


'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  (CMStochasticIterativeScheduler,)
  _lowerCamelCase		    =  10






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  {
       "num_train_timesteps": 201,
       "sigma_min": 0.002,
       "sigma_max": 80.0,
   }

   config.update(**UpperCamelCase     )
   return config






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  10

   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  self.scheduler_classes[0](**UpperCamelCase     )

   scheduler.set_timesteps(UpperCamelCase     )

   lowerCamelCase_      =  scheduler.timesteps[0]
   lowerCamelCase_      =  scheduler.timesteps[1]

   lowerCamelCase_      =  self.dummy_sample
   lowerCamelCase_      =  0.1 * sample

   lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ).prev_sample
   lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ).prev_sample

   self.assertEqual(output_a.shape			,  sample.shape     )
   self.assertEqual(output_a.shape			,  output_a.shape     )






  def   snake_case		(    self     ):







   """simple docstring"""
   for timesteps in [10, 50, 100, 1000]:
    self.check_over_configs(num_train_timesteps=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   for clip_denoised in [True, False]:
    self.check_over_configs(clip_denoised=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   lowerCamelCase_      =  1
   scheduler.set_timesteps(UpperCamelCase     )
   lowerCamelCase_      =  scheduler.timesteps

   lowerCamelCase_      =  torch.manual_seed(0     )

   lowerCamelCase_      =  self.dummy_model()
   lowerCamelCase_      =  self.dummy_sample_deter * scheduler.init_noise_sigma

   for i, t in enumerate(UpperCamelCase     ):
    # 1. scale model input
    lowerCamelCase_      =  scheduler.scale_model_input(UpperCamelCase			,  UpperCamelCase     )

    # 2. predict noise residual
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     )

    # 3. predict previous sample x_t-1
    lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    lowerCamelCase_      =  pred_prev_sample

   lowerCamelCase_      =  torch.sum(torch.abs(UpperCamelCase     )     )
   lowerCamelCase_      =  torch.mean(torch.abs(UpperCamelCase     )     )

   assert abs(result_sum.item() - 192.7_614     ) < 1e-2
   assert abs(result_mean.item() - 0.2_510     ) < 1e-3






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   lowerCamelCase_      =  [106, 0]
   scheduler.set_timesteps(timesteps=UpperCamelCase     )
   lowerCamelCase_      =  scheduler.timesteps

   lowerCamelCase_      =  torch.manual_seed(0     )

   lowerCamelCase_      =  self.dummy_model()
   lowerCamelCase_      =  self.dummy_sample_deter * scheduler.init_noise_sigma

   for t in timesteps:
    # 1. scale model input
    lowerCamelCase_      =  scheduler.scale_model_input(UpperCamelCase			,  UpperCamelCase     )

    # 2. predict noise residual
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     )

    # 3. predict previous sample x_t-1
    lowerCamelCase_      =  scheduler.step(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    lowerCamelCase_      =  pred_prev_sample

   lowerCamelCase_      =  torch.sum(torch.abs(UpperCamelCase     )     )
   lowerCamelCase_      =  torch.mean(torch.abs(UpperCamelCase     )     )

   assert abs(result_sum.item() - 347.6_357     ) < 1e-2
   assert abs(result_mean.item() - 0.4_527     ) < 1e-3






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   lowerCamelCase_      =  [39, 30, 12, 15, 0]

   with self.assertRaises(UpperCamelCase			,  msg="`timesteps` must be in descending order."     ):
    scheduler.set_timesteps(timesteps=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   lowerCamelCase_      =  [39, 30, 12, 1, 0]
   lowerCamelCase_      =  len(UpperCamelCase     )

   with self.assertRaises(UpperCamelCase			,  msg="Can only pass one of `num_inference_steps` or `timesteps`."     ):
    scheduler.set_timesteps(num_inference_steps=UpperCamelCase			,  timesteps=UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.scheduler_classes[0]
   lowerCamelCase_      =  self.get_scheduler_config()
   lowerCamelCase_      =  scheduler_class(**UpperCamelCase     )

   lowerCamelCase_      =  [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       UpperCamelCase			,  msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,  ):
    scheduler.set_timesteps(timesteps=UpperCamelCase     )









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 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad      ,      model.parameters()     )
					__lowerCAmelCase				   =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					if metric == "rouge2":
										__lowerCAmelCase				   =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__lowerCAmelCase				   =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__lowerCAmelCase				   =       "{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."     )

					__lowerCAmelCase				   =       ModelCheckpoint(
					    dirpath=SCREAMING_SNAKE_CASE_      ,      filename=SCREAMING_SNAKE_CASE_      ,      monitor=F"""val_{metric}"""      ,      mode="max"      ,      save_top_k=3      ,      every_n_epochs=1      ,      )
					return checkpoint_callback







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					return EarlyStopping(
					    monitor=F"""val_{metric}"""      ,      mode="min" if "loss" in metric else "max"      ,      patience=SCREAMING_SNAKE_CASE_      ,      verbose=SCREAMING_SNAKE_CASE_      ,      )


class 			a__    (					pl.Callback      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
											__lowerCAmelCase				   =       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
											__lowerCAmelCase				   =       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																__lowerCAmelCase				   =       od / "test_results.txt"
																__lowerCAmelCase				   =       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.
																__lowerCAmelCase				   =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__lowerCAmelCase				   =       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
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



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






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""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")




