


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      logging.get_logger(__name__)





class        __A       (__lowercase):





							'''simple docstring'''

							__lowercase:						Any							 =      """timm_backbone"""



							def __init__(			self							: Dict						,    UpperCAmelCase_							: List[Any]=None						,    UpperCAmelCase_							: int=3						,    UpperCAmelCase_							: Dict=True						,    UpperCAmelCase_							: Optional[int]=True						,    UpperCAmelCase_							: int=None						,    **UpperCAmelCase_							: Union[str, Any]						,    )   ->Tuple:



														"""simple docstring"""



														super().__init__(**_a    )
														snake_case_          =						backbone
														snake_case_          =						num_channels
														snake_case_          =						features_only
														snake_case_          =						use_pretrained_backbone
														snake_case_          =						True
														snake_case_          =						out_indices if out_indices is not None else (-1,)







"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__SCREAMING_SNAKE_CASE							:      Tuple						=      16
__SCREAMING_SNAKE_CASE							:      int						=      32







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 16			,	_SCREAMING_SNAKE_CASE = "bert-base-cased"			)      ->			Optional[Any]:
							snake_case_          =						AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						load_dataset("""glue"""			,	"""mrpc"""			)

							def tokenize_function(_SCREAMING_SNAKE_CASE			):
														# max_length=None => use the model max length (it's actually the default)
														snake_case_          =						tokenizer(examples["""sentence1"""]			,	examples["""sentence2"""]			,	truncation=_SCREAMING_SNAKE_CASE			,	max_length=_SCREAMING_SNAKE_CASE			)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							snake_case_          =						datasets.map(
							    _SCREAMING_SNAKE_CASE			,	batched=_SCREAMING_SNAKE_CASE			,	remove_columns=["""idx""", """sentence1""", """sentence2"""]			,	load_from_cache_file=_SCREAMING_SNAKE_CASE			)

							# 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"""			)

							def collate_fn(_SCREAMING_SNAKE_CASE			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(_SCREAMING_SNAKE_CASE			,	padding="""max_length"""			,	max_length=128			,	return_tensors="""pt"""			)
														return tokenizer.pad(_SCREAMING_SNAKE_CASE			,	padding="""longest"""			,	return_tensors="""pt"""			)

							# Instantiate dataloaders.
							snake_case_          =						DataLoader(
							    tokenized_datasets["""train"""]			,	shuffle=_SCREAMING_SNAKE_CASE			,	collate_fn=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						DataLoader(
							    tokenized_datasets["""validation"""]			,	shuffle=_SCREAMING_SNAKE_CASE			,	collate_fn=_SCREAMING_SNAKE_CASE			,	batch_size=_SCREAMING_SNAKE_CASE			)

							return train_dataloader, eval_dataloader







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							# Initialize accelerator
							snake_case_          =						Accelerator()

							# 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"""]			)
							snake_case_          =						args.model_name_or_path

							set_seed(_SCREAMING_SNAKE_CASE			)
							snake_case_    ,  snake_case_          =						get_dataloaders(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							snake_case_          =						AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE			,	return_dict=_SCREAMING_SNAKE_CASE			)

							# Instantiate optimizer
							snake_case_          =						(
							    AdamW
							    if accelerator.state.deepspeed_plugin is None
							    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
							    else DummyOptim
							)
							snake_case_          =						optimizer_cls(params=model.parameters()			,	lr=_SCREAMING_SNAKE_CASE			)

							if accelerator.state.deepspeed_plugin is not None:
														snake_case_          =						accelerator.state.deepspeed_plugin.deepspeed_config[
														    """gradient_accumulation_steps"""
														]
							else:
														snake_case_          =						1
							snake_case_          =						(len(_SCREAMING_SNAKE_CASE			) * num_epochs) // gradient_accumulation_steps

							# Instantiate scheduler
							if (
							    accelerator.state.deepspeed_plugin is None
							    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
							):
														snake_case_          =						get_linear_schedule_with_warmup(
														    optimizer=_SCREAMING_SNAKE_CASE			,	num_warmup_steps=0			,	num_training_steps=_SCREAMING_SNAKE_CASE			,	)
							else:
														snake_case_          =						DummyScheduler(_SCREAMING_SNAKE_CASE			,	total_num_steps=_SCREAMING_SNAKE_CASE			,	warmup_num_steps=0			)

							# 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(
							    _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							# 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

							# Now we train the model
							snake_case_          =						evaluate.load("""glue"""			,	"""mrpc"""			)
							snake_case_          =						0
							snake_case_          =						{}
							for epoch in range(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			):
														model.train()
														for step, batch in enumerate(_SCREAMING_SNAKE_CASE			):
																					snake_case_          =						model(**_SCREAMING_SNAKE_CASE			)
																					snake_case_          =						outputs.loss
																					snake_case_          =						loss / gradient_accumulation_steps
																					accelerator.backward(_SCREAMING_SNAKE_CASE			)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

																					overall_step += 1

														model.eval()
														snake_case_          =						0
														for step, batch in enumerate(_SCREAMING_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_          =						model(**_SCREAMING_SNAKE_CASE			)
																					snake_case_          =						outputs.logits.argmax(dim=-1			)
																					# It is slightly faster to call this once, than multiple times
																					snake_case_    ,  snake_case_          =						accelerator.gather(
																					    (predictions, batch["""labels"""])			)  # If we are in a multiprocess environment, the last batch has duplicates
																					if accelerator.use_distributed:
																												if step == len(_SCREAMING_SNAKE_CASE			) - 1:
																																			snake_case_          =						predictions[: len(eval_dataloader.dataset			) - samples_seen]
																																			snake_case_          =						references[: len(eval_dataloader.dataset			) - samples_seen]
																												else:
																																			samples_seen += references.shape[0]
																					metric.add_batch(
																					    predictions=_SCREAMING_SNAKE_CASE			,	references=_SCREAMING_SNAKE_CASE			,	)

														snake_case_          =						metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""			,	_SCREAMING_SNAKE_CASE			)
														snake_case_          =						eval_metric["""accuracy"""]

														if best_performance < eval_metric["accuracy"]:
																					snake_case_          =						eval_metric["""accuracy"""]

							if args.performance_lower_bound is not None:
														assert (
														    args.performance_lower_bound <= best_performance
														), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

							accelerator.wait_for_everyone()
							if accelerator.is_main_process:
														with open(os.path.join(args.output_dir			,	"""all_results.json"""			)			,	"""w"""			) as f:
																					json.dump(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( )      ->			int:
							snake_case_          =						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""			)
							parser.add_argument(
							    """--model_name_or_path"""			,	type=_SCREAMING_SNAKE_CASE			,	default="""bert-base-cased"""			,	help="""Path to pretrained model or model identifier from huggingface.co/models."""			,	required=_SCREAMING_SNAKE_CASE			,	)
							parser.add_argument(
							    """--output_dir"""			,	type=_SCREAMING_SNAKE_CASE			,	default="""."""			,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,	)
							parser.add_argument(
							    """--performance_lower_bound"""			,	type=_SCREAMING_SNAKE_CASE			,	default=_SCREAMING_SNAKE_CASE			,	help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""			,	)
							parser.add_argument(
							    """--num_epochs"""			,	type=_SCREAMING_SNAKE_CASE			,	default=3			,	help="""Number of train epochs."""			,	)
							snake_case_          =						parser.parse_args()
							snake_case_          =						{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
							training_function(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				main()



