




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_   		=     16
a_   		=     32




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = 16      )       ->	Tuple:

    """simple docstring"""


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

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

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

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

    # Instantiate dataloaders.
    __UpperCamelCase  		=						DataLoader(
        tokenized_datasets['''train''']					,     shuffle=lowercase_					,     collate_fn=lowercase_					,     batch_size=lowercase_      )
    __UpperCamelCase  		=						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_   		=     mocked_dataloaders  # noqa: F811




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,     lowercase_      ) == "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=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
    __UpperCamelCase  		=						config['''lr''']
    __UpperCamelCase  		=						int(config['''num_epochs''']      )
    __UpperCamelCase  		=						int(config['''seed''']      )
    __UpperCamelCase  		=						int(config['''batch_size''']      )

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

    set_seed(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						get_dataloaders(lowercase_					,     lowercase_      )
    # 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=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).
    __UpperCamelCase  		=						model.to(accelerator.device      )

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

    # Instantiate scheduler
    __UpperCamelCase  		=						get_linear_schedule_with_warmup(
        optimizer=lowercase_					,     num_warmup_steps=1_00					,     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.
    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						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_      ):
                    __UpperCamelCase  		=						model(**lowercase_      )
                    __UpperCamelCase  		=						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():
                __UpperCamelCase  		=						model(**lowercase_      )
            __UpperCamelCase  		=						outputs.logits.argmax(dim=-1      )
            __UpperCamelCase		,      __UpperCamelCase  		=						accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
            metric.add_batch(
                predictions=lowercase_					,     references=lowercase_					,     )

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




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						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.'''      )
    __UpperCamelCase  		=						parser.parse_args()
    __UpperCamelCase  		=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowercase_					,     lowercase_      )


if __name__ == "__main__":
 main()












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

a_   		=     ort.SessionOptions()
a_   		=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
a_   		=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
a_   		=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
a_   		=     ort.RunOptions()

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))






