



import argparse

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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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
#
########################################################################


_lowercase						  =		16
_lowercase						  =		32



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Accelerator ,       UpperCamelCase_				:       int = 1_6	)->  int:
    A__		 =					AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    A__		 =					load_dataset('''glue''' ,       '''mrpc'''	)

    def tokenize_function(UpperCamelCase_				:       Optional[Any]	):
        # max_length=None => use the model max length (it's actually the default)
        A__		 =					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():
        A__		 =					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
    A__		 =					tokenized_datasets.rename_column('''label''' ,       '''labels'''	)

    def collate_fn(UpperCamelCase_				:       Optional[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(
            UpperCamelCase_ ,       padding='''longest''' ,       max_length=UpperCamelCase_ ,       pad_to_multiple_of=UpperCamelCase_ ,       return_tensors='''pt''' ,       )

    # Instantiate dataloaders.
    A__		 =					DataLoader(
        tokenized_datasets['''train'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_ ,       drop_last=UpperCamelCase_	)
    A__		 =					DataLoader(
        tokenized_datasets['''validation'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_ ,       drop_last=(accelerator.mixed_precision == '''fp8''') ,       )

    return train_dataloader, eval_dataloader



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Optional[int] ,       UpperCamelCase_				:       Optional[Any]	)->  int:
    # Initialize accelerator
    A__		 =					Accelerator(cpu=args.cpu ,       mixed_precision=args.mixed_precision	)
    # 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'''	)

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

    set_seed(UpperCamelCase_	)
    A__ ,	A__		 =					get_dataloaders(UpperCamelCase_ ,       UpperCamelCase_	)
    # 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=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).
    A__		 =					model.to(accelerator.device	)
    # Instantiate optimizer
    A__		 =					AdamW(params=model.parameters() ,       lr=UpperCamelCase_	)

    # Instantiate scheduler
    A__		 =					get_linear_schedule_with_warmup(
        optimizer=UpperCamelCase_ ,       num_warmup_steps=1_0_0 ,       num_training_steps=(len(UpperCamelCase_	) * 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.

    A__ ,	A__ ,	A__ ,	A__ ,	A__		 =					accelerator.prepare(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

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

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

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



def 		lowerCAmelCase__    (					)->  str:
    A__		 =					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.''' ,       )
    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(UpperCamelCase_ ,       UpperCamelCase_	)


if __name__ == "__main__":
   main()





def 		lowerCAmelCase__    (					UpperCamelCase_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
