



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


UpperCAmelCase_							:      Optional[Any]      =  16
UpperCAmelCase_							:      Tuple      =  32
def  UpperCamelCase						(       _A			:				Accelerator			,							_A			:				int = 16			,							_A			:				str = "bert-base-cased"    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       AutoTokenizer.from_pretrained(_A    )
						A__							=       load_dataset("glue"			,							"mrpc"    )

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

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

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

						def collate_fn(_A			:				List[Any]    ):
												# 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(_A			,							padding="max_length"			,							max_length=128			,							return_tensors="pt"    )
												return tokenizer.pad(_A			,							padding="longest"			,							return_tensors="pt"    )

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

						return train_dataloader, eval_dataloader
def  UpperCamelCase						(       _A			:				Dict			,							_A			:				str			,							_A			:				List[Any]			,							_A			:				Optional[int]    )->       Union[str, Any]:





						"""simple docstring"""





						model.eval()
						A__							=       0
						for step, batch in enumerate(_A    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												with torch.no_grad():
																		A__							=       model(**_A    )
												A__							=       outputs.logits.argmax(dim=-1    )
												# It is slightly faster to call this once, than multiple times
												A__						,       A__							=       accelerator.gather(
												    (predictions, batch["labels"])    )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(_A    ) - 1:
																								A__							=       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																								A__							=       references[: len(eval_dataloader.dataset    ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=_A			,							references=_A			,							)

						A__							=       metric.compute()
						return eval_metric["accuracy"]
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Union[str, Any]    )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       Accelerator()

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

						set_seed(_A    )
						A__						,       A__							=       get_dataloaders(_A			,							_A			,							_A    )

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

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

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

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

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

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

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint    )
												A__							=       args.resume_from_checkpoint.split("epoch_"    )[1]
												A__							=       ""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												A__							=       int(_A    ) + 1
												A__							=       evaluation_loop(_A			,							_A			,							_A			,							_A    )
												accelerator.print("resumed checkpoint performance:"			,							_A    )
												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:
																		A__							=       json.load(_A    )
																		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
						A__							=       {}
						for epoch in range(_A			,							_A    ):
												model.train()
												for step, batch in enumerate(_A    ):
																		A__							=       model(**_A    )
																		A__							=       outputs.loss
																		A__							=       loss / gradient_accumulation_steps
																		accelerator.backward(_A    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												A__							=       f"""epoch_{epoch}"""
												A__							=       os.path.join(args.output_dir			,							_A    )
												accelerator.save_state(_A    )
												A__							=       evaluation_loop(_A			,							_A			,							_A			,							_A    )
												A__							=       accuracy
												A__							=       lr_scheduler.get_lr()[0]
												A__							=       optimizer.param_groups[0]["lr"]
												A__							=       epoch
												A__							=       overall_step
												accelerator.print(f"""epoch {epoch}:"""			,							_A    )

												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(_A			,							_A    )
def  UpperCamelCase						(       )->       Union[str, Any]:





						"""simple docstring"""





						A__							=       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."    )
						parser.add_argument(
						    "--model_name_or_path"			,							type=_A			,							default="bert-base-cased"			,							help="Path to pretrained model or model identifier from huggingface.co/models."			,							required=_A			,							)
						parser.add_argument(
						    "--output_dir"			,							type=_A			,							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=_A			,							default=_A			,							help="If the training should continue from a checkpoint folder."			,							)
						parser.add_argument(
						    "--partial_train_epoch"			,							type=_A			,							default=_A			,							help="If passed, the training will stop after this number of epochs."			,							)
						parser.add_argument(
						    "--num_epochs"			,							type=_A			,							default=2			,							help="Number of train epochs."			,							)
						A__							=       parser.parse_args()
						A__							=       {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

						training_function(_A			,							_A    )


if __name__ == "__main__":
						main()










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





