




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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_					,     lowercase_					,     lowercase_					,     lowercase_ = 16      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''bert-base-cased'''      )
    __UpperCamelCase  		=						DatasetDict(
        {
            '''train''': dataset['''train'''].select(lowercase_      ),
            '''validation''': dataset['''train'''].select(lowercase_      ),
            '''test''': dataset['''validation'''],
        }      )

    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_      )

    __UpperCamelCase  		=						DataLoader(
        tokenized_datasets['''test''']					,     shuffle=lowercase_					,     collate_fn=lowercase_					,     batch_size=lowercase_      )

    return train_dataloader, eval_dataloader, test_dataloader




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    # Download the dataset
    __UpperCamelCase  		=						load_dataset('''glue'''					,     '''mrpc'''      )
    # Create our splits
    __UpperCamelCase  		=						StratifiedKFold(n_splits=int(args.num_folds      )      )
    # Initialize accelerator
    __UpperCamelCase  		=						Accelerator(cpu=args.cpu					,     mixed_precision=args.mixed_precision      )
    # 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'''      )

    # If the batch size is too big we use gradient accumulation
    __UpperCamelCase  		=						1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __UpperCamelCase  		=						batch_size // MAX_GPU_BATCH_SIZE
        __UpperCamelCase  		=						MAX_GPU_BATCH_SIZE

    set_seed(lowercase_      )

    # New Code #
    # Create our folds:
    __UpperCamelCase  		=						kfold.split(np.zeros(datasets['''train'''].num_rows      )					,     datasets['''train''']['''label''']      )
    __UpperCamelCase  		=						[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(lowercase_      ):
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						get_fold_dataloaders(
            lowercase_					,     lowercase_					,     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) // 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.
        __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						accelerator.prepare(
            lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

        # Now we train the model
        for epoch in range(lowercase_      ):
            model.train()
            for step, batch in enumerate(lowercase_      ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device      )
                __UpperCamelCase  		=						model(**lowercase_      )
                __UpperCamelCase  		=						outputs.loss
                __UpperCamelCase  		=						loss / gradient_accumulation_steps
                accelerator.backward(lowercase_      )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            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_      )

        # New Code #
        # We also run predictions on the test set at the very end
        __UpperCamelCase  		=						[]
        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
            __UpperCamelCase		,      __UpperCamelCase  		=						accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
            fold_predictions.append(predictions.cpu()      )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()      )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(lowercase_					,     dim=0      )      )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    __UpperCamelCase  		=						torch.cat(lowercase_					,     dim=0      )
    __UpperCamelCase  		=						torch.stack(lowercase_					,     dim=0      ).sum(dim=0      ).div(int(args.num_folds      )      ).argmax(dim=-1      )
    __UpperCamelCase  		=						metric.compute(predictions=lowercase_					,     references=lowercase_      )
    accelerator.print('''Average test metrics from all folds:'''					,     lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Tuple:

    """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.'''					,     )
    parser.add_argument('''--cpu'''					,     action='''store_true'''					,     help='''If passed, will train on the CPU.'''      )
    # New Code #
    parser.add_argument('''--num_folds'''					,     type=lowercase_					,     default=3					,     help='''The number of splits to perform across the dataset'''      )
    __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))






