
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  			=					1_6
__a  			=					3_2




def 				a						(							snake_case__:					Accelerator						,   snake_case__:					DatasetDict						,   snake_case__:					List[int]						,   snake_case__:					List[int]						,   snake_case__:					int = 16					):


				'''simple docstring'''
				lowercase_       =					AutoTokenizer.from_pretrained('''bert-base-cased'''					)
				lowercase_       =					DatasetDict(
				    {
				        '''train''': dataset['''train'''].select(snake_case__					),
				        '''validation''': dataset['''train'''].select(snake_case__					),
				        '''test''': dataset['''validation'''],
				    }					)

				def tokenize_function(snake_case__:					Tuple					):
								# max_length=None => use the model max length (it's actually the default)
								lowercase_       =					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():
								lowercase_       =					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
				lowercase_       =					tokenized_datasets.rename_column('''label'''						,   '''labels'''					)

				def collate_fn(snake_case__:					int					):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase_       =					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":
												lowercase_       =					16
								elif accelerator.mixed_precision != "no":
												lowercase_       =					8
								else:
												lowercase_       =					None

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

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

				lowercase_       =					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__:					Optional[Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				# New Code #
				lowercase_       =					[]
				# Download the dataset
				lowercase_       =					load_dataset('''glue'''						,   '''mrpc'''					)
				# Create our splits
				lowercase_       =					StratifiedKFold(n_splits=int(args.num_folds					)					)
				# Initialize accelerator
				lowercase_       =					Accelerator(cpu=args.cpu						,   mixed_precision=args.mixed_precision					)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowercase_       =					config['''lr''']
				lowercase_       =					int(config['''num_epochs''']					)
				lowercase_       =					int(config['''seed''']					)
				lowercase_       =					int(config['''batch_size''']					)

				lowercase_       =					evaluate.load('''glue'''						,   '''mrpc'''					)

				# If the batch size is too big we use gradient accumulation
				lowercase_       =					1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowercase_       =					batch_size // MAX_GPU_BATCH_SIZE
								lowercase_       =					MAX_GPU_BATCH_SIZE

				set_seed(snake_case__					)

				# New Code #
				# Create our folds:
				lowercase_       =					kfold.split(np.zeros(datasets['''train'''].num_rows					)						,   datasets['''train''']['''label''']					)
				lowercase_       =					[]
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(snake_case__					):
								lowercase_  ,	lowercase_  ,	lowercase_       =					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)
								lowercase_       =					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).
								lowercase_       =					model.to(accelerator.device					)

								# Instantiate optimizer
								lowercase_       =					AdamW(params=model.parameters()						,   lr=snake_case__					)

								# Instantiate scheduler
								lowercase_       =					get_linear_schedule_with_warmup(
								    optimizer=snake_case__						,   num_warmup_steps=100						,   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.
								lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					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					)
																lowercase_       =					model(**snake_case__					)
																lowercase_       =					outputs.loss
																lowercase_       =					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():
																				lowercase_       =					model(**snake_case__					)
																lowercase_       =					outputs.logits.argmax(dim=-1					)
																lowercase_  ,	lowercase_       =					accelerator.gather_for_metrics((predictions, batch['''labels'''])					)
																metric.add_batch(
																    predictions=snake_case__						,   references=snake_case__						,   )

												lowercase_       =					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
								lowercase_       =					[]
								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():
																lowercase_       =					model(**snake_case__					)
												lowercase_       =					outputs.logits
												lowercase_  ,	lowercase_       =					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:
				lowercase_       =					torch.cat(snake_case__						,   dim=0					)
				lowercase_       =					torch.stack(snake_case__						,   dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
				lowercase_       =					metric.compute(predictions=snake_case__						,   references=snake_case__					)
				accelerator.print('''Average test metrics from all folds:'''						,   snake_case__					)




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					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'''					)
				lowercase_       =					parser.parse_args()
				lowercase_       =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(snake_case__						,   snake_case__					)


if __name__ == "__main__":
						main()






import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
						print(solution())




