
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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     :	List[str]    = 1_6
A     :	str    = 3_2


def     __lowerCAmelCase			(			a__						,    a__ = 16  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained('''bert-base-cased'''  )
				__a										=    load_dataset('''glue'''						,    '''mrpc'''  )

				def tokenize_function(a__  ):
								# max_length=None => use the model max length (it's actually the default)
								__a										=    tokenizer(examples['''sentence1''']						,    examples['''sentence2''']						,    truncation=a__						,    max_length=a__  )
								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(
								    a__						,    batched=a__						,    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(a__  ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__a										=    128 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										=    16
								elif accelerator.mixed_precision != "no":
												__a										=    8
								else:
												__a										=    None

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

				# Instantiate dataloaders.
				__a										=    DataLoader(
				    tokenized_datasets['''train''']						,    shuffle=a__						,    collate_fn=a__						,    batch_size=a__  )
				__a										=    DataLoader(
				    tokenized_datasets['''validation''']						,    shuffle=a__						,    collate_fn=a__						,    batch_size=a__  )

				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     :	Union[str, Any]    = mocked_dataloaders  # noqa: F811


def     __lowerCAmelCase			(			a__						,    a__  )       ->  List[str]:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,    a__  ) == "1":
								__a										=    2
				# 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'''  )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=a__  )
				def inner_training_loop(a__  ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(a__  )

								# 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=a__  )

								# 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=a__  )
								__a							,     __a										=    get_dataloaders(a__						,    a__  )

								# Instantiate scheduler
								__a										=    get_linear_schedule_with_warmup(
								    optimizer=a__						,    num_warmup_steps=100						,    num_training_steps=(len(a__  ) * num_epochs)						,    )

								# 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(
								    a__						,    a__						,    a__						,    a__						,    a__  )

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

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
				inner_training_loop()


def     __lowerCAmelCase			(			)       ->  Dict:
				__a										=    argparse.ArgumentParser(description='''Simple example of training script.'''  )
				parser.add_argument(
				    '''--mixed_precision'''						,    type=a__						,    default=a__						,    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''': 42, '''batch_size''': 16}
				training_function(a__						,    a__  )


if __name__ == "__main__":
					main()

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


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