





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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				:							Optional[int]							= 16
_A				:							Union[str, Any]							= 32


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[Any]:
    # Initialize accelerator
    if args.with_tracking:
        SCREAMING_SNAKE_CASE__       							= Accelerator(
            cpu=args.cpu   ,       mixed_precision=args.mixed_precision   ,       log_with='''all'''   ,       project_dir=args.project_dir  )
    else:
        SCREAMING_SNAKE_CASE__       							= Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision  )

    if hasattr(args.checkpointing_steps   ,       '''isdigit'''  ):
        if args.checkpointing_steps == "epoch":
            SCREAMING_SNAKE_CASE__       							= args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            SCREAMING_SNAKE_CASE__       							= int(args.checkpointing_steps  )
        else:
            raise ValueError(
                f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''  )
    else:
        SCREAMING_SNAKE_CASE__       							= None
    # 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''']  )

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        SCREAMING_SNAKE_CASE__       							= os.path.split(lowerCAmelCase_  )[-1].split('''.'''  )[0]
        accelerator.init_trackers(lowerCAmelCase_   ,       lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained('''bert-base-cased'''  )
    SCREAMING_SNAKE_CASE__       							= load_dataset('''glue'''   ,       '''mrpc'''  )
    SCREAMING_SNAKE_CASE__       							= evaluate.load('''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'''  )

    # 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

    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_  )

    set_seed(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_  )

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

    # Potentially load in the weights and states from a previous save
    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  )
            SCREAMING_SNAKE_CASE__       							= os.path.basename(args.resume_from_checkpoint  )
        else:
            # Get the most recent checkpoint
            SCREAMING_SNAKE_CASE__       							= [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
            dirs.sort(key=os.path.getctime  )
            SCREAMING_SNAKE_CASE__       							= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        SCREAMING_SNAKE_CASE__       							= os.path.splitext(lowerCAmelCase_  )[0]

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

    # Now we train the model
    for epoch in range(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        model.train()
        if args.with_tracking:
            SCREAMING_SNAKE_CASE__       							= 0
        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
            SCREAMING_SNAKE_CASE__       							= 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
            SCREAMING_SNAKE_CASE__       							= 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  )
            SCREAMING_SNAKE_CASE__       							= model(**lowerCAmelCase_  )
            SCREAMING_SNAKE_CASE__       							= outputs.loss
            SCREAMING_SNAKE_CASE__       							= loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(lowerCAmelCase_  )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
                SCREAMING_SNAKE_CASE__       							= f'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        SCREAMING_SNAKE_CASE__       							= 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`.
            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_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_  )
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': eval_metric['''accuracy'''],
                    '''f1''': eval_metric['''f1'''],
                    '''train_loss''': total_loss.item() / len(lowerCAmelCase_  ),
                    '''epoch''': epoch,
                }   ,       step=lowerCAmelCase_   ,       )

        if checkpointing_steps == "epoch":
            SCREAMING_SNAKE_CASE__       							= f'''epoch_{epoch}'''
            if args.output_dir is not None:
                SCREAMING_SNAKE_CASE__       							= os.path.join(args.output_dir   ,       lowerCAmelCase_  )
            accelerator.save_state(lowerCAmelCase_  )

    if args.with_tracking:
        accelerator.end_training()


def        __snake_case       (						)							->	Union[str, 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.'''  )
    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(
        '''--resume_from_checkpoint'''   ,       type=lowerCAmelCase_   ,       default=lowerCAmelCase_   ,       help='''If the training should continue from a checkpoint folder.'''   ,       )
    parser.add_argument(
        '''--with_tracking'''   ,       action='''store_true'''   ,       help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''   ,       )
    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(
        '''--project_dir'''   ,       type=lowerCAmelCase_   ,       default='''logs'''   ,       help='''Location on where to store experiment tracking logs` and relevent project information'''   ,       )
    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()







import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

    if len(lowerCAmelCase_  ) > 1:
        logger.info(f'''averaging the weights of {ckpt_files}'''  )

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= hf_model.load_state_dict(lowerCAmelCase_   ,       strict=lowerCAmelCase_  )
    assert not missing, f'''missing keys: {missing}'''
    hf_model.save_pretrained(lowerCAmelCase_  )
    try:
        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(lowerCAmelCase_  )
        tok.save_pretrained(lowerCAmelCase_  )
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)
