
"""simple docstring"""



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


_snake_case          =						1_6
_snake_case          =						3_2






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Accelerator     ,  SCREAMING_SNAKE_CASE:					int = 16						):






							"""simple docstring"""



							_lowerCAmelCase  	=			AutoTokenizer.from_pretrained('bert-base-cased'						)
							_lowerCAmelCase  	=			load_dataset('glue'     ,  'mrpc'						)

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

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

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

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

							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

							_snake_case          =						mocked_dataloaders  # noqa: F811






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  SCREAMING_SNAKE_CASE						) == "1":
														_lowerCAmelCase  	=			2
							# Initialize accelerator
							_lowerCAmelCase  	=			Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision						)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowerCAmelCase  	=			config['lr']
							_lowerCAmelCase  	=			int(config['num_epochs']						)
							_lowerCAmelCase  	=			int(config['seed']						)
							_lowerCAmelCase  	=			int(config['batch_size']						)

							_lowerCAmelCase  	=			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=SCREAMING_SNAKE_CASE						)
							def inner_training_loop(SCREAMING_SNAKE_CASE:					Dict						):
														# 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(SCREAMING_SNAKE_CASE						)

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_lowerCAmelCase  	=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,  return_dict=SCREAMING_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).
														_lowerCAmelCase  	=			model.to(accelerator.device						)

														# Instantiate optimizer
														_lowerCAmelCase  	=			AdamW(params=model.parameters()     ,  lr=SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase	,		_lowerCAmelCase  	=			get_dataloaders(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

														# Instantiate scheduler
														_lowerCAmelCase  	=			get_linear_schedule_with_warmup(
														    optimizer=SCREAMING_SNAKE_CASE     ,  num_warmup_steps=100     ,  num_training_steps=(len(SCREAMING_SNAKE_CASE						) * 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.
														_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			accelerator.prepare(
														    SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

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

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

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

    # 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 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser(description='Simple example of training script.'						)
							parser.add_argument(
							    '--mixed_precision'     ,  type=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_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.'						)
							_lowerCAmelCase  	=			parser.parse_args()
							_lowerCAmelCase  	=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
							main()








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							if len(SCREAMING_SNAKE_CASE						) == 1:
														return [nums.copy()]
							for _ in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			nums.pop(0						)
														_lowerCAmelCase  	=			permute(SCREAMING_SNAKE_CASE						)
														for perm in permutations:
																					perm.append(SCREAMING_SNAKE_CASE						)
														result.extend(SCREAMING_SNAKE_CASE						)
														nums.append(SCREAMING_SNAKE_CASE						)
							return result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



							def backtrack(SCREAMING_SNAKE_CASE:					Tuple						):
														if start == len(SCREAMING_SNAKE_CASE						) - 1:
																					output.append(nums[:]						)
														else:
																					for i in range(SCREAMING_SNAKE_CASE     ,  len(SCREAMING_SNAKE_CASE						)						):
																												_lowerCAmelCase	,		_lowerCAmelCase  	=			nums[i], nums[start]
																												backtrack(start + 1						)
																												_lowerCAmelCase	,		_lowerCAmelCase  	=			nums[i], nums[start]  # backtrack

							_lowerCAmelCase  	=			[]
							backtrack(0						)
							return output


if __name__ == "__main__":
	import doctest

	# use res to print the data in permute2 function
	_snake_case          =						permutea([1, 2, 3])
	print(res)
	doctest.testmod()






