






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


UpperCamelCase        =				16
UpperCamelCase        =				32




def 				A		(					lowercase__  :  Accelerator ,		lowercase__  :  int = 16  )		-> Dict:
	UpperCamelCase__ :Optional[Any]						=      AutoTokenizer.from_pretrained("""bert-base-cased"""  )
	UpperCamelCase__ :Any						=      load_dataset("""glue""" ,		"""mrpc"""  )

	def tokenize_function(lowercase__  :  str  ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCamelCase__ :Dict						=      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():
		UpperCamelCase__ :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
	UpperCamelCase__ :List[str]						=      tokenized_datasets.rename_column("""label""" ,		"""labels"""  )

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

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

	# Instantiate dataloaders.
	UpperCamelCase__ :Tuple						=      DataLoader(
	    tokenized_datasets["""train"""] ,		shuffle=lowercase__ ,		collate_fn=lowercase__ ,		batch_size=lowercase__  )
	UpperCamelCase__ :List[str]						=      DataLoader(
	    tokenized_datasets["""validation"""] ,		shuffle=lowercase__ ,		collate_fn=lowercase__ ,		batch_size=lowercase__  )

	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

							UpperCamelCase        =				mocked_dataloaders  # noqa: F811




def 				A		(					lowercase__  :  str ,		lowercase__  :  Optional[int]  )		-> Tuple:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		lowercase__  ) == "1":
		UpperCamelCase__ :List[str]						=      2
	# Initialize accelerator
	UpperCamelCase__ :Dict						=      Accelerator(cpu=args.cpu ,		mixed_precision=args.mixed_precision  )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCamelCase__ :Tuple						=      config["""lr"""]
	UpperCamelCase__ :List[str]						=      int(config["""num_epochs"""]  )
	UpperCamelCase__ :List[str]						=      int(config["""seed"""]  )
	UpperCamelCase__ :List[str]						=      int(config["""batch_size"""]  )

	UpperCamelCase__ :Any						=      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=lowercase__  )
	def inner_training_loop(lowercase__  :  Union[str, Any]  ):
		# 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(lowercase__  )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		UpperCamelCase__ :int						=      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).
		UpperCamelCase__ :List[Any]						=      model.to(accelerator.device  )

		# Instantiate optimizer
		UpperCamelCase__ :Any						=      AdamW(params=model.parameters() ,		lr=lowercase__  )
		UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      get_dataloaders(lowercase__ ,		lowercase__  )

		# Instantiate scheduler
		UpperCamelCase__ :List[str]						=      get_linear_schedule_with_warmup(
		    optimizer=lowercase__ ,		num_warmup_steps=100 ,		num_training_steps=(len(lowercase__  ) * 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.
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :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  )
				UpperCamelCase__ :Dict						=      model(**lowercase__  )
				UpperCamelCase__ :Optional[int]						=      outputs.loss
				accelerator.backward(lowercase__  )
				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():
					UpperCamelCase__ :List[Any]						=      model(**lowercase__  )
				UpperCamelCase__ :Optional[int]						=      outputs.logits.argmax(dim=-1  )
				UpperCamelCase__				,		UpperCamelCase__ :Union[str, Any]						=      accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
				metric.add_batch(
				    predictions=lowercase__ ,		references=lowercase__ ,		)

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

    # 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 				A		(					)		-> List[Any]:
	UpperCamelCase__ :str						=      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."""  )
	UpperCamelCase__ :Any						=      parser.parse_args()
	UpperCamelCase__ :List[Any]						=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowercase__ ,		lowercase__  )


if __name__ == "__main__":
							main()


from __future__ import annotations





def 	__a     (      A__			:  list[int | str]       ):
    create_state_space_tree(A__			,  []			,  0			,  [0 for i in range(len(A__       )       )]       )





def 	__a     (      A__			:  list[int | str]			,  A__			:  list[int | str]			,  A__			:  int			,  A__			:  list[int]			,  ):

    if index == len(A__       ):
        print(A__       )
        return

    for i in range(len(A__       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            SCREAMING_SNAKE_CASE										=   True
            create_state_space_tree(A__			,  A__			,  index + 1			,  A__       )
            current_sequence.pop()
            SCREAMING_SNAKE_CASE										=   False
__A			:					list[int | str]						=			[3, 1, 2, 4]
generate_all_permutations(sequence)

__A			:					list[int | str]						=			["A", "B", "C"]





generate_all_permutations(sequence_a)