





import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   filter(lambda UpperCamelCase__     : p.requires_grad       , model.parameters()     )
		SCREAMING_SNAKE_CASE__								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
		return params


_lowerCamelCase 					=      logging.getLogger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Dict     ):
		if metric == "rouge2":
				SCREAMING_SNAKE_CASE__								=   """{val_avg_rouge2:.4f}-{step_count}"""
		elif metric == "bleu":
				SCREAMING_SNAKE_CASE__								=   """{val_avg_bleu:.4f}-{step_count}"""
		elif metric == "em":
				SCREAMING_SNAKE_CASE__								=   """{val_avg_em:.4f}-{step_count}"""
		else:
				raise NotImplementedError(
				    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
				    """ function."""     )

		SCREAMING_SNAKE_CASE__								=   ModelCheckpoint(
		    dirpath=UpperCamelCase__       , filename=UpperCamelCase__       , monitor=f'''val_{metric}'''       , mode="""max"""       , save_top_k=3       , every_n_epochs=1       , )
		return checkpoint_callback






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				List[str]     ):
		return EarlyStopping(
		    monitor=f'''val_{metric}'''       , mode="""min""" if """loss""" in metric else """max"""       , patience=UpperCamelCase__       , verbose=UpperCamelCase__       , )




class 	UpperCamelCase_							(  pl.Callback     ):


							def   _snake_case	(				self     :List[Any]				,					__A     :Any				,					__A     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
									pl_module.logger.log_metrics(__A		)


							@rank_zero_only
							def   _snake_case	(				self     :Union[str, Any]				,					__A     :pl.Trainer				,					__A     :pl.LightningModule				,					__A     :str				,					__A     :str=True		) -> None:



									"""simple docstring"""



									logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''		)
									SCREAMING_SNAKE_CASE__								=   trainer.callback_metrics
									trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
									# Log results
									SCREAMING_SNAKE_CASE__								=   Path(pl_module.hparams.output_dir		)
									if type_path == "test":
											SCREAMING_SNAKE_CASE__								=   od / """test_results.txt"""
											SCREAMING_SNAKE_CASE__								=   od / """test_generations.txt"""
									else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											SCREAMING_SNAKE_CASE__								=   od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
											SCREAMING_SNAKE_CASE__								=   od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=__A		)
											generations_file.parent.mkdir(exist_ok=__A		)
									with open(__A				,					"""a+"""		) as writer:
											for key in sorted(__A		):
													if key in ["log", "progress_bar", "preds"]:
															continue
													SCREAMING_SNAKE_CASE__								=   metrics[key]
													if isinstance(__A				,					torch.Tensor		):
															SCREAMING_SNAKE_CASE__								=   val.item()
													SCREAMING_SNAKE_CASE__								=   f'''{key}: {val:.6f}\n'''
													writer.write(__A		)

									if not save_generations:
											return

									if "preds" in metrics:
											SCREAMING_SNAKE_CASE__								=   """\n""".join(metrics["""preds"""]		)
											generations_file.open("""w+"""		).write(__A		)


							@rank_zero_only
							def   _snake_case	(				self     :List[str]				,					__A     :str				,					__A     :Union[str, Any]		) -> List[str]:



									"""simple docstring"""



									try:
											SCREAMING_SNAKE_CASE__								=   pl_module.model.model.num_parameters()
									except AttributeError:
											SCREAMING_SNAKE_CASE__								=   pl_module.model.num_parameters()

									SCREAMING_SNAKE_CASE__								=   count_trainable_parameters(__A		)
									# mp stands for million parameters
									trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)


							@rank_zero_only
							def   _snake_case	(				self     :List[Any]				,					__A     :pl.Trainer				,					__A     :pl.LightningModule		) -> Dict:



									"""simple docstring"""



									save_json(pl_module.metrics				,					pl_module.metrics_save_path		)
									return self._write_logs(__A				,					__A				,					"""test"""		)


							@rank_zero_only
							def   _snake_case	(				self     :int				,					__A     :pl.Trainer				,					__A     :Dict		) -> int:



									"""simple docstring"""



									save_json(pl_module.metrics				,					pl_module.metrics_save_path		)
									# Uncommenting this will save val generations
									# return self._write_logs(trainer, pl_module, "valid")



'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

							# extract state_dict for VQVAE
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''first_stage_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

    # extract state_dict for UNetLDM
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''model.diffusion_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      DDIMScheduler(
							    timesteps=config.model.params.timesteps					,       beta_schedule='''scaled_linear'''					,       beta_start=config.model.params.linear_start					,       beta_end=config.model.params.linear_end					,       clip_sample=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument('''--checkpoint_path''', type=str, required=True)
		parser.add_argument('''--config_path''', type=str, required=True)
		parser.add_argument('''--output_path''', type=str, required=True)
		__UpperCAmelCase       							=				parser.parse_args()

		convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)