
'''simple docstring'''





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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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 :       List[str]     					=    32






def  a_	( __lowercase     :			Any ,      __lowercase     :			List[str]				)				->						Tuple:
			# Initialize accelerator
			if args.with_tracking:
						_snake_case						=    Accelerator(
						    cpu=args.cpu ,      mixed_precision=args.mixed_precision ,      log_with='all' ,      project_dir=args.project_dir				)
			else:
						_snake_case						=    Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision				)

			if hasattr(args.checkpointing_steps ,      'isdigit'				):
						if args.checkpointing_steps == "epoch":
									_snake_case						=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									_snake_case						=    int(args.checkpointing_steps				)
						else:
									raise ValueError(
									    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
			else:
						_snake_case						=    None
			# 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']				)

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						_snake_case						=    os.path.split(__lowercase				)[-1].split('.'				)[0]
						accelerator.init_trackers(__lowercase ,      __lowercase				)

			_snake_case						=    AutoTokenizer.from_pretrained('bert-base-cased'				)
			_snake_case						=    load_dataset('glue' ,      'mrpc'				)
			_snake_case						=    evaluate.load('glue' ,      'mrpc'				)

			def tokenize_function(__lowercase     :			Optional[int]				):
						# max_length=None => use the model max length (it's actually the default)
						_snake_case						=    tokenizer(examples['sentence1'] ,      examples['sentence2'] ,      truncation=__lowercase ,      max_length=__lowercase				)
						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(
						    __lowercase ,      batched=__lowercase ,      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'				)

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

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

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

			# Instantiate dataloaders.
			_snake_case						=    DataLoader(
			    tokenized_datasets['train'] ,      shuffle=__lowercase ,      collate_fn=__lowercase ,      batch_size=__lowercase				)
			_snake_case						=    DataLoader(
			    tokenized_datasets['validation'] ,      shuffle=__lowercase ,      collate_fn=__lowercase ,      batch_size=__lowercase				)

			set_seed(__lowercase				)

			# 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=__lowercase				)

			# 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=__lowercase				)

			# Instantiate scheduler
			_snake_case						=    get_linear_schedule_with_warmup(
			    optimizer=__lowercase ,      num_warmup_steps=100 ,      num_training_steps=(len(__lowercase				) * 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.
			_snake_case			,      _snake_case			,      _snake_case			,      _snake_case			,      _snake_case						=    accelerator.prepare(
			    __lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase				)

			# We need to keep track of how many total steps we have iterated over
			_snake_case						=    0
			# We also need to keep track of the stating epoch so files are named properly
			_snake_case						=    0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''				)
									accelerator.load_state(args.resume_from_checkpoint				)
									_snake_case						=    os.path.basename(args.resume_from_checkpoint				)
						else:
									# Get the most recent checkpoint
									_snake_case						=    [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
									dirs.sort(key=os.path.getctime				)
									_snake_case						=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						_snake_case						=    os.path.splitext(__lowercase				)[0]

						if "epoch" in training_difference:
									_snake_case						=    int(training_difference.replace('epoch_' ,      ''				)				) + 1
									_snake_case						=    None
						else:
									_snake_case						=    int(training_difference.replace('step_' ,      ''				)				)
									_snake_case						=    resume_step // len(__lowercase				)
									resume_step -= starting_epoch * len(__lowercase				)

    # Now we train the model
			for epoch in range(__lowercase ,      __lowercase				):
						model.train()
						if args.with_tracking:
									_snake_case						=    0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									_snake_case						=    accelerator.skip_first_batches(__lowercase ,      __lowercase				)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									_snake_case						=    train_dataloader
						for step, batch in enumerate(__lowercase				):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device				)
									_snake_case						=    model(**__lowercase				)
									_snake_case						=    outputs.loss
									_snake_case						=    loss / gradient_accumulation_steps
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(__lowercase				)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(__lowercase ,      __lowercase				):
												_snake_case						=    f'''step_{overall_step}'''
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		_snake_case						=    os.path.join(args.output_dir ,      __lowercase				)
															accelerator.save_state(__lowercase				)

						model.eval()
						for step, batch in enumerate(__lowercase				):
									# 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(**__lowercase				)
									_snake_case						=    outputs.logits.argmax(dim=-1				)
									_snake_case			,      _snake_case						=    accelerator.gather_for_metrics((predictions, batch['labels'])				)
									metric.add_batch(
									    predictions=__lowercase ,      references=__lowercase ,      )

						_snake_case						=    metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:''' ,      __lowercase				)
						if args.with_tracking:
									accelerator.log(
									    {
									        'accuracy': eval_metric['accuracy'],
									        'f1': eval_metric['f1'],
									        'train_loss': total_loss.item() / len(__lowercase				),
									        'epoch': epoch,
									    } ,      step=__lowercase ,      )

						if checkpointing_steps == "epoch":
									_snake_case						=    f'''epoch_{epoch}'''
									if args.output_dir is not None:
												_snake_case						=    os.path.join(args.output_dir ,      __lowercase				)
									accelerator.save_state(__lowercase				)

			if args.with_tracking:
						accelerator.end_training()






def  a_	( )				->						List[str]:
			_snake_case						=    argparse.ArgumentParser(description='Simple example of training script.'				)
			parser.add_argument(
			    '--mixed_precision' ,      type=__lowercase ,      default=__lowercase ,      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(
			    '--checkpointing_steps' ,      type=__lowercase ,      default=__lowercase ,      help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' ,      )
			parser.add_argument(
			    '--resume_from_checkpoint' ,      type=__lowercase ,      default=__lowercase ,      help='If the training should continue from a checkpoint folder.' ,      )
			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(
			    '--output_dir' ,      type=__lowercase ,      default='.' ,      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,      )
			parser.add_argument(
			    '--project_dir' ,      type=__lowercase ,      default='logs' ,      help='Location on where to store experiment tracking logs` and relevent project information' ,      )
			_snake_case						=    parser.parse_args()
			_snake_case						=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__lowercase ,      __lowercase				)


if __name__ == "__main__":
			main()

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)