





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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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					:				Dict				=			16
_A					:				Tuple				=			32


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ = 1_6	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    SCREAMING_SNAKE_CASE__											=			load_dataset('''glue''' ,							'''mrpc'''	)

    def tokenize_function(lowerCAmelCase_	):
        # max_length=None => use the model max length (it's actually the default)
        SCREAMING_SNAKE_CASE__											=			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():
        SCREAMING_SNAKE_CASE__											=			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
    SCREAMING_SNAKE_CASE__											=			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.
        SCREAMING_SNAKE_CASE__											=			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":
            SCREAMING_SNAKE_CASE__											=			1_6
        elif accelerator.mixed_precision != "no":
            SCREAMING_SNAKE_CASE__											=			8
        else:
            SCREAMING_SNAKE_CASE__											=			None

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

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

  _A					:				Optional[Any]				=			mocked_dataloaders  # noqa: F811


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							lowerCAmelCase_	) == "1":
        SCREAMING_SNAKE_CASE__											=			2
    # Initialize accelerator
    SCREAMING_SNAKE_CASE__											=			Accelerator(cpu=args.cpu ,							mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    SCREAMING_SNAKE_CASE__											=			config['''lr''']
    SCREAMING_SNAKE_CASE__											=			int(config['''num_epochs''']	)
    SCREAMING_SNAKE_CASE__											=			int(config['''seed''']	)
    SCREAMING_SNAKE_CASE__											=			int(config['''batch_size''']	)

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

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

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

    # Instantiate optimizer
    SCREAMING_SNAKE_CASE__											=			AdamW(params=model.parameters() ,							lr=lowerCAmelCase_	)

    # Instantiate scheduler
    SCREAMING_SNAKE_CASE__											=			get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase_ ,							num_warmup_steps=1_0_0 ,							num_training_steps=(len(lowerCAmelCase_	) * 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.
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			accelerator.prepare(
        lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)

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

        model.eval()
        SCREAMING_SNAKE_CASE__											=			0
        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():
                SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			outputs.logits.argmax(dim=-1	)
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			accelerator.gather((predictions, batch['''labels'''])	)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(lowerCAmelCase_	) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    SCREAMING_SNAKE_CASE__											=			predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    SCREAMING_SNAKE_CASE__											=			references[: len(eval_dataloader.dataset	) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=lowerCAmelCase_ ,							references=lowerCAmelCase_ ,							)

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


def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			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.''' ,							)
    parser.add_argument('''--cpu''' ,							action='''store_true''' ,							help='''If passed, will train on the CPU.'''	)
    SCREAMING_SNAKE_CASE__											=			parser.parse_args()
    SCREAMING_SNAKE_CASE__											=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
    training_function(lowerCAmelCase_ ,							lowerCAmelCase_	)


if __name__ == "__main__":
  main()




def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						assert column_title.isupper()
						__snake_case				=		0
						__snake_case				=		len(_lowerCAmelCase			) - 1
						__snake_case				=		0

						while index >= 0:
												__snake_case				=		(ord(column_title[index]			) - 64) * pow(26   ,						_lowerCAmelCase			)
												answer += value
												power += 1
												index -= 1

						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()





