






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


__lowerCamelCase :      Optional[Any]										=				16
__lowerCamelCase :      Optional[Any]										=				32







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Accelerator					,  snake_case_ :							int = 16       ):
	snake_case__			:				Optional[Any]       =  AutoTokenizer.from_pretrained("bert-base-cased"       )
	snake_case__			:				List[Any]       =  load_dataset("glue"					,  "mrpc"       )

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

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

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

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

	__lowerCamelCase :      Optional[int]										=				mocked_dataloaders  # noqa: F811







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str					,  snake_case_ :							str       ):
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"					,  snake_case_       ) == "1":
		snake_case__			:				Union[str, Any]       =  2
	# Initialize accelerator
	snake_case__			:				int       =  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__			:				Optional[int]       =  config["lr"]
	snake_case__			:				Tuple       =  int(config["num_epochs"]       )
	snake_case__			:				Optional[int]       =  int(config["seed"]       )
	snake_case__			:				Union[str, Any]       =  int(config["batch_size"]       )

	snake_case__			:				List[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=snake_case_       )
	def inner_training_loop(snake_case_ :							List[str]       ):
		# 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(snake_case_       )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		snake_case__			:				Tuple       =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,  return_dict=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).
		snake_case__			:				Optional[int]       =  model.to(accelerator.device       )

		# Instantiate optimizer
		snake_case__			:				Tuple       =  AdamW(params=model.parameters()					,  lr=snake_case_       )
		snake_case__,						snake_case__			:				Dict       =  get_dataloaders(snake_case_					,  snake_case_       )

		# Instantiate scheduler
		snake_case__			:				str       =  get_linear_schedule_with_warmup(
		    optimizer=snake_case_					,  num_warmup_steps=100					,  num_training_steps=(len(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.
		snake_case__,						snake_case__,						snake_case__,						snake_case__,						snake_case__			:				str       =  accelerator.prepare(
		    snake_case_					,  snake_case_					,  snake_case_					,  snake_case_					,  snake_case_       )

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

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

			snake_case__			:				Optional[Any]       =  metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F'''epoch {epoch}:'''					,  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 					SCREAMING_SNAKE_CASE    (			):
	snake_case__			:				str       =  argparse.ArgumentParser(description="Simple example of training script."       )
	parser.add_argument(
	    "--mixed_precision"					,  type=snake_case_					,  default=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."       )
	snake_case__			:				List[Any]       =  parser.parse_args()
	snake_case__			:				Any       =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(snake_case_					,  snake_case_       )


if __name__ == "__main__":
	main()











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



