer_norm_eps
         __A       =						initializer_range
         __A       =						encoder_stride
         # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         __A       =						int(embed_dim * 2 ** (len(UpperCamelCase_					) - 1)					)
         __A       =						(0, 0, 0, 0)












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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


__a   :							int						= 16
__a   :							str						= 32



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Accelerator      ,					__lowercase :      int = 1_6     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
    __A       =						load_dataset("""glue"""      ,					"""mrpc"""     )

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

    def collate_fn(__lowercase :      List[Any]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __A       =						1_2_8 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       =						1_6
        elif accelerator.mixed_precision != "no":
            __A       =						8
        else:
            __A       =						None

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

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

  __a   :							int						= mocked_dataloaders  # noqa: F811



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      List[Any]     )    ->   Tuple:





    """simple docstring"""

    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,					__lowercase     ) == "1":
        __A       =						2
    # New Code #
    __A       =						int(args.gradient_accumulation_steps     )
    __A       =						int(args.local_sgd_steps     )
    # Initialize accelerator
    __A       =						Accelerator(
        cpu=args.cpu      ,					mixed_precision=args.mixed_precision      ,					gradient_accumulation_steps=__lowercase     )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""     )
    # 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"""     )

    set_seed(__lowercase     )
    __A     ,						__A       =						get_dataloaders(__lowercase      ,					__lowercase     )
    # 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=__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).
    __A       =						model.to(accelerator.device     )

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

    # Instantiate scheduler
    __A       =						get_linear_schedule_with_warmup(
        optimizer=__lowercase      ,					num_warmup_steps=1_0_0      ,					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.
    __A     ,						__A     ,						__A     ,						__A     ,						__A       =						accelerator.prepare(
        __lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )

    # Now we train the model
    for epoch in range(__lowercase     ):
        model.train()
        with LocalSGD(
            accelerator=__lowercase      ,					model=__lowercase      ,					local_sgd_steps=__lowercase      ,					enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(__lowercase     ):
                # 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(__lowercase     ):
                    __A       =						model(**__lowercase     )
                    __A       =						output.loss
                    accelerator.backward(__lowercase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        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():
                __A       =						model(**__lowercase     )
            __A       =						outputs.logits.argmax(dim=-1     )
            __A     ,						__A       =						accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
            metric.add_batch(
                predictions=__lowercase      ,					references=__lowercase      ,					)

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



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    __A       =						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."""      ,					)
    # New Code #
    parser.add_argument(
        """--gradient_accumulation_steps"""      ,					type=__lowercase      ,					default=1      ,					help="""The number of minibatches to be ran before gradients are accumulated."""      ,					)
    parser.add_argument(
        """--local_sgd_steps"""      ,					type=__lowercase      ,					default=8      ,					help="""Number of local SGD steps or None to disable local SGD"""     )
    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""": 4_2, """batch_size""": 1_6}
    training_function(__lowercase      ,					__lowercase     )


if __name__ == "__main__":
  main()




