"""simple docstring"""




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		str			):
       _UpperCamelCase       :    Dict					  =					UNetaDModel(**lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					checkpoint['all_modules.0.W'].data
       _UpperCamelCase       :    Dict					  =					checkpoint['all_modules.0.W'].data
       _UpperCamelCase       :    Any					  =					checkpoint['all_modules.1.weight'].data
       _UpperCamelCase       :    Union[str, Any]					  =					checkpoint['all_modules.1.bias'].data

       _UpperCamelCase       :    Dict					  =					checkpoint['all_modules.2.weight'].data
       _UpperCamelCase       :    Any					  =					checkpoint['all_modules.2.bias'].data

       _UpperCamelCase       :    List[Any]					  =					checkpoint['all_modules.3.weight'].data
       _UpperCamelCase       :    List[Any]					  =					checkpoint['all_modules.3.bias'].data

       _UpperCamelCase       :    Dict					  =					checkpoint[list(checkpoint.keys()			)[-4]].data
       _UpperCamelCase       :    Dict					  =					checkpoint[list(checkpoint.keys()			)[-3]].data
       _UpperCamelCase       :    Any					  =					checkpoint[list(checkpoint.keys()			)[-2]].data
       _UpperCamelCase       :    Optional[Any]					  =					checkpoint[list(checkpoint.keys()			)[-1]].data

       _UpperCamelCase       :    Any					  =					4

       def set_attention_weights(lowercase_:		Union[str, Any]						,  lowercase_:		Dict						,  lowercase_:		Any			):
              _UpperCamelCase       :    Union[str, Any]					  =					old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
              _UpperCamelCase       :    int					  =					old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
              _UpperCamelCase       :    Any					  =					old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

              _UpperCamelCase       :    Any					  =					old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
              _UpperCamelCase       :    Dict					  =					old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
              _UpperCamelCase       :    Any					  =					old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

              _UpperCamelCase       :    Tuple					  =					old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
              _UpperCamelCase       :    List[Any]					  =					old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

              _UpperCamelCase       :    Dict					  =					old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
              _UpperCamelCase       :    str					  =					old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

       def set_resnet_weights(lowercase_:		Dict						,  lowercase_:		str						,  lowercase_:		List[str]			):
              _UpperCamelCase       :    Tuple					  =					old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
              _UpperCamelCase       :    List[Any]					  =					old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
              _UpperCamelCase       :    Tuple					  =					old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
              _UpperCamelCase       :    str					  =					old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

              _UpperCamelCase       :    Optional[int]					  =					old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
              _UpperCamelCase       :    Dict					  =					old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
              _UpperCamelCase       :    int					  =					old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
              _UpperCamelCase       :    List[Any]					  =					old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

              _UpperCamelCase       :    Optional[int]					  =					old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
              _UpperCamelCase       :    int					  =					old_checkpoint[F"all_modules.{index}.Dense_0.bias"].data

              if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                     _UpperCamelCase       :    Tuple					  =					old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
                     _UpperCamelCase       :    Optional[int]					  =					old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

       for i, block in enumerate(new_model_architecture.downsample_blocks			):
              _UpperCamelCase       :    List[str]					  =					hasattr(lowercase_						,  'attentions'			)
              for j in range(len(block.resnets			)			):
                     set_resnet_weights(block.resnets[j]						,  lowercase_						,  lowercase_			)
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]						,  lowercase_						,  lowercase_			)
                            module_index += 1

              if hasattr(lowercase_						,  'downsamplers'			) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down						,  lowercase_						,  lowercase_			)
                     module_index += 1
                     _UpperCamelCase       :    str					  =					checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
                     _UpperCamelCase       :    Optional[Any]					  =					checkpoint[F"all_modules.{module_index}.Conv_0.bias"].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,  lowercase_						,  lowercase_			)
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]						,  lowercase_						,  lowercase_			)
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,  lowercase_						,  lowercase_			)
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks			):
              _UpperCamelCase       :    Union[str, Any]					  =					hasattr(lowercase_						,  'attentions'			)
              for j in range(len(block.resnets			)			):
                     set_resnet_weights(block.resnets[j]						,  lowercase_						,  lowercase_			)
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]						,  lowercase_						,  lowercase_			)  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(lowercase_						,  'resnet_up'			) and block.resnet_up is not None:
                     _UpperCamelCase       :    int					  =					checkpoint[F"all_modules.{module_index}.weight"].data
                     _UpperCamelCase       :    Optional[int]					  =					checkpoint[F"all_modules.{module_index}.bias"].data
                     module_index += 1
                     _UpperCamelCase       :    Any					  =					checkpoint[F"all_modules.{module_index}.weight"].data
                     _UpperCamelCase       :    Dict					  =					checkpoint[F"all_modules.{module_index}.bias"].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up						,  lowercase_						,  lowercase_			)
                     module_index += 1

       _UpperCamelCase       :    str					  =					checkpoint[F"all_modules.{module_index}.weight"].data
       _UpperCamelCase       :    Optional[Any]					  =					checkpoint[F"all_modules.{module_index}.bias"].data
       module_index += 1
       _UpperCamelCase       :    Union[str, Any]					  =					checkpoint[F"all_modules.{module_index}.weight"].data
       _UpperCamelCase       :    Optional[Any]					  =					checkpoint[F"all_modules.{module_index}.bias"].data

       return new_model_architecture.state_dict()


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

 parser.add_argument(
     '''--checkpoint_path''',
     default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
     type=str,
     required=False,
     help='''Path to the checkpoint to convert.''',
 )

 parser.add_argument(
     '''--config_file''',
     default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
     type=str,
     required=False,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument(
     '''--dump_path''',
     default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
     type=str,
     required=False,
     help='''Path to the output model.''',
 )

 UpperCAmelCase     						=			parser.parse_args()

 UpperCAmelCase     						=			torch.load(args.checkpoint_path, map_location='''cpu''')

 with open(args.config_file) as f:
  UpperCAmelCase     						=			json.loads(f.read())

 UpperCAmelCase     						=			convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

 if "sde" in config:
  del config["sde"]

 UpperCAmelCase     						=			UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  UpperCAmelCase     						=			ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  UpperCAmelCase     						=			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



