"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict	)       ->      Dict:
  if hor == 128:
    _lowerCAmelCase						:       Any  			=     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    _lowerCAmelCase						:       Optional[int]  			=     (32, 128, 256)
    _lowerCAmelCase						:       Any  			=     ("""UpResnetBlock1D""", """UpResnetBlock1D""")

  elif hor == 32:
    _lowerCAmelCase						:       Tuple  			=     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    _lowerCAmelCase						:       int  			=     (32, 64, 128, 256)
    _lowerCAmelCase						:       List[Any]  			=     ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
  _lowerCAmelCase						:       Tuple  			=     torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"	)
  _lowerCAmelCase						:       Union[str, Any]  			=     model.state_dict()
  _lowerCAmelCase						:       Dict  			=     {
      """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""": 65536,
      """mid_block_type""": """MidResTemporalBlock1D""",
      """act_fn""": """mish""",
  }
  _lowerCAmelCase						:       Optional[int]  			=     UNetaDModel(**_lowerCamelCase	)
  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						:       int  			=     dict(zip(model.state_dict().keys()			,hf_value_function.state_dict().keys()	)	)
  for k, v in mapping.items():
    _lowerCAmelCase						:       Optional[int]  			=     state_dict.pop(_lowerCamelCase	)
  hf_value_function.load_state_dict(_lowerCamelCase	)

  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(_lowerCamelCase			,_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			)       ->      List[str]:
  _lowerCAmelCase						:       List[str]  			=     {
      """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, 128, 256),
      """layers_per_block""": 1,
      """downsample_each_block""": True,
      """sample_size""": 65536,
      """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						:       Union[str, Any]  			=     torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""	)
  _lowerCAmelCase						:       Any  			=     model
  _lowerCAmelCase						:       int  			=     UNetaDModel(**_lowerCamelCase	)
  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						:       Optional[Any]  			=     dict(zip(state_dict.keys()			,hf_value_function.state_dict().keys()	)	)
  for k, v in mapping.items():
    _lowerCAmelCase						:       Optional[int]  			=     state_dict.pop(_lowerCamelCase	)

  hf_value_function.load_state_dict(_lowerCamelCase	)

  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(_lowerCamelCase			,_lowerCamelCase	)


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





"""simple docstring"""




# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)




