

'''simple docstring'''





import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[str]:
					if hor == 1_28:
										lowerCamelCase_  				=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
										lowerCamelCase_  				=      (32, 1_28, 2_56)
										lowerCamelCase_  				=      ('UpResnetBlock1D', 'UpResnetBlock1D')

					elif hor == 32:
										lowerCamelCase_  				=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
										lowerCamelCase_  				=      (32, 64, 1_28, 2_56)
										lowerCamelCase_  				=      ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
					lowerCamelCase_  				=      torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''	)
					lowerCamelCase_  				=      model.state_dict()
					lowerCamelCase_  				=      {
					    'down_block_types': down_block_types,
					    'block_out_channels': block_out_channels,
					    'up_block_types': up_block_types,
					    'layers_per_block': 1,
					    'use_timestep_embedding': True,
					    'out_block_type': 'OutConv1DBlock',
					    'norm_num_groups': 8,
					    'downsample_each_block': False,
					    'in_channels': 14,
					    'out_channels': 14,
					    'extra_in_channels': 0,
					    'time_embedding_type': 'positional',
					    'flip_sin_to_cos': False,
					    'freq_shift': 1,
					    'sample_size': 6_55_36,
					    'mid_block_type': 'MidResTemporalBlock1D',
					    'act_fn': 'mish',
					}
					lowerCamelCase_  				=      UNetaDModel(**__UpperCamelCase	)
					print(f'''length of state dict: {len(state_dict.keys()	)}'''	)
					print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()	)}'''	)
					lowerCamelCase_  				=      dict(zip(model.state_dict().keys()    ,hf_value_function.state_dict().keys()	)	)
					for k, v in mapping.items():
										lowerCamelCase_  				=      state_dict.pop(__UpperCamelCase	)
					hf_value_function.load_state_dict(__UpperCamelCase	)

					torch.save(hf_value_function.state_dict()    ,f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''	)
					with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''    ,'w'	) as f:
										json.dump(__UpperCamelCase    ,__UpperCamelCase	)







def   _UpperCamelCase	(		)       ->			Tuple:
					lowerCamelCase_  				=      {
					    'in_channels': 14,
					    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
					    'up_block_types': (),
					    'out_block_type': 'ValueFunction',
					    'mid_block_type': 'ValueFunctionMidBlock1D',
					    'block_out_channels': (32, 64, 1_28, 2_56),
					    'layers_per_block': 1,
					    'downsample_each_block': True,
					    'sample_size': 6_55_36,
					    'out_channels': 14,
					    'extra_in_channels': 0,
					    'time_embedding_type': 'positional',
					    'use_timestep_embedding': True,
					    'flip_sin_to_cos': False,
					    'freq_shift': 1,
					    'norm_num_groups': 8,
					    'act_fn': 'mish',
					}

					lowerCamelCase_  				=      torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'	)
					lowerCamelCase_  				=      model
					lowerCamelCase_  				=      UNetaDModel(**__UpperCamelCase	)
					print(f'''length of state dict: {len(state_dict.keys()	)}'''	)
					print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()	)}'''	)

					lowerCamelCase_  				=      dict(zip(state_dict.keys()    ,hf_value_function.state_dict().keys()	)	)
					for k, v in mapping.items():
										lowerCamelCase_  				=      state_dict.pop(__UpperCamelCase	)

					hf_value_function.load_state_dict(__UpperCamelCase	)

					torch.save(hf_value_function.state_dict()    ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'	)
					with open('hub/hopper-medium-v2/value_function/config.json'    ,'w'	) as f:
										json.dump(__UpperCamelCase    ,__UpperCamelCase	)


if __name__ == "__main__":
				unet(32)
				# unet(128)
				value_function()








'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
