



"""simple docstring"""






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				(HeunDiscreteScheduler,)
					_a       =				1_0



					def 						snake_case			(			self				:    Any,    **lowerCamelCase				:    Any	)->   Optional[int]:
								lowerCamelCase__						:						Optional[int]						      ={
								    """num_train_timesteps""": 1100,
								    """beta_start""": 0.0_001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**UpperCAmelCase__	)
								return config



					def 						snake_case			(			self				:    Any	)->   List[Any]:







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase__	)



					def 						snake_case			(			self				:    Optional[Any]	)->   str:







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001],    [0.0_002, 0.002, 0.02]	):
											self.check_over_configs(beta_start=UpperCAmelCase__,    beta_end=UpperCAmelCase__	)



					def 						snake_case			(			self				:    Any	)->   Any:







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=UpperCAmelCase__	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=UpperCAmelCase__,    clip_sample=UpperCAmelCase__	)



					def 						snake_case			(			self				:    List[str]	)->   Optional[Any]:







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=UpperCAmelCase__	)



					def 						snake_case			(			self				:    List[Any]	)->   int:
								lowerCamelCase__						:						List[str]						      =self.scheduler_classes[0]
								lowerCamelCase__						:						Optional[Any]						      =self.get_scheduler_config()
								lowerCamelCase__						:						Tuple						      =scheduler_class(**UpperCAmelCase__	)

								scheduler.set_timesteps(self.num_inference_steps	)

								lowerCamelCase__						:						Union[str, Any]						      =self.dummy_model()
								lowerCamelCase__						:						str						      =self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCamelCase__						:						Optional[Any]						      =sample.to(UpperCAmelCase__	)

								for i, t in enumerate(scheduler.timesteps	):
											lowerCamelCase__						:						int						      =scheduler.scale_model_input(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						List[str]						      =model(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						str						      =scheduler.step(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__	)
											lowerCamelCase__						:						str						      =output.prev_sample

								lowerCamelCase__						:						Tuple						      =torch.sum(torch.abs(UpperCAmelCase__	)	)
								lowerCamelCase__						:						Any						      =torch.mean(torch.abs(UpperCAmelCase__	)	)

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233	) < 1E-2
											assert abs(result_mean.item() - 0.0_002	) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233	) < 1E-2
											assert abs(result_mean.item() - 0.0_002	) < 1E-3



					def 						snake_case			(			self				:    Optional[Any]	)->   Optional[int]:
								lowerCamelCase__						:						str						      =self.scheduler_classes[0]
								lowerCamelCase__						:						Union[str, Any]						      =self.get_scheduler_config(prediction_type='''v_prediction'''	)
								lowerCamelCase__						:						Union[str, Any]						      =scheduler_class(**UpperCAmelCase__	)

								scheduler.set_timesteps(self.num_inference_steps	)

								lowerCamelCase__						:						Any						      =self.dummy_model()
								lowerCamelCase__						:						Any						      =self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCamelCase__						:						Dict						      =sample.to(UpperCAmelCase__	)

								for i, t in enumerate(scheduler.timesteps	):
											lowerCamelCase__						:						List[Any]						      =scheduler.scale_model_input(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						Dict						      =model(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						str						      =scheduler.step(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__	)
											lowerCamelCase__						:						List[Any]						      =output.prev_sample

								lowerCamelCase__						:						Tuple						      =torch.sum(torch.abs(UpperCAmelCase__	)	)
								lowerCamelCase__						:						Union[str, Any]						      =torch.mean(torch.abs(UpperCAmelCase__	)	)

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07	) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10	) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693428650170972E-07	) < 1E-2
											assert abs(result_mean.item() - 0.0_002	) < 1E-3



					def 						snake_case			(			self				:    str	)->   Union[str, Any]:
								lowerCamelCase__						:						int						      =self.scheduler_classes[0]
								lowerCamelCase__						:						Dict						      =self.get_scheduler_config()
								lowerCamelCase__						:						str						      =scheduler_class(**UpperCAmelCase__	)

								scheduler.set_timesteps(self.num_inference_steps,    device=UpperCAmelCase__	)

								lowerCamelCase__						:						int						      =self.dummy_model()
								lowerCamelCase__						:						Optional[Any]						      =self.dummy_sample_deter.to(UpperCAmelCase__	) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCamelCase__						:						List[str]						      =scheduler.scale_model_input(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						str						      =model(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						List[Any]						      =scheduler.step(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__	)
											lowerCamelCase__						:						Optional[Any]						      =output.prev_sample

								lowerCamelCase__						:						int						      =torch.sum(torch.abs(UpperCAmelCase__	)	)
								lowerCamelCase__						:						Dict						      =torch.mean(torch.abs(UpperCAmelCase__	)	)

								if str(UpperCAmelCase__	).startswith('''cpu'''	):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233	) < 1E-2
											assert abs(result_mean.item() - 0.0_002	) < 1E-3
								elif str(UpperCAmelCase__	).startswith('''mps'''	):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002	) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233	) < 1E-2
											assert abs(result_mean.item() - 0.0_002	) < 1E-3




					def 						snake_case			(			self				:    Any	)->   str:
								lowerCamelCase__						:						int						      =self.scheduler_classes[0]
								lowerCamelCase__						:						Optional[Any]						      =self.get_scheduler_config()
								lowerCamelCase__						:						Dict						      =scheduler_class(**UpperCAmelCase__,    use_karras_sigmas=UpperCAmelCase__	)

								scheduler.set_timesteps(self.num_inference_steps,    device=UpperCAmelCase__	)

								lowerCamelCase__						:						Tuple						      =self.dummy_model()
								lowerCamelCase__						:						Optional[Any]						      =self.dummy_sample_deter.to(UpperCAmelCase__	) * scheduler.init_noise_sigma
								lowerCamelCase__						:						Optional[Any]						      =sample.to(UpperCAmelCase__	)

								for t in scheduler.timesteps:
											lowerCamelCase__						:						Optional[int]						      =scheduler.scale_model_input(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						int						      =model(UpperCAmelCase__,    UpperCAmelCase__	)

											lowerCamelCase__						:						List[str]						      =scheduler.step(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__	)
											lowerCamelCase__						:						Any						      =output.prev_sample

								lowerCamelCase__						:						Any						      =torch.sum(torch.abs(UpperCAmelCase__	)	)
								lowerCamelCase__						:						Dict						      =torch.mean(torch.abs(UpperCAmelCase__	)	)

								assert abs(result_sum.item() - 0.00_015	) < 1E-2
								assert abs(result_mean.item() - 1.9869554535034695E-07	) < 1E-2







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 import save_json






def        __lowercase   (  _A      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Dict	  = filter(lambda _A      : p.requires_grad    ,     model.parameters()      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = sum([np.prod(p.size()      ) for p in model_parameters]      )
      return params


UpperCAmelCase__			:  List[Any]					     =     logging.getLogger(__name__)







class 							a__							( pl.Callback							):







  """simple docstring"""




  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Any		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
        pl_module.logger.log_metrics(UpperCAmelCase__		)


  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Tuple=True		)       ->None:
        """simple docstring"""







        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"		)
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = 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			:   Union[str, Any]	  = Path(pl_module.hparams.output_dir		)
        if type_path == "test":
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = od / """test_results.txt"""
              SCREAMING_SNAKE_CASE			:   int	  = 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			:   Any	  = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
              SCREAMING_SNAKE_CASE			:   int	  = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
              results_file.parent.mkdir(exist_ok=UpperCAmelCase__		)
              generations_file.parent.mkdir(exist_ok=UpperCAmelCase__		)
        with open(UpperCAmelCase__					,					"""a+"""		) as writer:
              for key in sorted(UpperCAmelCase__		):
                    if key in ["log", "progress_bar", "preds"]:
                          continue
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = metrics[key]
                    if isinstance(UpperCAmelCase__					,					torch.Tensor		):
                          SCREAMING_SNAKE_CASE			:   Optional[int]	  = val.item()
                    SCREAMING_SNAKE_CASE			:   List[str]	  = f"{key}: {val:.6f}\n"
                    writer.write(UpperCAmelCase__		)

        if not save_generations:
              return

        if "preds" in metrics:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = """\n""".join(metrics["""preds"""]		)
              generations_file.open("""w+"""		).write(UpperCAmelCase__		)


  @rank_zero_only
  def 			_lowercase   (      self     :			Optional[int]					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->int:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE			:   int	  = pl_module.model.model.num_parameters()
        except AttributeError:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = pl_module.model.num_parameters()

        SCREAMING_SNAKE_CASE			:   Dict	  = count_trainable_parameters(UpperCAmelCase__		)
        # 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 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			pl.LightningModule		)       ->Optional[int]:
        """simple docstring"""







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




  @rank_zero_only
  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			pl.Trainer					,					UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """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")






def        __lowercase   (  _A    ,     _A    ,     _A=1    ,     _A=False      )  ->    Tuple:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE			:   Tuple	  = """{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE			:   Any	  = """{val_avg_loss:.4f}-{step_count}"""
      else:
            raise NotImplementedError(
                F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
                """ this function."""      )

      SCREAMING_SNAKE_CASE			:   Tuple	  = ModelCheckpoint(
          dirpath=_A    ,     filename=_A    ,     monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     save_top_k=_A    ,     )
      return checkpoint_callback







def        __lowercase   (  _A    ,     _A      )  ->    Optional[int]:
      return EarlyStopping(
          monitor=F"val_{metric}"    ,     mode="""min""" if """loss""" in metric else """max"""    ,     patience=_A    ,     verbose=_A    ,     )




