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


UpperCAmelCase_								=							16
UpperCAmelCase_								=							32

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_ = 16   )		->						List[str]:




       '''simple docstring'''






       _A=	AutoTokenizer.from_pretrained('bert-base-cased'   )
       _A=	load_dataset('glue'       ,					'mrpc'   )

       def tokenize_function(lowerCAmelCase_   ):
              # max_length=None => use the model max length (it's actually the default)
              _A=	tokenizer(examples['sentence1']       ,					examples['sentence2']       ,					truncation=lowerCAmelCase_       ,					max_length=lowerCAmelCase_   )
              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(
                  lowerCAmelCase_       ,					batched=lowerCAmelCase_       ,					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(lowerCAmelCase_   ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              _A=	1_28 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=	16
              elif accelerator.mixed_precision != "no":
                     _A=	8
              else:
                     _A=	None

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

       # Instantiate dataloaders.
       _A=	DataLoader(
           tokenized_datasets['train']       ,					shuffle=lowerCAmelCase_       ,					collate_fn=lowerCAmelCase_       ,					batch_size=lowerCAmelCase_   )
       _A=	DataLoader(
           tokenized_datasets['validation']       ,					shuffle=lowerCAmelCase_       ,					collate_fn=lowerCAmelCase_       ,					batch_size=lowerCAmelCase_   )

       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_								=							mocked_dataloaders  # noqa: F811

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,					lowerCAmelCase_   ) == "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=lowerCAmelCase_   )
       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(lowerCAmelCase_   )
       _A,			_A=	get_dataloaders(lowerCAmelCase_       ,					lowerCAmelCase_   )
       # 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=lowerCAmelCase_   )

       # 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=lowerCAmelCase_   )

       # Instantiate scheduler
       _A=	get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase_       ,					num_warmup_steps=1_00       ,					num_training_steps=(len(lowerCAmelCase_   ) * 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(
           lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )

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

              model.eval()
              for step, batch in enumerate(lowerCAmelCase_   ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device   )
                     with torch.no_grad():
                            _A=	model(**lowerCAmelCase_   )
                     _A=	outputs.logits.argmax(dim=-1   )
                     _A,			_A=	accelerator.gather_for_metrics((predictions, batch['labels'])   )
                     metric.add_batch(
                         predictions=lowerCAmelCase_       ,					references=lowerCAmelCase_       ,					)

              _A=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"epoch {epoch}:"       ,					lowerCAmelCase_   )

def 							UpperCamelCase				(    )		->						List[str]:




       '''simple docstring'''






       _A=	argparse.ArgumentParser(description='Simple example of training script.'   )
       parser.add_argument(
           '--mixed_precision'       ,					type=lowerCAmelCase_       ,					default=lowerCAmelCase_       ,					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=lowerCAmelCase_       ,					default=1       ,					help='The number of minibatches to be ran before gradients are accumulated.'       ,					)
       parser.add_argument(
           '--local_sgd_steps'       ,					type=lowerCAmelCase_       ,					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': 42, 'batch_size': 16}
       training_function(lowerCAmelCase_       ,					lowerCAmelCase_   )


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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		="""convbert"""



     def __init__(					self				,      lowerCAmelCase__=30522				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=2				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=1E-12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=768				,      lowerCAmelCase__=2				,      lowerCAmelCase__=9				,      lowerCAmelCase__=1				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	intermediate_size
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	initializer_range
            _A=	layer_norm_eps
            _A=	embedding_size
            _A=	head_ratio
            _A=	conv_kernel_size
            _A=	num_groups
            _A=	classifier_dropout





class      lowerCAmelCase						(					_a			):



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