




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 the checkpointing capability,
# 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_ = 16   )							->						int:

    """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=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():
        __UpperCamelCase			=    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
    __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(
            lowerCAmelCase_       ,  padding='''longest'''       ,  max_length=lowerCAmelCase_       ,  pad_to_multiple_of=lowerCAmelCase_       ,  return_tensors='''pt'''       ,  )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,  lowerCAmelCase_   ) == "1":
        __UpperCamelCase			=    2
    # 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''']   )

    # New Code #
    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps       ,  '''isdigit'''   ):
        if args.checkpointing_steps == "epoch":
            __UpperCamelCase			=    args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __UpperCamelCase			=    int(args.checkpointing_steps   )
        else:
            raise ValueError(
                F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."   )
    else:
        __UpperCamelCase			=    None

    set_seed(lowerCAmelCase_   )

    __UpperCamelCase		,   __UpperCamelCase			=    get_dataloaders(lowerCAmelCase_       ,  lowerCAmelCase_   )
    __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

    # 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=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).
    __UpperCamelCase			=    model.to(accelerator.device   )

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

    # Instantiate scheduler
    __UpperCamelCase			=    get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase_       ,  num_warmup_steps=1_00       ,  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.
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(
        lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_   )

    # New Code #
    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase			=    0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase			=    0

    # We need to load the checkpoint back in before training here with `load_state`
    # The total number of epochs is adjusted based on where the state is being loaded from,
    # as we assume continuation of the same training script
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"   )
            accelerator.load_state(args.resume_from_checkpoint   )
            __UpperCamelCase			=    os.path.basename(args.resume_from_checkpoint   )
        else:
            # Get the most recent checkpoint
            __UpperCamelCase			=    [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
            dirs.sort(key=os.path.getctime   )
            __UpperCamelCase			=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __UpperCamelCase			=    os.path.splitext(lowerCAmelCase_   )[0]

        if "epoch" in training_difference:
            __UpperCamelCase			=    int(training_difference.replace('''epoch_'''       ,  ''''''   )   ) + 1
            __UpperCamelCase			=    None
        else:
            __UpperCamelCase			=    int(training_difference.replace('''step_'''       ,  ''''''   )   )
            __UpperCamelCase			=    resume_step // len(lowerCAmelCase_   )
            resume_step -= starting_epoch * len(lowerCAmelCase_   )

    # Now we train the model
    for epoch in range(lowerCAmelCase_       ,  lowerCAmelCase_   ):
        model.train()
        # New Code #
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            __UpperCamelCase			=    accelerator.skip_first_batches(lowerCAmelCase_       ,  lowerCAmelCase_   )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __UpperCamelCase			=    train_dataloader
        for step, batch in enumerate(lowerCAmelCase_   ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device   )
            __UpperCamelCase			=    model(**lowerCAmelCase_   )
            __UpperCamelCase			=    outputs.loss
            __UpperCamelCase			=    loss / gradient_accumulation_steps
            accelerator.backward(lowerCAmelCase_   )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
            # New Code #
            overall_step += 1

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `step_{overall_step}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_   ):
                __UpperCamelCase			=    F"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __UpperCamelCase			=    os.path.join(args.output_dir       ,  lowerCAmelCase_   )
                    accelerator.save_state(lowerCAmelCase_   )

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

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

        # New Code #
        # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
        # These are saved to folders named `epoch_{epoch}`
        # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
        # If mixed precision was used, will also save a "scalar.bin" file
        if checkpointing_steps == "epoch":
            __UpperCamelCase			=    F"epoch_{epoch}"
            if args.output_dir is not None:
                __UpperCamelCase			=    os.path.join(args.output_dir       ,  lowerCAmelCase_   )
            accelerator.save_state(lowerCAmelCase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    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.'''   )
    parser.add_argument(
        '''--checkpointing_steps'''       ,  type=lowerCAmelCase_       ,  default=lowerCAmelCase_       ,  help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''       ,  )
    parser.add_argument(
        '''--output_dir'''       ,  type=lowerCAmelCase_       ,  default='''.'''       ,  help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''       ,  )
    parser.add_argument(
        '''--resume_from_checkpoint'''       ,  type=lowerCAmelCase_       ,  default=lowerCAmelCase_       ,  help='''If the training should continue from a checkpoint folder.'''       ,  )
    __UpperCamelCase			=    parser.parse_args()
    __UpperCamelCase			=    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowerCAmelCase_       ,  lowerCAmelCase_   )


if __name__ == "__main__":
 main()









import numpy as np







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=np.exp(lowerCAmelCase_     )

     # Add up the all the exponentials
     SCREAMING_SNAKE_CASE 		=np.sum(lowerCAmelCase_     )

     # Divide every exponent by the sum of all exponents
     SCREAMING_SNAKE_CASE 		=exponent_vector / sum_of_exponents

     return softmax_vector


if __name__ == "__main__":
 print(softmax((0,)))

