

"""simple docstring"""




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
#
########################################################################


A_							:			Union[str, Any]											= 16
A_							:			str											= 32

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = 16    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      AutoTokenizer.from_pretrained("""bert-base-cased"""    )
			SCREAMING_SNAKE_CASE__								=      DatasetDict(
			    {
			        """train""": dataset["""train"""].select(snake_case__    ),
			        """validation""": dataset["""train"""].select(snake_case__    ),
			        """test""": dataset["""validation"""],
			    }    )

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

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

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE__								=      DataLoader(
			    tokenized_datasets["""train"""]		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(
			    tokenized_datasets["""validation"""]		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__    )

			SCREAMING_SNAKE_CASE__								=      DataLoader(
			    tokenized_datasets["""test"""]		,				shuffle=snake_case__		,				collate_fn=snake_case__		,				batch_size=snake_case__    )

			return train_dataloader, eval_dataloader, test_dataloader

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			# Download the dataset
			SCREAMING_SNAKE_CASE__								=      load_dataset("""glue"""		,				"""mrpc"""    )
			# Create our splits
			SCREAMING_SNAKE_CASE__								=      StratifiedKFold(n_splits=int(args.num_folds    )    )
			# 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(snake_case__    )

			# New Code #
			# Create our folds:
			SCREAMING_SNAKE_CASE__								=      kfold.split(np.zeros(datasets["""train"""].num_rows    )		,				datasets["""train"""]["""label"""]    )
			SCREAMING_SNAKE_CASE__								=      []
			# Iterate over them
			for i, (train_idxs, valid_idxs) in enumerate(snake_case__    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      get_fold_dataloaders(
						    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				)
						# 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=snake_case__    )

						# 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=snake_case__    )

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE__								=      get_linear_schedule_with_warmup(
						    optimizer=snake_case__		,				num_warmup_steps=1_00		,				num_training_steps=(len(snake_case__    ) * 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(
						    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )

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

									model.eval()
									for step, batch in enumerate(snake_case__    ):
												# 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(**snake_case__    )
												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=snake_case__		,				references=snake_case__		,				)

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

						# New Code #
						# We also run predictions on the test set at the very end
						SCREAMING_SNAKE_CASE__								=      []
						for step, batch in enumerate(snake_case__    ):
									# 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(**snake_case__    )
									SCREAMING_SNAKE_CASE__								=      outputs.logits
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      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(snake_case__		,				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:
			SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			SCREAMING_SNAKE_CASE__								=      torch.stack(snake_case__		,				dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
			SCREAMING_SNAKE_CASE__								=      metric.compute(predictions=snake_case__		,				references=snake_case__    )
			accelerator.print("""Average test metrics from all folds:"""		,				snake_case__    )

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      argparse.ArgumentParser(description="""Simple example of training script."""    )
			parser.add_argument(
			    """--mixed_precision"""		,				type=snake_case__		,				default=snake_case__		,				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=snake_case__		,				default=3		,				help="""The number of splits to perform across the dataset"""    )
			SCREAMING_SNAKE_CASE__								=      parser.parse_args()
			SCREAMING_SNAKE_CASE__								=      {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(snake_case__		,				snake_case__    )


if __name__ == "__main__":
						main()





"""simple docstring"""




def 	A       (     snake_case__    ):




			'''simple docstring'''


			if not nums:  # Makes sure that the list is not empty
						raise ValueError("""List is empty"""    )

			SCREAMING_SNAKE_CASE__								=      sum(snake_case__    ) / len(snake_case__    )  # Calculate the average
			return sum(abs(x - average    ) for x in nums    ) / len(snake_case__    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()


