


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__    (		snake_case__					:							str     ):
  if hor == 128:
    _UpperCAmelCase		:							Optional[Any]							=     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    _UpperCAmelCase		:							int							=     (32, 128, 256)
    _UpperCAmelCase		:							List[str]							=     ("""UpResnetBlock1D""", """UpResnetBlock1D""")

  elif hor == 32:
    _UpperCAmelCase		:							Any							=     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    _UpperCAmelCase		:							Optional[int]							=     (32, 64, 128, 256)
    _UpperCAmelCase		:							Union[str, Any]							=     ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
  _UpperCAmelCase		:							Optional[Any]							=     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''     )
  _UpperCAmelCase		:							Union[str, Any]							=     model.state_dict()
  _UpperCAmelCase		:							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""": 6_5536,
      """mid_block_type""": """MidResTemporalBlock1D""",
      """act_fn""": """mish""",
  }
  _UpperCAmelCase		:							List[str]							=     UNetaDModel(**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()     )}'''     )
  _UpperCAmelCase		:							Tuple							=     dict(zip(model.state_dict().keys()						,   hf_value_function.state_dict().keys()     )     )
  for k, v in mapping.items():
    _UpperCAmelCase		:							Optional[Any]							=     state_dict.pop(snake_case__     )
  hf_value_function.load_state_dict(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(snake_case__						,   snake_case__     )




def    a__    (		):
  _UpperCAmelCase		:							Tuple							=     {
      """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""",
  }

  _UpperCAmelCase		:							str							=     torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""     )
  _UpperCAmelCase		:							Optional[int]							=     model
  _UpperCAmelCase		:							Dict							=     UNetaDModel(**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()     )}'''     )

  _UpperCAmelCase		:							str							=     dict(zip(state_dict.keys()						,   hf_value_function.state_dict().keys()     )     )
  for k, v in mapping.items():
    _UpperCAmelCase		:							List[str]							=     state_dict.pop(snake_case__     )

  hf_value_function.load_state_dict(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(snake_case__						,   snake_case__     )


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









from PIL import Image




def    a__    (		snake_case__					:							Image						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     (259 * (level + 255)) / (255 * (259 - level))

  def contrast(snake_case__					:							int     ) -> int:
    return int(128 + factor * (c - 128)     )

  return img.point(snake_case__     )


if __name__ == "__main__":
 # Load image
 with Image.open('image_data/lena.jpg') as img:
  # Change contrast to 170
  SCREAMING_SNAKE_CASE__					:     int       =     change_contrast(img, 170)
  cont_img.save('image_data/lena_high_contrast.png', format='png')





