


'''simple docstring'''






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 				UpperCamelCase_			(    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   filter(lambda A__					: p.requires_grad							,    model.parameters()					)
  lowerCAmelCase_			:      List[str]			=   sum([np.prod(p.size()					) for p in model_parameters]					)
  return params


__A					:  Any											=      logging.getLogger(__name__)







def 				UpperCamelCase_			(    A__					:   Optional[int]							,    A__					:   Union[str, Any]					):
  '''simple docstring'''





  if metric == "rouge2":
    lowerCAmelCase_			:      int			=   """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    lowerCAmelCase_			:      Tuple			=   """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    lowerCAmelCase_			:      Optional[Any]			=   """{val_avg_em:.4f}-{step_count}"""
  elif metric == "loss":
    lowerCAmelCase_			:      Dict			=   """{val_avg_loss:.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_			:      Dict			=   ModelCheckpoint(
      dirpath=__snake_case							,    filename=__snake_case							,    monitor=f'val_{metric}'							,    mode="""max"""							,    save_top_k=1							,    every_n_epochs=1							,    )

  return checkpoint_callback







def 				UpperCamelCase_			(    A__					:   Any							,    A__					:   Any					):
  '''simple docstring'''





  return EarlyStopping(
      monitor=f'val_{metric}'							,    mode="""min""" if """loss""" in metric else """max"""							,    patience=__snake_case							,    verbose=__snake_case							,    )






class    __snake_case       (   pl.Callback):







    """simple docstring"""



    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Dict    ,						lowerCamelCase    :  Tuple		)   ->  List[str]:
      lowerCAmelCase_			:      Optional[Any]			=   {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 __lowercase   (   self    :  str    ,						lowerCamelCase    :  Any    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  List[str]=True		)   ->  None:
      logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****'		)
      lowerCAmelCase_			:      str			=   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_			:      List[Any]			=   Path(pl_module.hparams.output_dir		)
      if type_path == "test":
        lowerCAmelCase_			:      Any			=   od / """test_results.txt"""
        lowerCAmelCase_			:      List[Any]			=   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_			:      str			=   od / F'{type_path}_results/{trainer.global_step:05d}.txt'
        lowerCAmelCase_			:      List[str]			=   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_			:      List[str]			=   metrics[key]
          if isinstance(__a    ,						torch.Tensor		):
            lowerCAmelCase_			:      Dict			=   val.item()
          lowerCAmelCase_			:      List[str]			=   F'{key}: {val:.6f}\n'
          writer.write(__a		)

      if not save_generations:
        return

      if "preds" in metrics:
        lowerCAmelCase_			:      List[Any]			=   """\n""".join(metrics["""preds"""]		)
        generations_file.open("""w+"""		).write(__a		)

    @rank_zero_only
    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  Tuple    ,						lowerCamelCase    :  List[Any]		)   ->  List[str]:
      try:
        lowerCAmelCase_			:      List[str]			=   pl_module.model.model.num_parameters()
      except AttributeError:
        lowerCAmelCase_			:      Any			=   pl_module.model.num_parameters()

      lowerCAmelCase_			:      Any			=   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 __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  List[Any]    ,						lowerCamelCase    :  Optional[Any]		)   ->  Any:
      save_json(pl_module.metrics    ,						pl_module.metrics_save_path		)
      return self._write_logs(__a    ,						__a    ,						"""test"""		)





    @rank_zero_only
    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  Optional[int]    ,						lowerCamelCase    :  Any		)   ->  Tuple:
      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 numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       Optional[int]:






							'''simple docstring'''
							super().__init__()
							_UpperCamelCase							 =  value_function
							_UpperCamelCase							 =  unet
							_UpperCamelCase							 =  scheduler
							_UpperCamelCase							 =  env
							_UpperCamelCase							 =  env.get_dataset()
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].mean()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  {}
							for key in self.data.keys():
												try:
																	_UpperCamelCase							 =  self.data[key].std()
												except:  # noqa: E722
																	pass
							_UpperCamelCase							 =  env.observation_space.shape[0]
							_UpperCamelCase							 =  env.action_space.shape[0]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							return (x_in - self.means[key]) / self.stds[key]



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							return x_in * self.stds[key] + self.means[key]



		def 						UpperCAmelCase							(					self				,		__a)       ->       Union[str, Any]:






							'''simple docstring'''
							if type(__a) is dict:
												return {k: self.to_torch(__a) for k, v in x_in.items()}
							elif torch.is_tensor(__a):
												return x_in.to(self.unet.device)
							return torch.tensor(__a				,		device=self.unet.device)



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       str:






							'''simple docstring'''
							for key, val in cond.items():
												_UpperCamelCase							 =  val.clone()
							return x_in



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  x.shape[0]
							_UpperCamelCase							 =  None
							for i in tqdm.tqdm(self.scheduler.timesteps):
												# create batch of timesteps to pass into model
												_UpperCamelCase							 =  torch.full((batch_size,)				,		__a				,		device=self.unet.device				,		dtype=torch.long)
												for _ in range(__a):
																	with torch.enable_grad():
																						x.requires_grad_()

																						# permute to match dimension for pre-trained models
																						_UpperCamelCase							 =  self.value_function(x.permute(0				,		2				,		1)				,		__a).sample
																						_UpperCamelCase							 =  torch.autograd.grad([y.sum()]				,		[x])[0]

																						_UpperCamelCase							 =  self.scheduler._get_variance(__a)
																						_UpperCamelCase							 =  torch.exp(0.5 * posterior_variance)
																						_UpperCamelCase							 =  model_std * grad

																	_UpperCamelCase							 =  0
																	_UpperCamelCase							 =  x.detach()
																	_UpperCamelCase							 =  x + scale * grad
																	_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)

												_UpperCamelCase							 =  self.unet(x.permute(0				,		2				,		1)				,		__a).sample.permute(0				,		2				,		1)

												# TODO: verify deprecation of this kwarg
												_UpperCamelCase							 =  self.scheduler.step(__a				,		__a				,		__a				,		predict_epsilon=__a)['''prev_sample''']

												# apply conditions to the trajectory (set the initial state)
												_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
												_UpperCamelCase							 =  self.to_torch(__a)
							return x, y




		def __call__(					self				,		__a				,		__a=64				,		__a=32				,		__a=2				,		__a=0.1)       ->       Optional[Any]:






							'''simple docstring'''
							# normalize the observations and create  batch dimension
							_UpperCamelCase							 =  self.normalize(__a				,		'''observations''')
							_UpperCamelCase							 =  obs[None].repeat(__a				,		axis=0)

							_UpperCamelCase							 =  {0: self.to_torch(__a)}
							_UpperCamelCase							 =  (batch_size, planning_horizon, self.state_dim + self.action_dim)

							# generate initial noise and apply our conditions (to make the trajectories start at current state)
							_UpperCamelCase							 =  randn_tensor(__a				,		device=self.unet.device)
							_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
							_UpperCamelCase							 =  self.to_torch(__a)

							# run the diffusion process
							_UpperCamelCase ,  _UpperCamelCase							 =  self.run_diffusion(__a				,		__a				,		__a				,		__a)

							# sort output trajectories by value
							_UpperCamelCase							 =  y.argsort(0				,		descending=__a).squeeze()
							_UpperCamelCase							 =  x[sorted_idx]
							_UpperCamelCase							 =  sorted_values[:, :, : self.action_dim]
							_UpperCamelCase							 =  actions.detach().cpu().numpy()
							_UpperCamelCase							 =  self.de_normalize(__a				,		key='''actions''')

							# select the action with the highest value
							if y is not None:
												_UpperCamelCase							 =  0
							else:
												# if we didn't run value guiding, select a random action
												_UpperCamelCase							 =  np.random.randint(0				,		__a)

							_UpperCamelCase							 =  denorm_actions[selected_index, 0]
							return denorm_actions






