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


__UpperCamelCase	: int    		=  16
__UpperCamelCase	: str    		=  32
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase = 16		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							AutoTokenizer.from_pretrained('''bert-base-cased'''		)
						SCREAMING_SNAKE_CASE							:					Any           =							DatasetDict(
						    {
						        '''train''': dataset['''train'''].select(_lowercase		),
						        '''validation''': dataset['''train'''].select(_lowercase		),
						        '''test''': dataset['''validation'''],
						    }		)

						def tokenize_function(_lowercase		):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE							:					int           =							tokenizer(examples['''sentence1''']	,						examples['''sentence2''']	,						truncation=_lowercase	,						max_length=_lowercase		)
												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							:					Union[str, Any]           =							datasets.map(
												    _lowercase	,						batched=_lowercase	,						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							:					Tuple           =							tokenized_datasets.rename_column('''label'''	,						'''labels'''		)

						def collate_fn(_lowercase		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE							:					Dict           =							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":
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE							:					str           =							8
												else:
																		SCREAMING_SNAKE_CASE							:					Any           =							None

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							DataLoader(
						    tokenized_datasets['''train''']	,						shuffle=_lowercase	,						collate_fn=_lowercase	,						batch_size=_lowercase		)
						SCREAMING_SNAKE_CASE							:					int           =							DataLoader(
						    tokenized_datasets['''validation''']	,						shuffle=_lowercase	,						collate_fn=_lowercase	,						batch_size=_lowercase		)

						SCREAMING_SNAKE_CASE							:					str           =							DataLoader(
						    tokenized_datasets['''test''']	,						shuffle=_lowercase	,						collate_fn=_lowercase	,						batch_size=_lowercase		)

						return train_dataloader, eval_dataloader, test_dataloader
def    A			(					_lowercase	,						_lowercase		):
						# New Code #
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[]
						# Download the dataset
						SCREAMING_SNAKE_CASE							:					Dict           =							load_dataset('''glue'''	,						'''mrpc'''		)
						# Create our splits
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							StratifiedKFold(n_splits=int(args.num_folds		)		)
						# Initialize accelerator
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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							:					Optional[Any]           =							config['''lr''']
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							int(config['''num_epochs''']		)
						SCREAMING_SNAKE_CASE							:					Dict           =							int(config['''seed''']		)
						SCREAMING_SNAKE_CASE							:					int           =							int(config['''batch_size''']		)

						SCREAMING_SNAKE_CASE							:					str           =							evaluate.load('''glue'''	,						'''mrpc'''		)

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

						set_seed(_lowercase		)

						# New Code #
						# Create our folds:
						SCREAMING_SNAKE_CASE							:					Dict           =							kfold.split(np.zeros(datasets['''train'''].num_rows		)	,						datasets['''train''']['''label''']		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							[]
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(_lowercase		):
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					int           =							get_fold_dataloaders(
												    _lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						)
												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												SCREAMING_SNAKE_CASE							:					Tuple           =							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,						return_dict=_lowercase		)

												# 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							:					List[Any]           =							model.to(accelerator.device		)

												# Instantiate optimizer
												SCREAMING_SNAKE_CASE							:					Any           =							AdamW(params=model.parameters()	,						lr=_lowercase		)

												# Instantiate scheduler
												SCREAMING_SNAKE_CASE							:					int           =							get_linear_schedule_with_warmup(
												    optimizer=_lowercase	,						num_warmup_steps=100	,						num_training_steps=(len(_lowercase		) * 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							:					Tuple           =							accelerator.prepare(
												    _lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase		)

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

																		model.eval()
																		for step, batch in enumerate(_lowercase		):
																								# 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							:					Union[str, Any]           =							model(**_lowercase		)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							outputs.logits.argmax(dim=-1		)
																								SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[Any]           =							accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
																								metric.add_batch(
																								    predictions=_lowercase	,						references=_lowercase	,						)

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

												# New Code #
												# We also run predictions on the test set at the very end
												SCREAMING_SNAKE_CASE							:					Dict           =							[]
												for step, batch in enumerate(_lowercase		):
																		# 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							:					Dict           =							model(**_lowercase		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							outputs.logits
																		SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							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(_lowercase	,						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							:					Union[str, Any]           =							torch.cat(_lowercase	,						dim=0		)
						SCREAMING_SNAKE_CASE							:					int           =							torch.stack(_lowercase	,						dim=0		).sum(dim=0		).div(int(args.num_folds		)		).argmax(dim=-1		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							metric.compute(predictions=_lowercase	,						references=_lowercase		)
						accelerator.print('''Average test metrics from all folds:'''	,						_lowercase		)
def    A			(					):
						SCREAMING_SNAKE_CASE							:					Dict           =							argparse.ArgumentParser(description='''Simple example of training script.'''		)
						parser.add_argument(
						    '''--mixed_precision'''	,						type=_lowercase	,						default=_lowercase	,						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=_lowercase	,						default=3	,						help='''The number of splits to perform across the dataset'''		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							parser.parse_args()
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(_lowercase	,						_lowercase		)


if __name__ == "__main__":
					main()






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





