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

_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       32






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_ = 16  ):



			"""simple docstring"""





			_snake_case              =      AutoTokenizer.from_pretrained("bert-base-cased"  )
			_snake_case              =      load_dataset("glue"							,	"mrpc"  )

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

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

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

			# Instantiate dataloaders.
			_snake_case              =      DataLoader(
			    tokenized_datasets["train"]							,	shuffle=lowerCamelCase_							,	collate_fn=lowerCamelCase_							,	batch_size=lowerCamelCase_  )
			_snake_case              =      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

			_SCREAMING_SNAKE_CASE:						Optional[Any]    	=       mocked_dataloaders  # noqa: F811






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





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

			_snake_case              =      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_  ):
						# 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
						_snake_case              =      observed_batch_size // batch_size

						# And then set it in the Accelerator directly:
						_snake_case              =      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)
						_snake_case              =      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).
						_snake_case              =      model.to(accelerator.device  )

						# Instantiate optimizer
						_snake_case              =      AdamW(params=model.parameters()							,	lr=lowerCamelCase_  )
						_snake_case       , _snake_case              =      get_dataloaders(lowerCamelCase_							,	lowerCamelCase_  )

						# Instantiate scheduler
						_snake_case              =      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.
						_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case              =      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  )
															_snake_case              =      model(**lowerCamelCase_  )
															_snake_case              =      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():
															_snake_case              =      model(**lowerCamelCase_  )
												_snake_case              =      outputs.logits.argmax(dim=-1  )
												_snake_case       , _snake_case              =      accelerator.gather_for_metrics((predictions, batch["labels"])  )
												metric.add_batch(
												    predictions=lowerCamelCase_							,	references=lowerCamelCase_							,	)

									_snake_case              =      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    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      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."  )
			_snake_case              =      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
			_snake_case              =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
			training_function(lowerCamelCase_							,	lowerCamelCase_  )


if __name__ == "__main__":
			main()


from math import gcd






def    UpperCAmelCase__(						lowerCamelCase_ = 12000  ):



			"""simple docstring"""





			_snake_case              =      0
			for d in range(max_d + 1  ):
						for n in range(d // 3 + 1							,	(d + 1) // 2  ):
									if gcd(lowerCamelCase_							,	lowerCamelCase_  ) == 1:
												fractions_number += 1
			return fractions_number


if __name__ == "__main__":
			print(F"{solution() = }")

