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  __magic_name__  (		lowerCAmelCase__    ):





   """simple docstring"""




   def __init__(	self					:  Tuple		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[int]		,  _lowercase					:  List[Any]		,  _lowercase					:  List[str]		,  ):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Dict		,  _lowercase					:  str							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Optional[Any]		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    return x_in * self.stds[key] + self.means[key]



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Any							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Any		,  _lowercase					:  Tuple		,  _lowercase					:  List[str]							):



    """simple docstring"""



    for key, val in cond.items():
     _UpperCamelCase:     Optional[Any]							= val.clone()
    return x_in



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  str		,  _lowercase					:  str		,  _lowercase					:  int		,  _lowercase					:  int							):



    """simple docstring"""



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

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

       _UpperCamelCase:     List[Any]							= self.scheduler._get_variance(_lowerCamelCase							)
       _UpperCamelCase:     int							= torch.exp(0.5 * posterior_variance							)
       _UpperCamelCase:     Dict							= model_std * grad

      _UpperCamelCase:     Any							= 0
      _UpperCamelCase:     List[str]							= x.detach()
      _UpperCamelCase:     Optional[int]							= x + scale * grad
      _UpperCamelCase:     str							= self.reset_xa(_lowerCamelCase		,  _lowerCamelCase		,  self.action_dim							)

     _UpperCamelCase:     Optional[Any]							= self.unet(x.permute(0		,  2		,  1							)		,  _lowerCamelCase							).sample.permute(0		,  2		,  1							)

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

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase:     Optional[int]							= self.reset_xa(_lowerCamelCase		,  _lowerCamelCase		,  self.action_dim							)
     _UpperCamelCase:     Optional[int]							= self.to_torch(_lowerCamelCase							)
    return x, y



   def __call__(	self					:  List[str]		,  _lowercase					:  int		,  _lowercase					:  List[Any]=64		,  _lowercase					:  List[Any]=32		,  _lowercase					:  Any=2		,  _lowercase					:  List[Any]=0.1							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.normalize(_lowerCamelCase		,  '''observations'''							)
    _UpperCamelCase:     Any							= obs[None].repeat(_lowerCamelCase		,  axis=0							)

    _UpperCamelCase:     int							= {0: self.to_torch(_lowerCamelCase							)}
    _UpperCamelCase:     Dict							= (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:     Tuple							= randn_tensor(_lowerCamelCase		,  device=self.unet.device							)
    _UpperCamelCase:     int							= self.reset_xa(_lowerCamelCase		,  _lowerCamelCase		,  self.action_dim							)
    _UpperCamelCase:     int							= self.to_torch(_lowerCamelCase							)

    # run the diffusion process
    _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= self.run_diffusion(_lowerCamelCase		,  _lowerCamelCase		,  _lowerCamelCase		,  _lowerCamelCase							)

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

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

    _UpperCamelCase:     int							= denorm_actions[selected_index, 0]
    return denorm_actions
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

 # directly update weight in diffusers model
 for key in state_dict:
  # it is suggested to print out the key, it usually will be something like below
  # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

  # as we have set the alpha beforehand, so just skip
  if ".alpha" in key or key in visited:
   continue

  if "text" in key:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

  # find the target layer
  _UpperCamelCase:     Union[str, Any]							= layer_infos.pop(0		)
  while len(lowercase		) > -1:
   try:
    _UpperCamelCase:     Union[str, Any]							= curr_layer.__getattr__(lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:     Tuple							= layer_infos.pop(0		)
    elif len(lowercase		) == 0:
     break
   except Exception:
    if len(lowercase		) > 0:
     temp_name += "_" + layer_infos.pop(0		)
    else:
     _UpperCamelCase:     List[Any]							= layer_infos.pop(0		)

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

  # update weight
  if len(state_dict[pair_keys[0]].shape		) == 4:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= state_dict[pair_keys[1]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		).unsqueeze(2		).unsqueeze(3		)
  else:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= state_dict[pair_keys[1]].to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		)

  # update visited list
  for item in pair_keys:
   visited.append(lowercase		)

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()

  parser.add_argument(
      '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
  )
  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument(
      '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
  )
  parser.add_argument(
      '''--lora_prefix_text_encoder''',
      default='''lora_te''',
      type=str,
      help='''The prefix of text encoder weight in safetensors''',
  )
  parser.add_argument('''--alpha''', default=0.7_5, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
  parser.add_argument(
      '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
  )
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

  UpperCAmelCase_			    =							parser.parse_args()

  UpperCAmelCase_			    =							args.base_model_path
  UpperCAmelCase_			    =							args.checkpoint_path
  UpperCAmelCase_			    =							args.dump_path
  UpperCAmelCase_			    =							args.lora_prefix_unet
  UpperCAmelCase_			    =							args.lora_prefix_text_encoder
  UpperCAmelCase_			    =							args.alpha

  UpperCAmelCase_			    =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)