


"""simple docstring"""


import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			OmegaConf.load(_UpperCamelCase      )
			if display:
						print(yaml.dump(OmegaConf.to_container(_UpperCamelCase      )      )      )
			return config


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=None    , _UpperCamelCase=None      ):

			'''simple docstring'''




			if conf_path is None:
						__lowerCAmelCase				    =			"./model_checkpoints/vqgan_only.yaml"
			__lowerCAmelCase				    =			load_config(_UpperCamelCase    , display=_UpperCamelCase      )
			__lowerCAmelCase				    =			VQModel(**config.model.params      )
			if ckpt_path is None:
						__lowerCAmelCase				    =			"./model_checkpoints/vqgan_only.pt"
			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location=_UpperCamelCase      )
			if ".ckpt" in ckpt_path:
						__lowerCAmelCase				    =			sd["state_dict"]
			model.load_state_dict(_UpperCamelCase    , strict=_UpperCamelCase      )
			model.to(_UpperCamelCase      )
			del sd
			return model


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			model.encode(_UpperCamelCase      )
			print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
			__lowerCAmelCase				    =			model.decode(_UpperCamelCase      )
			return xrec


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			string.rsplit("."    , 1      )
			if reload:
						__lowerCAmelCase				    =			importlib.import_module(_UpperCamelCase      )
						importlib.reload(_UpperCamelCase      )
			return getattr(importlib.import_module(_UpperCamelCase    , package=_UpperCamelCase      )    , cls      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if "target" not in config:
						raise KeyError("Expected key `target` to instantiate."      )
			return get_obj_from_str(config["target"]      )(**config.get("params"    , {}      )      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=True    , _UpperCamelCase=True      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			instantiate_from_config(_UpperCamelCase      )
			if sd is not None:
						model.load_state_dict(_UpperCamelCase      )
			if gpu:
						model.cuda()
			if eval_mode:
						model.eval()
			return {"model": model}


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if ckpt:
						__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location="cpu"      )
						__lowerCAmelCase				    =			pl_sd["global_step"]
						print(f"loaded model from global step {global_step}."      )
			else:
						__lowerCAmelCase				    =			{"state_dict": None}
						__lowerCAmelCase				    =			None
			__lowerCAmelCase				    =			load_model_from_config(config.model    , pl_sd["state_dict"]    , gpu=_UpperCamelCase    , eval_mode=_UpperCamelCase      )["model"]
			return model, global_step








"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class   _UpperCamelCase  (   pl.LightningModule		):
							'''simple docstring'''







							def __init__(     self    ,   __a	):
										super().__init__()
										__lowerCAmelCase				    =			model
										__lowerCAmelCase				    =			2
										__lowerCAmelCase				    =			nn.Linear(self.model.config.hidden_size    ,   self.num_labels	)



							def 			snake_case				(     self	):
										pass


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			LongformerModel.from_pretrained(_UpperCamelCase      )
			__lowerCAmelCase				    =			LightningModel(_UpperCamelCase      )

			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location=torch.device("cpu"      )      )
			lightning_model.load_state_dict(ckpt["state_dict"]      )

			# init longformer question answering model
			__lowerCAmelCase				    =			LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase      )

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()      )
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()      )
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(_UpperCamelCase      )

			print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"      )


if __name__ == "__main__":
	A     :   Tuple			    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--longformer_model",
	    default=None,
	    type=str,
	    required=True,
	    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
	)
	parser.add_argument(
	    "--longformer_question_answering_ckpt_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path the official PyTorch Lightning Checkpoint.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	A     :   Optional[int]			    =			parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)




