

from math import ceil

def       __snake_case						( __UpperCamelCase	: Tuple				,__UpperCamelCase	: List[str]    ):



      """simple docstring"""

      A_						  =				list(range(0				,__UpperCamelCase    )    )

      A_						  =				[item for sublist in list(device_map.values()    ) for item in sublist]

      # Duplicate check
      A_						  =				[]
      for i in device_map_blocks:
            if device_map_blocks.count(__UpperCamelCase    ) > 1 and i not in duplicate_blocks:
                  duplicate_blocks.append(__UpperCamelCase    )
    # Missing blocks
      A_						  =				[i for i in blocks if i not in device_map_blocks]
      A_						  =				[i for i in device_map_blocks if i not in blocks]

      if len(__UpperCamelCase    ) != 0:
            raise ValueError(
                "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
                " These attention blocks were specified more than once: " + str(__UpperCamelCase    )    )
      if len(__UpperCamelCase    ) != 0:
            raise ValueError(
                "There are attention blocks for this model that are not specified in the device_map. Add these attention "
                "blocks to a device on the device_map: " + str(__UpperCamelCase    )    )
      if len(__UpperCamelCase    ) != 0:
            raise ValueError(
                "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
                + str(__UpperCamelCase    )    )



def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: Tuple    ):



      """simple docstring"""

      A_						  =				list(range(__UpperCamelCase    )    )
      A_						  =				int(ceil(n_layers / len(__UpperCamelCase    )    )    )
      A_						  =				[layers[i : i + n_blocks] for i in range(0				,__UpperCamelCase				,__UpperCamelCase    )]

      return dict(zip(__UpperCamelCase				,__UpperCamelCase    )    )



'''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 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       Any      =							{
         "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_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)
     __A	:       Dict      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "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": 6_5536,
         "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",
     }

     __A	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     print(f"length of state dict: {len(state_dict.keys()							)}"							)
     print(f"length of value function dict: {len(hf_value_function.state_dict().keys()							)}"							)

     __A	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


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

