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 the experiment tracking capability,
# 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						=		16
_UpperCAmelCase						=		32




def 				UpperCamelCase						(       __lowercase						: Optional[int]						,__lowercase						: Tuple = 16	):
		'''simple docstring'''




		A_		:				Union[str, Any]     =      AutoTokenizer.from_pretrained('bert-base-cased'	)
		A_		:				str     =      load_dataset('glue'						,'mrpc'	)

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

		def collate_fn(__lowercase						: List[str]	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				A_		:				Tuple     =      1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						A_		:				List[str]     =      16
				elif accelerator.mixed_precision != "no":
						A_		:				List[Any]     =      8
				else:
						A_		:				Union[str, Any]     =      None

				return tokenizer.pad(
				    a_						,padding='longest'						,max_length=a_						,pad_to_multiple_of=a_						,return_tensors='pt'						,)

		# Instantiate dataloaders.
		A_		:				Union[str, Any]     =      DataLoader(
		    tokenized_datasets['train']						,shuffle=a_						,collate_fn=a_						,batch_size=a_	)
		A_		:				Optional[Any]     =      DataLoader(
		    tokenized_datasets['validation']						,shuffle=a_						,collate_fn=a_						,batch_size=a_	)

		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 				UpperCamelCase						(       __lowercase						: Optional[int]						,__lowercase						: Union[str, Any]	):
		'''simple docstring'''




		if os.environ.get('TESTING_MOCKED_DATALOADERS'						,a_	) == "1":
				A_		:				Optional[int]     =      2
		# Initialize Accelerator

		# New Code #
		# We pass in "all" to `log_with` to grab all available trackers in the environment
		# Note: If using a custom `Tracker` class, should be passed in here such as:
		# >>> log_with = ["all", MyCustomTrackerClassInstance()]
		if args.with_tracking:
				A_		:				Tuple     =      Accelerator(
				    cpu=args.cpu						,mixed_precision=args.mixed_precision						,log_with='all'						,project_dir=args.project_dir	)
		else:
				A_		:				str     =      Accelerator(cpu=args.cpu						,mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		A_		:				Any     =      config['lr']
		A_		:				str     =      int(config['num_epochs']	)
		A_		:				List[Any]     =      int(config['seed']	)
		A_		:				Tuple     =      int(config['batch_size']	)
		set_seed(a_	)

		A_   ,			A_		:				List[Any]     =      get_dataloaders(a_						,a_	)
		A_		:				Union[str, Any]     =      evaluate.load('glue'						,'mrpc'	)

		# If the batch size is too big we use gradient accumulation
		A_		:				Union[str, Any]     =      1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				A_		:				Union[str, Any]     =      batch_size // MAX_GPU_BATCH_SIZE
				A_		:				str     =      MAX_GPU_BATCH_SIZE

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A_		:				Optional[int]     =      AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,return_dict=a_	)

		# 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).
		A_		:				Optional[int]     =      model.to(accelerator.device	)

		# Instantiate optimizer
		A_		:				Any     =      AdamW(params=model.parameters()						,lr=a_	)

		# Instantiate scheduler
		A_		:				Tuple     =      get_linear_schedule_with_warmup(
		    optimizer=a_						,num_warmup_steps=1_00						,num_training_steps=(len(a_	) * 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.
		A_   ,			A_   ,			A_   ,			A_   ,			A_		:				str     =      accelerator.prepare(
		    a_						,a_						,a_						,a_						,a_	)

		# New Code #
		# We need to initialize the trackers we use. Overall configurations can also be stored
		if args.with_tracking:
				A_		:				Union[str, Any]     =      os.path.split(a_	)[-1].split('.'	)[0]
				accelerator.init_trackers(a_						,a_	)

		# Now we train the model
		for epoch in range(a_	):
				model.train()
				# New Code #
				# For our tracking example, we will log the total loss of each epoch
				if args.with_tracking:
						A_		:				Dict     =      0
				for step, batch in enumerate(a_	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						A_		:				Union[str, Any]     =      model(**a_	)
						A_		:				str     =      outputs.loss
						# New Code #
						if args.with_tracking:
								total_loss += loss.detach().float()
						A_		:				Any     =      loss / gradient_accumulation_steps
						accelerator.backward(a_	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

				A_		:				Optional[int]     =      metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''						,a_	)

				# New Code #
				# To actually log, we call `Accelerator.log`
				# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
				if args.with_tracking:
						accelerator.log(
						    {
						        'accuracy': eval_metric['accuracy'],
						        'f1': eval_metric['f1'],
						        'train_loss': total_loss.item() / len(a_	),
						        'epoch': epoch,
						    }						,step=a_						,)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
		if args.with_tracking:
				accelerator.end_training()




def 				UpperCamelCase						(       ):
		'''simple docstring'''




		A_		:				Tuple     =      argparse.ArgumentParser(description='Simple example of training script.'	)
		parser.add_argument(
		    '--mixed_precision'						,type=a_						,default=a_						,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.'	)
		parser.add_argument(
		    '--with_tracking'						,action='store_true'						,help='Whether to load in all available experiment trackers from the environment and use them for logging.'						,)
		parser.add_argument(
		    '--project_dir'						,type=a_						,default='logs'						,help='Location on where to store experiment tracking logs` and relevent project information'						,)
		A_		:				Optional[int]     =      parser.parse_args()
		A_		:				Any     =      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(a_						,a_	)


if __name__ == "__main__":
	main()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							:List[Any]							 =				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				['BartphoTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
