



'''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,
# 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[int]												=				16
UpperCamelCase__ :			Optional[Any]												=				32


def        lowerCAmelCase_     (			_lowerCamelCase:     Accelerator ,   _lowerCamelCase:     int = 16      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	AutoTokenizer.from_pretrained("""bert-base-cased"""      )
		__SCREAMING_SNAKE_CASE :	Tuple					 =	load_dataset("""glue""" ,   """mrpc"""      )

		def tokenize_function(_lowerCamelCase:     Union[str, Any]      ):
				# max_length=None => use the model max length (it's actually the default)
				__SCREAMING_SNAKE_CASE :	List[str]					 =	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():
				__SCREAMING_SNAKE_CASE :	Any					 =	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
		__SCREAMING_SNAKE_CASE :	Dict					 =	tokenized_datasets.rename_column("""label""" ,   """labels"""      )

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

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

		# Instantiate dataloaders.
		__SCREAMING_SNAKE_CASE :	List[str]					 =	DataLoader(
		    tokenized_datasets["""train"""] ,   shuffle=_lowerCamelCase ,   collate_fn=_lowerCamelCase ,   batch_size=_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	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__ :			str												=				mocked_dataloaders  # noqa: F811


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     List[Any]      ):
		# For testing only
		if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,   _lowerCamelCase      ) == "1":
				__SCREAMING_SNAKE_CASE :	List[str]					 =	2
		# Initialize accelerator
		__SCREAMING_SNAKE_CASE :	Any					 =	Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision      )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__SCREAMING_SNAKE_CASE :	int					 =	config["""lr"""]
		__SCREAMING_SNAKE_CASE :	List[str]					 =	int(config["""num_epochs"""]      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	int(config["""seed"""]      )
		__SCREAMING_SNAKE_CASE :	str					 =	int(config["""batch_size"""]      )

		__SCREAMING_SNAKE_CASE :	str					 =	evaluate.load("""glue""" ,   """mrpc"""      )

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

		set_seed(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	int					 =	get_dataloaders(_lowerCamelCase ,   _lowerCamelCase      )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__SCREAMING_SNAKE_CASE :	List[str]					 =	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).
		__SCREAMING_SNAKE_CASE :	List[str]					 =	model.to(accelerator.device      )

		# Instantiate optimizer
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	AdamW(params=model.parameters() ,   lr=_lowerCamelCase      )

		# Instantiate scheduler
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	get_linear_schedule_with_warmup(
		    optimizer=_lowerCamelCase ,   num_warmup_steps=1_00 ,   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.
		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	str					 =	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      )
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	model(**_lowerCamelCase      )
						__SCREAMING_SNAKE_CASE :	List[str]					 =	outputs.loss
						__SCREAMING_SNAKE_CASE :	List[str]					 =	loss / gradient_accumulation_steps
						accelerator.backward(_lowerCamelCase      )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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():
								__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(**_lowerCamelCase      )
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	outputs.logits.argmax(dim=-1      )
						__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[Any]					 =	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
										__SCREAMING_SNAKE_CASE :	List[Any]					 =	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
										__SCREAMING_SNAKE_CASE :	Any					 =	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 ,   )

				__SCREAMING_SNAKE_CASE :	Dict					 =	metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:" ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Tuple					 =	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."""      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	parser.parse_args()
		__SCREAMING_SNAKE_CASE :	Dict					 =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_lowerCamelCase ,   _lowerCamelCase      )


if __name__ == "__main__":
						main()




'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					warnings.warn(
					    """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use DeiTImageProcessor instead."""							,  lowerCAmelCase__							,  )
					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)