




'''simple docstring'''





import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict




@pytest.mark.parametrize(
    """split_dict"""      ,    [
        SplitDict(),
        SplitDict({"""train""": SplitInfo(name="""train"""      ,    num_bytes=1337      ,    num_examples=42      ,    dataset_name="""my_dataset"""       )}       ),
        SplitDict({"""train""": SplitInfo(name="""train"""      ,    num_bytes=1337      ,    num_examples=42       )}       ),
        SplitDict({"""train""": SplitInfo()}       ),
    ]      ,    )
def 							_UpperCAmelCase  (UpperCamelCase_ : SplitDict       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      split_dict._to_yaml_list()
				assert len(UpperCamelCase_       ) == len(UpperCamelCase_       )
				_lowerCAmelCase    :    Any										=      SplitDict._from_yaml_list(UpperCamelCase_       )
				for split_name, split_info in split_dict.items():
								# dataset_name field is deprecated, and is therefore not part of the YAML dump
								_lowerCAmelCase    :    str										=      None
								# the split name of split_dict takes over the name of the split info object
								_lowerCAmelCase    :    Optional[Any]										=      split_name
				assert split_dict == reloaded





@pytest.mark.parametrize(
    """split_info"""      ,    [SplitInfo(), SplitInfo(dataset_name=UpperCamelCase_       ), SplitInfo(dataset_name="""my_dataset"""       )]       )
def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      asdict(SplitDict({"""train""": split_info}       )       )
				assert "dataset_name" in split_dict_asdict["train"]
				assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  Tuple      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : DatasetDict      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : int = 16       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				_lowerCAmelCase    :    int										=      DatasetDict(
				    {
				        """train""": dataset["""train"""].select(UpperCamelCase_       ),
				        """validation""": dataset["""train"""].select(UpperCamelCase_       ),
				        """test""": dataset["""validation"""],
				    }       )

				def tokenize_function(UpperCamelCase_ : str       ):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCAmelCase    :    List[Any]										=      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():
								_lowerCAmelCase    :    List[str]										=      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
				_lowerCAmelCase    :    Optional[Any]										=      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.
								_lowerCAmelCase    :    List[str]										=      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":
												_lowerCAmelCase    :    Optional[Any]										=      16
								elif accelerator.mixed_precision != "no":
												_lowerCAmelCase    :    int										=      8
								else:
												_lowerCAmelCase    :    Dict										=      None

								return tokenizer.pad(
								    UpperCamelCase_      ,    padding="""longest"""      ,    max_length=UpperCamelCase_      ,    pad_to_multiple_of=UpperCamelCase_      ,    return_tensors="""pt"""      ,    )

				# Instantiate dataloaders.
				_lowerCAmelCase    :    Optional[Any]										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""test"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				return train_dataloader, eval_dataloader, test_dataloader




def 							_UpperCAmelCase  (UpperCamelCase_ : Any      ,    UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				# New Code #
				_lowerCAmelCase    :    Dict										=      []
				# Download the dataset
				_lowerCAmelCase    :    Optional[int]										=      load_dataset("""glue"""      ,    """mrpc"""       )
				# Create our splits
				_lowerCAmelCase    :    Any										=      StratifiedKFold(n_splits=int(args.num_folds       )       )
				# Initialize accelerator
				_lowerCAmelCase    :    int										=      Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_lowerCAmelCase    :    List[str]										=      config["""lr"""]
				_lowerCAmelCase    :    Any										=      int(config["""num_epochs"""]       )
				_lowerCAmelCase    :    Dict										=      int(config["""seed"""]       )
				_lowerCAmelCase    :    Union[str, Any]										=      int(config["""batch_size"""]       )

				_lowerCAmelCase    :    Optional[Any]										=      evaluate.load("""glue"""      ,    """mrpc"""       )

				# If the batch size is too big we use gradient accumulation
				_lowerCAmelCase    :    Optional[Any]										=      1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								_lowerCAmelCase    :    Optional[Any]										=      batch_size // MAX_GPU_BATCH_SIZE
								_lowerCAmelCase    :    int										=      MAX_GPU_BATCH_SIZE

				set_seed(UpperCamelCase_       )

				# New Code #
				# Create our folds:
				_lowerCAmelCase    :    Optional[int]										=      kfold.split(np.zeros(datasets["""train"""].num_rows       )      ,    datasets["""train"""]["""label"""]       )
				_lowerCAmelCase    :    Any										=      []
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase_       ):
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      get_fold_dataloaders(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    )
								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								_lowerCAmelCase    :    int										=      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).
								_lowerCAmelCase    :    Union[str, Any]										=      model.to(accelerator.device       )

								# Instantiate optimizer
								_lowerCAmelCase    :    Any										=      AdamW(params=model.parameters()      ,    lr=UpperCamelCase_       )

								# Instantiate scheduler
								_lowerCAmelCase    :    Tuple										=      get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    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.
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      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       )
																_lowerCAmelCase    :    Tuple										=      model(**UpperCamelCase_       )
																_lowerCAmelCase    :    Optional[int]										=      outputs.loss
																_lowerCAmelCase    :    Optional[int]										=      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():
																				_lowerCAmelCase    :    List[Any]										=      model(**UpperCamelCase_       )
																_lowerCAmelCase    :    List[Any]										=      outputs.logits.argmax(dim=-1       )
																_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
																metric.add_batch(
																    predictions=UpperCamelCase_      ,    references=UpperCamelCase_      ,    )

												_lowerCAmelCase    :    List[Any]										=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_       )

								# New Code #
								# We also run predictions on the test set at the very end
								_lowerCAmelCase    :    Dict										=      []
								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():
																_lowerCAmelCase    :    Dict										=      model(**UpperCamelCase_       )
												_lowerCAmelCase    :    List[Any]										=      outputs.logits
												_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
												fold_predictions.append(predictions.cpu()       )
												if i == 0:
																# We need all of the test predictions
																test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
								test_predictions.append(torch.cat(UpperCamelCase_      ,    dim=0       )       )
								# We now need to release all our memory and get rid of the current model, optimizer, etc
								accelerator.free_memory()
				# New Code #
				# Finally we check the accuracy of our folded results:
				_lowerCAmelCase    :    Tuple										=      torch.cat(UpperCamelCase_      ,    dim=0       )
				_lowerCAmelCase    :    Tuple										=      torch.stack(UpperCamelCase_      ,    dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
				_lowerCAmelCase    :    Union[str, Any]										=      metric.compute(predictions=UpperCamelCase_      ,    references=UpperCamelCase_       )
				accelerator.print("""Average test metrics from all folds:"""      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      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."""       )
				# New Code #
				parser.add_argument("""--num_folds"""      ,    type=UpperCamelCase_      ,    default=3      ,    help="""The number of splits to perform across the dataset"""       )
				_lowerCAmelCase    :    Optional[Any]										=      parser.parse_args()
				_lowerCAmelCase    :    List[Any]										=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(UpperCamelCase_      ,    UpperCamelCase_       )


if __name__ == "__main__":
	main()

