



'''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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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											=			32


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  Any = 16 ):

			"""simple docstring"""





			__UpperCAmelCase   :      int										=     AutoTokenizer.from_pretrained("""bert-base-cased""" )
			__UpperCAmelCase   :      Optional[Any]										=     load_dataset("""glue"""			,							"""mrpc""" )

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

			def collate_fn(lowerCAmelCase__	:  Dict ):
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__UpperCAmelCase   :      Dict										=     16
						elif accelerator.mixed_precision != "no":
									__UpperCAmelCase   :      str										=     8
						else:
									__UpperCAmelCase   :      int										=     None

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

			# Instantiate dataloaders.
			__UpperCAmelCase   :      Any										=     DataLoader(
			    tokenized_datasets["""train"""]			,							shuffle=lowerCAmelCase__			,							collate_fn=lowerCAmelCase__			,							batch_size=lowerCAmelCase__ )
			__UpperCAmelCase   :      Tuple										=     DataLoader(
			    tokenized_datasets["""validation"""]			,							shuffle=lowerCAmelCase__			,							collate_fn=lowerCAmelCase__			,							batch_size=lowerCAmelCase__ )

			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   lowercase_	(   lowerCAmelCase__	:  Optional[Any]			,							lowerCAmelCase__	:  Dict ):

			"""simple docstring"""





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

			__UpperCAmelCase   :      Optional[int]										=     evaluate.load("""glue"""			,							"""mrpc""" )

			# New Code #
			# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
			# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
			# half each time. From this, we can calculate the number of gradient accumulation steps needed
			# and modify the Accelerator object as a result
			@find_executable_batch_size(starting_batch_size=int(lowerCAmelCase__ ) )
			def inner_training_loop(lowerCAmelCase__	:  Dict ):
						# Since we need to modify the outside accelerator object, we need to bring it
						# to the local scope
						nonlocal accelerator

						# We can calculate the number of gradient accumulation steps based on the current
						# batch size vs the starting batch size
						__UpperCAmelCase   :      Optional[Any]										=     observed_batch_size // batch_size

						# And then set it in the Accelerator directly:
						__UpperCAmelCase   :      List[str]										=     num_gradient_accumulation_steps

						# Next we need to free all of the stored model references in the Accelerator each time
						accelerator.free_memory()

						# And set the seed so our results are reproducable each reset
						set_seed(lowerCAmelCase__ )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCAmelCase   :      str										=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,							return_dict=lowerCAmelCase__ )

						# 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   :      str										=     AdamW(params=model.parameters()			,							lr=lowerCAmelCase__ )
						__UpperCAmelCase   :      List[str]										=     get_dataloaders(lowerCAmelCase__			,							lowerCAmelCase__ )

						# Instantiate scheduler
						__UpperCAmelCase   :      List[Any]										=     get_linear_schedule_with_warmup(
						    optimizer=lowerCAmelCase__			,							num_warmup_steps=100			,							num_training_steps=(len(lowerCAmelCase__ ) * 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   :      Any										=     accelerator.prepare(
						    lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__			,							lowerCAmelCase__ )

						# Now we train the model
						for epoch in range(lowerCAmelCase__ ):
									model.train()
									for step, batch in enumerate(lowerCAmelCase__ ):
												# And perform gradient accumulation
												with accelerator.accumulate(lowerCAmelCase__ ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device )
															__UpperCAmelCase   :      Any										=     model(**lowerCAmelCase__ )
															__UpperCAmelCase   :      List[Any]										=     outputs.loss
															accelerator.backward(lowerCAmelCase__ )
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

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

									__UpperCAmelCase   :      int										=     metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f'epoch {epoch}:'			,							lowerCAmelCase__ )

    # 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   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     argparse.ArgumentParser(description="""Simple example of training script.""" )
			parser.add_argument(
			    """--mixed_precision"""			,							type=lowerCAmelCase__			,							default=lowerCAmelCase__			,							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   :      int										=     parser.parse_args()
			# New Code #
			# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
			__UpperCAmelCase   :      Dict										=     {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
			training_function(lowerCAmelCase__			,							lowerCAmelCase__ )


if __name__ == "__main__":
					main()









'''simple docstring'''
from itertools import permutations


def   lowercase_	(   lowerCAmelCase__	:  int = 5 ):

			"""simple docstring"""





			if gon_side < 3 or gon_side > 5:
						raise ValueError("""gon_side must be in the range [3, 5]""" )

			# Since it's 16, we know 10 is on the outer ring
			# Put the big numbers at the end so that they are never the first number
			__UpperCAmelCase   :      int										=     list(range(gon_side + 1			,							0			,							-1 ) )
			__UpperCAmelCase   :      int										=     list(range(gon_side + 2			,							gon_side * 2 + 1 ) )

			for perm in permutations(small_numbers + big_numbers ):
						__UpperCAmelCase   :      Optional[int]										=     generate_gon_ring(lowerCAmelCase__			,							list(lowerCAmelCase__ ) )
						if is_magic_gon(lowerCAmelCase__ ):
									return int("""""".join(str(lowerCAmelCase__ ) for n in numbers ) )

			__UpperCAmelCase   :      Optional[int]										=     f'Magic {gon_side}-gon ring is impossible'
			raise ValueError(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     [0] * (gon_side * 3)
			__UpperCAmelCase   :      Optional[Any]										=     perm[0:3]
			perm.append(perm[1] )

			__UpperCAmelCase   :      Union[str, Any]										=     1 if gon_side < 5 else 2

			for i in range(1			,							len(lowerCAmelCase__ ) // 3 + magic_number ):
						__UpperCAmelCase   :      List[str]										=     perm[2 * i + 1]
						__UpperCAmelCase   :      Optional[int]										=     result[3 * i - 1]
						__UpperCAmelCase   :      Optional[int]										=     perm[2 * i + 2]

			return result


def   lowercase_	(   lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			if len(lowerCAmelCase__ ) % 3 != 0:
						raise ValueError("""a gon ring should have a length that is a multiple of 3""" )

			if min(numbers[::3] ) != numbers[0]:
						return False

			__UpperCAmelCase   :      Optional[int]										=     sum(numbers[:3] )

			return all(sum(numbers[i : i + 3] ) == total for i in range(3			,							len(lowerCAmelCase__ )			,							3 ) )


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




