"""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


_a   :	List[Any] 					=     16
_a   :	Union[str, Any] 					=     32





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: List[Any] = 16    ,_lowerCamelCase		: Optional[Any] = "bert-base-cased"			) ->   List[str]:
		_lowerCAmelCase		: Dict						    =  AutoTokenizer.from_pretrained(_lowerCamelCase			)
		_lowerCAmelCase		: Optional[int]						    =  load_dataset("""glue"""    ,"""mrpc"""			)

		def tokenize_function(_lowerCamelCase		: Union[str, Any]			):
				# max_length=None => use the model max length (it's actually the default)
				_lowerCAmelCase		: 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
		_lowerCAmelCase		: List[str]						    =  datasets.map(
		    _lowerCamelCase    ,batched=_lowerCamelCase    ,remove_columns=["""idx""", """sentence1""", """sentence2"""]    ,load_from_cache_file=_lowerCamelCase			)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		_lowerCAmelCase		: Optional[Any]						    =  tokenized_datasets.rename_column("""label"""    ,"""labels"""			)

		def collate_fn(_lowerCamelCase		: int			):
				# 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(_lowerCamelCase    ,padding="""max_length"""    ,max_length=128    ,return_tensors="""pt"""			)
				return tokenizer.pad(_lowerCamelCase    ,padding="""longest"""    ,return_tensors="""pt"""			)

		# Instantiate dataloaders.
		_lowerCAmelCase		: Any						    =  DataLoader(
		    tokenized_datasets["""train"""]    ,shuffle=_lowerCamelCase    ,collate_fn=_lowerCamelCase    ,batch_size=_lowerCamelCase			)
		_lowerCAmelCase		: int						    =  DataLoader(
		    tokenized_datasets["""validation"""]    ,shuffle=_lowerCamelCase    ,collate_fn=_lowerCamelCase    ,batch_size=_lowerCamelCase			)

		return train_dataloader, eval_dataloader





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str			) ->   List[Any]:
		# Initialize accelerator
		_lowerCAmelCase		: List[Any]						    =  Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_lowerCAmelCase		: Any						    =  config["""lr"""]
		_lowerCAmelCase		: Dict						    =  int(config["""num_epochs"""]			)
		_lowerCAmelCase		: List[Any]						    =  int(config["""seed"""]			)
		_lowerCAmelCase		: List[str]						    =  int(config["""batch_size"""]			)
		_lowerCAmelCase		: List[Any]						    =  args.model_name_or_path

		set_seed(_lowerCamelCase			)
		_lowerCAmelCase		: List[str]						    =  get_dataloaders(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_lowerCAmelCase		: Tuple						    =  AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase    ,return_dict=_lowerCamelCase			)

		# Instantiate optimizer
		_lowerCAmelCase		: Tuple						    =  (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		_lowerCAmelCase		: List[str]						    =  optimizer_cls(params=model.parameters()    ,lr=_lowerCamelCase			)

		if accelerator.state.deepspeed_plugin is not None:
				_lowerCAmelCase		: Any						    =  accelerator.state.deepspeed_plugin.deepspeed_config[
				    """gradient_accumulation_steps"""
				]
		else:
				_lowerCAmelCase		: Any						    =  1
		_lowerCAmelCase		: str						    =  (len(_lowerCamelCase			) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				_lowerCAmelCase		: List[Any]						    =  get_linear_schedule_with_warmup(
				    optimizer=_lowerCamelCase    ,num_warmup_steps=0    ,num_training_steps=_lowerCamelCase    ,)
		else:
				_lowerCAmelCase		: Dict						    =  DummyScheduler(_lowerCamelCase    ,total_num_steps=_lowerCamelCase    ,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.
		_lowerCAmelCase		: Dict						    =  accelerator.prepare(
		    _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)

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

		# Now we train the model
		_lowerCAmelCase		: str						    =  evaluate.load("""glue"""    ,"""mrpc"""			)
		_lowerCAmelCase		: int						    =  0
		_lowerCAmelCase		: Tuple						    =  {}
		for epoch in range(_lowerCamelCase    ,_lowerCamelCase			):
				model.train()
				for step, batch in enumerate(_lowerCamelCase			):
						_lowerCAmelCase		: List[str]						    =  model(**_lowerCamelCase			)
						_lowerCAmelCase		: int						    =  outputs.loss
						_lowerCAmelCase		: List[Any]						    =  loss / gradient_accumulation_steps
						accelerator.backward(_lowerCamelCase			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

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

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

				if best_performance < eval_metric["accuracy"]:
						_lowerCAmelCase		: Dict						    =  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(_lowerCamelCase    ,_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   Union[str, Any]:
		_lowerCAmelCase		: int						    =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""			)
		parser.add_argument(
		    """--model_name_or_path"""    ,type=_lowerCamelCase    ,default="""bert-base-cased"""    ,help="""Path to pretrained model or model identifier from huggingface.co/models."""    ,required=_lowerCamelCase    ,)
		parser.add_argument(
		    """--output_dir"""    ,type=_lowerCamelCase    ,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=_lowerCamelCase    ,default=_lowerCamelCase    ,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=_lowerCamelCase    ,default=3    ,help="""Number of train epochs."""    ,)
		_lowerCAmelCase		: Optional[int]						    =  parser.parse_args()
		_lowerCAmelCase		: Any						    =  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
		training_function(_lowerCamelCase    ,_lowerCamelCase			)


if __name__ == "__main__":
					main()





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




