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      a__ (		__UpperCamelCase				):
      if hor == 1_2_8:
            SCREAMING_SNAKE_CASE_          =  ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
            SCREAMING_SNAKE_CASE_          =  (3_2, 1_2_8, 2_5_6)
            SCREAMING_SNAKE_CASE_          =  ("UpResnetBlock1D", "UpResnetBlock1D")

      elif hor == 3_2:
            SCREAMING_SNAKE_CASE_          =  ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
            SCREAMING_SNAKE_CASE_          =  (3_2, 6_4, 1_2_8, 2_5_6)
            SCREAMING_SNAKE_CASE_          =  ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
      SCREAMING_SNAKE_CASE_          =  torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''				)
      SCREAMING_SNAKE_CASE_          =  model.state_dict()
      SCREAMING_SNAKE_CASE_          =  {
          "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": 1_4,
          "out_channels": 1_4,
          "extra_in_channels": 0,
          "time_embedding_type": "positional",
          "flip_sin_to_cos": False,
          "freq_shift": 1,
          "sample_size": 6_5_5_3_6,
          "mid_block_type": "MidResTemporalBlock1D",
          "act_fn": "mish",
      }
      SCREAMING_SNAKE_CASE_          =  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()				)}'''				)
      SCREAMING_SNAKE_CASE_          =  dict(zip(model.state_dict().keys()     ,      hf_value_function.state_dict().keys()				)				)
      for k, v in mapping.items():
            SCREAMING_SNAKE_CASE_          =  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      a__ (		):
      SCREAMING_SNAKE_CASE_          =  {
          "in_channels": 1_4,
          "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
          "up_block_types": (),
          "out_block_type": "ValueFunction",
          "mid_block_type": "ValueFunctionMidBlock1D",
          "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
          "layers_per_block": 1,
          "downsample_each_block": True,
          "sample_size": 6_5_5_3_6,
          "out_channels": 1_4,
          "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",
      }

      SCREAMING_SNAKE_CASE_          =  torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
      SCREAMING_SNAKE_CASE_          =  model
      SCREAMING_SNAKE_CASE_          =  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()				)}'''				)

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



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # preprocessing the first row
      for i in range(1     ,      len(matrix[0]				)				):
            matrix[0][i] += matrix[0][i - 1]

      # preprocessing the first column
      for i in range(1     ,      len(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            for j in range(1     ,      len(matrix[0]				)				):
                  matrix[i][j] += min(matrix[i - 1][j]     ,      matrix[i][j - 1]				)

      return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()


