




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


_lowerCamelCase      			=						16
_lowerCamelCase      			=						32




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Accelerator	,					__UpperCamelCase      :						int = 16				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					AutoTokenizer.from_pretrained('''bert-base-cased'''				)
    UpperCAmelCase_										=					load_dataset('''glue'''	,					'''mrpc'''				)

    def tokenize_function(__UpperCamelCase      :						int				):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase_										=					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():
        UpperCAmelCase_										=					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
    UpperCAmelCase_										=					tokenized_datasets.rename_column('''label'''	,					'''labels'''				)

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

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

    # Instantiate dataloaders.
    UpperCAmelCase_										=					DataLoader(
        tokenized_datasets['''train''']	,					shuffle=__UpperCamelCase	,					collate_fn=__UpperCamelCase	,					batch_size=__UpperCamelCase				)
    UpperCAmelCase_										=					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

 _lowerCamelCase      			=						mocked_dataloaders  # noqa: F811




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Union[str, Any]				)  ->							Any:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,					__UpperCamelCase				) == "1":
        UpperCAmelCase_										=					2
    # New Code #
    UpperCAmelCase_										=					int(args.gradient_accumulation_steps				)
    UpperCAmelCase_										=					int(args.local_sgd_steps				)
    # Initialize accelerator
    UpperCAmelCase_										=					Accelerator(
        cpu=args.cpu	,					mixed_precision=args.mixed_precision	,					gradient_accumulation_steps=__UpperCamelCase				)
    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
    UpperCAmelCase_										=					config['''lr''']
    UpperCAmelCase_										=					int(config['''num_epochs''']				)
    UpperCAmelCase_										=					int(config['''seed''']				)
    UpperCAmelCase_										=					int(config['''batch_size''']				)

    UpperCAmelCase_										=					evaluate.load('''glue'''	,					'''mrpc'''				)

    set_seed(__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_dataloaders(__UpperCamelCase	,					__UpperCamelCase				)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    UpperCAmelCase_										=					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).
    UpperCAmelCase_										=					model.to(accelerator.device				)

    # Instantiate optimizer
    UpperCAmelCase_										=					AdamW(params=model.parameters()	,					lr=__UpperCamelCase				)

    # Instantiate scheduler
    UpperCAmelCase_										=					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.
    UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					accelerator.prepare(
        __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

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

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

        UpperCAmelCase_										=					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'	,					__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	)  ->							Optional[Any]:
    UpperCAmelCase_										=					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(
        '''--local_sgd_steps'''	,					type=__UpperCamelCase	,					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.'''				)
    UpperCAmelCase_										=					parser.parse_args()
    UpperCAmelCase_										=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__UpperCamelCase	,					__UpperCamelCase				)


if __name__ == "__main__":
 main()











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(pad_token_id=__snake_case ,   bos_token_id=__snake_case ,   eos_token_id=__snake_case ,   **__snake_case    )

         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					hidden_size
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					intermediate_size
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					layer_norm_eps
         UpperCAmelCase_										=					position_embedding_type
         UpperCAmelCase_										=					use_cache
         UpperCAmelCase_										=					classifier_dropout



class       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         if self.task == "multiple-choice":
             UpperCAmelCase_										=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             UpperCAmelCase_										=					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





