






import argparse
import os

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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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   :   Optional[Any]      =    16
__UpperCamelCase   :   Any      =    32





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase = 16       ):




	'''simple docstring'''







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

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

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

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

	# Instantiate dataloaders.
	__lowercase   							=	DataLoader(
	    tokenized_datasets["""train"""]					,	shuffle=lowerCamelCase					,	collate_fn=lowerCamelCase					,	batch_size=lowerCamelCase       )
	__lowercase   							=	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   :   Optional[Any]      =    mocked_dataloaders  # noqa: F811





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







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

	__lowercase   							=	evaluate.load("""glue"""					,	"""mrpc"""       )

	# If the batch size is too big we use gradient accumulation
	__lowercase   							=	1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__lowercase   							=	batch_size // MAX_GPU_BATCH_SIZE
		__lowercase   							=	MAX_GPU_BATCH_SIZE

	set_seed(lowerCamelCase       )
	__lowercase							,    __lowercase   							=	get_dataloaders(lowerCamelCase					,	lowerCamelCase       )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__lowercase   							=	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).
	__lowercase   							=	model.to(accelerator.device       )

	# Instantiate optimizer
	__lowercase   							=	AdamW(params=model.parameters()					,	lr=lowerCamelCase       )

	# Instantiate scheduler
	__lowercase   							=	get_linear_schedule_with_warmup(
	    optimizer=lowerCamelCase					,	num_warmup_steps=100					,	num_training_steps=(len(lowerCamelCase       ) * num_epochs) // gradient_accumulation_steps					,	)

	# 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.
	__lowercase							,    __lowercase							,    __lowercase							,    __lowercase							,    __lowercase   							=	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       ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device       )
			__lowercase   							=	model(**lowerCamelCase       )
			__lowercase   							=	outputs.loss
			__lowercase   							=	loss / gradient_accumulation_steps
			accelerator.backward(lowerCamelCase       )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		__lowercase   							=	0
		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():
				__lowercase   							=	model(**lowerCamelCase       )
			__lowercase   							=	outputs.logits.argmax(dim=-1       )
			__lowercase							,    __lowercase   							=	accelerator.gather((predictions, batch["""labels"""])       )
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(lowerCamelCase       ) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					__lowercase   							=	predictions[: len(eval_dataloader.dataset       ) - samples_seen]
					__lowercase   							=	references[: len(eval_dataloader.dataset       ) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=lowerCamelCase					,	references=lowerCamelCase					,	)

		__lowercase   							=	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'					,	lowerCamelCase       )





def 					snake_case	(  ):




	'''simple docstring'''







	__lowercase   							=	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."""       )
	__lowercase   							=	parser.parse_args()
	__lowercase   							=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowerCamelCase					,	lowerCamelCase       )


if __name__ == "__main__":
						main()








class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
