
"""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


_lowercase							=				16
_lowercase							=				32



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__ = 1_6			,   __magic_name__ = "bert-base-cased"      )						->Dict:
			__lowercase       =       AutoTokenizer.from_pretrained(_lowercase      )
			__lowercase       =       load_dataset("glue"			,   "mrpc"      )

			def tokenize_function(__magic_name__      ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase       =       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
			__lowercase       =       datasets.map(
			    _lowercase			,   batched=_lowercase			,   remove_columns=["idx", "sentence1", "sentence2"]			,   load_from_cache_file=_lowercase      )

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

			def collate_fn(__magic_name__      ):
						# 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(_lowercase			,   padding="max_length"			,   max_length=1_2_8			,   return_tensors="pt"      )
						return tokenizer.pad(_lowercase			,   padding="longest"			,   return_tensors="pt"      )

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

			return train_dataloader, eval_dataloader



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

			__lowercase       =       metric.compute()
			return eval_metric["accuracy"]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Dict:
			__lowercase       =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__lowercase       =       config['lr']
			__lowercase       =       int(config["num_epochs"]      )
			__lowercase       =       int(config["seed"]      )
			__lowercase       =       int(config["batch_size"]      )
			__lowercase       =       args.model_name_or_path

			set_seed(_lowercase      )
			__lowercase       =       get_dataloaders(_lowercase			,   _lowercase			,   _lowercase      )

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

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

			if accelerator.state.deepspeed_plugin is not None:
						__lowercase       =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    'gradient_accumulation_steps'
						]
			else:
						__lowercase       =       1
			__lowercase       =       (len(_lowercase      ) * num_epochs) // gradient_accumulation_steps

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

			# We need to keep track of how many total steps we have iterated over
			__lowercase       =       0
			# We also need to keep track of the stating epoch so files are named properly
			__lowercase       =       0
			__lowercase       =       evaluate.load("glue"			,   "mrpc"      )
			__lowercase       =       num_epochs

			if args.partial_train_epoch is not None:
						__lowercase       =       args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint      )
						__lowercase       =       args.resume_from_checkpoint.split("epoch_"      )[1]
						__lowercase       =       ''
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						__lowercase       =       int(_lowercase      ) + 1
						__lowercase       =       evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase      )
						accelerator.print("resumed checkpoint performance:"			,   _lowercase      )
						accelerator.print("resumed checkpoint\'s scheduler\'s lr:"			,   lr_scheduler.get_lr()[0]      )
						accelerator.print("resumed optimizers\'s lr:"			,   optimizer.param_groups[0]["lr"]      )
						with open(os.path.join(args.output_dir			,   F'''state_{starting_epoch-1}.json'''      )			,   "r"      ) as f:
									__lowercase       =       json.load(_lowercase      )
									assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["lr"] == lr_scheduler.get_lr()[0]
									), "Scheduler learning rate mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
									), "Optimizer learning rate mismatch, loading from checkpoint failed"
									assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
									return

    # Now we train the model
			__lowercase       =       {}
			for epoch in range(_lowercase			,   _lowercase      ):
						model.train()
						for step, batch in enumerate(_lowercase      ):
									__lowercase       =       model(**_lowercase      )
									__lowercase       =       outputs.loss
									__lowercase       =       loss / gradient_accumulation_steps
									accelerator.backward(_lowercase      )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1
						__lowercase       =       F'''epoch_{epoch}'''
						__lowercase       =       os.path.join(args.output_dir			,   _lowercase      )
						accelerator.save_state(_lowercase      )
						__lowercase       =       evaluation_loop(_lowercase			,   _lowercase			,   _lowercase			,   _lowercase      )
						__lowercase       =       accuracy
						__lowercase       =       lr_scheduler.get_lr()[0]
						__lowercase       =       optimizer.param_groups[0]['lr']
						__lowercase       =       epoch
						__lowercase       =       overall_step
						accelerator.print(F'''epoch {epoch}:'''			,   _lowercase      )

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									with open(os.path.join(args.output_dir			,   F'''state_{epoch}.json'''      )			,   "w"      ) as f:
												json.dump(_lowercase			,   _lowercase      )



def      lowerCAmelCase__			(		)						->str:
			__lowercase       =       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
			parser.add_argument(
			    "--model_name_or_path"			,   type=_lowercase			,   default="bert-base-cased"			,   help="Path to pretrained model or model identifier from huggingface.co/models."			,   required=_lowercase			,   )
			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(
			    "--resume_from_checkpoint"			,   type=_lowercase			,   default=_lowercase			,   help="If the training should continue from a checkpoint folder."			,   )
			parser.add_argument(
			    "--partial_train_epoch"			,   type=_lowercase			,   default=_lowercase			,   help="If passed, the training will stop after this number of epochs."			,   )
			parser.add_argument(
			    "--num_epochs"			,   type=_lowercase			,   default=2			,   help="Number of train epochs."			,   )
			__lowercase       =       parser.parse_args()
			__lowercase       =       {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

			training_function(_lowercase			,   _lowercase      )


if __name__ == "__main__":
					main()












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


