



'''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        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,)    ->		Tuple:
      super().__init__()
      lowerCAmelCase__ :  Union[str, Any]							      =					value_function
      lowerCAmelCase__ :  List[Any]							      =					unet
      lowerCAmelCase__ :  Any							      =					scheduler
      lowerCAmelCase__ :  int							      =					env
      lowerCAmelCase__ :  Union[str, Any]							      =					env.get_dataset()
      lowerCAmelCase__ :  Tuple							      =					{}
      for key in self.data.keys():
          try:
              lowerCAmelCase__ :  int							      =					self.data[key].mean()
          except:  # noqa: E722
              pass
      lowerCAmelCase__ :  List[Any]							      =					{}
      for key in self.data.keys():
          try:
              lowerCAmelCase__ :  Any							      =					self.data[key].std()
          except:  # noqa: E722
              pass
      lowerCAmelCase__ :  List[str]							      =					env.observation_space.shape[0]
      lowerCAmelCase__ :  List[str]							      =					env.action_space.shape[0]




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Dict:
      return (x_in - self.means[key]) / self.stds[key]




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		str:
      return x_in * self.stds[key] + self.means[key]




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		List[Any]:
      if type(__UpperCAmelCase      ) is dict:
          return {k: self.to_torch(__UpperCAmelCase      ) for k, v in x_in.items()}
      elif torch.is_tensor(__UpperCAmelCase      ):
          return x_in.to(self.unet.device      )
      return torch.tensor(__UpperCAmelCase  ,device=self.unet.device      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		List[str]:
      for key, val in cond.items():
          lowerCAmelCase__ :  List[Any]							      =					val.clone()
      return x_in




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  Optional[int]							      =					x.shape[0]
      lowerCAmelCase__ :  int							      =					None
      for i in tqdm.tqdm(self.scheduler.timesteps      ):
          # create batch of timesteps to pass into model
          lowerCAmelCase__ :  Dict							      =					torch.full((batch_size,)  ,__UpperCAmelCase  ,device=self.unet.device  ,dtype=torch.long      )
          for _ in range(__UpperCAmelCase      ):
              with torch.enable_grad():
                  x.requires_grad_()

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

                  lowerCAmelCase__ :  Tuple							      =					self.scheduler._get_variance(__UpperCAmelCase      )
                  lowerCAmelCase__ :  str							      =					torch.exp(0.5 * posterior_variance      )
                  lowerCAmelCase__ :  Union[str, Any]							      =					model_std * grad

              lowerCAmelCase__ :  Optional[Any]							      =					0
              lowerCAmelCase__ :  Tuple							      =					x.detach()
              lowerCAmelCase__ :  str							      =					x + scale * grad
              lowerCAmelCase__ :  Optional[Any]							      =					self.reset_xa(__UpperCAmelCase  ,__UpperCAmelCase  ,self.action_dim      )

          lowerCAmelCase__ :  Union[str, Any]							      =					self.unet(x.permute(0  ,2  ,1      )  ,__UpperCAmelCase      ).sample.permute(0  ,2  ,1      )

          # TODO: verify deprecation of this kwarg
          lowerCAmelCase__ :  Union[str, Any]							      =					self.scheduler.step(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,predict_epsilon=__UpperCAmelCase      )["""prev_sample"""]

          # apply conditions to the trajectory (set the initial state)
          lowerCAmelCase__ :  Dict							      =					self.reset_xa(__UpperCAmelCase  ,__UpperCAmelCase  ,self.action_dim      )
          lowerCAmelCase__ :  Any							      =					self.to_torch(__UpperCAmelCase      )
      return x, y






  def __call__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=64  ,__UpperCAmelCase=32  ,__UpperCAmelCase=2  ,__UpperCAmelCase=0.1      )    ->		Optional[int]:
      # normalize the observations and create  batch dimension
      lowerCAmelCase__ :  int							      =					self.normalize(__UpperCAmelCase  ,"""observations"""      )
      lowerCAmelCase__ :  Dict							      =					obs[None].repeat(__UpperCAmelCase  ,axis=0      )

      lowerCAmelCase__ :  Dict							      =					{0: self.to_torch(__UpperCAmelCase      )}
      lowerCAmelCase__ :  Union[str, Any]							      =					(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)
      lowerCAmelCase__ :  Any							      =					randn_tensor(__UpperCAmelCase  ,device=self.unet.device      )
      lowerCAmelCase__ :  int							      =					self.reset_xa(__UpperCAmelCase  ,__UpperCAmelCase  ,self.action_dim      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.to_torch(__UpperCAmelCase      )

      # run the diffusion process
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self.run_diffusion(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      # sort output trajectories by value
      lowerCAmelCase__ :  Optional[Any]							      =					y.argsort(0  ,descending=__UpperCAmelCase      ).squeeze()
      lowerCAmelCase__ :  Tuple							      =					x[sorted_idx]
      lowerCAmelCase__ :  Optional[Any]							      =					sorted_values[:, :, : self.action_dim]
      lowerCAmelCase__ :  Any							      =					actions.detach().cpu().numpy()
      lowerCAmelCase__ :  Optional[Any]							      =					self.de_normalize(__UpperCAmelCase  ,key="""actions"""      )

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

      lowerCAmelCase__ :  Optional[int]							      =					denorm_actions[selected_index, 0]
      return denorm_actions




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class 	_lowerCAmelCase					(	pl.LightningModule       ):




				def __init__(					self				,				_UpperCamelCase     )				->      List[str]:
							super().__init__()
							lowerCAmelCase_						=	model
							lowerCAmelCase_						=	2
							lowerCAmelCase_						=	nn.Linear(self.model.config.hidden_size				,				self.num_labels     )




				def      __a						(					self     )				->      Tuple:
							pass






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


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

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

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

			# 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(__lowerCAmelCase				)

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


if __name__ == "__main__":
			_A   =     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   =     parser.parse_args()
			convert_longformer_qa_checkpoint_to_pytorch(
			    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
			)



