


'''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_      (   A__ :       Tuple ):
  '''simple docstring'''





  if hor == 1_28:
    lowerCAmelCase_      :				Optional[Any]      =       ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    lowerCAmelCase_      :				List[Any]      =       (32, 1_28, 2_56)
    lowerCAmelCase_      :				int      =       ("""UpResnetBlock1D""", """UpResnetBlock1D""")

  elif hor == 32:
    lowerCAmelCase_      :				str      =       ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    lowerCAmelCase_      :				List[Any]      =       (32, 64, 1_28, 2_56)
    lowerCAmelCase_      :				Dict      =       ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
  lowerCAmelCase_      :				List[Any]      =       torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch' )
  lowerCAmelCase_      :				Union[str, Any]      =       model.state_dict()
  lowerCAmelCase_      :				List[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_55_36,
      """mid_block_type""": """MidResTemporalBlock1D""",
      """act_fn""": """mish""",
  }
  lowerCAmelCase_      :				Dict      =       UNetaDModel(**A__ )
  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[int]      =       dict(zip(model.state_dict().keys()							,   hf_value_function.state_dict().keys() ) )
  for k, v in mapping.items():
    lowerCAmelCase_      :				Union[str, Any]      =       state_dict.pop(A__ )
  hf_value_function.load_state_dict(A__ )

  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(A__							,   A__ )







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				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, 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_      :				Optional[int]      =       torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
  lowerCAmelCase_      :				Union[str, Any]      =       model
  lowerCAmelCase_      :				List[str]      =       UNetaDModel(**A__ )
  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_      :				List[str]      =       dict(zip(state_dict.keys()							,   hf_value_function.state_dict().keys() ) )
  for k, v in mapping.items():
    lowerCAmelCase_      :				List[Any]      =       state_dict.pop(A__ )

  hf_value_function.load_state_dict(A__ )

  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(A__							,   A__ )


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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A :  List[str]    							=				{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Optional[int]    							=				["OwlViTFeatureExtractor"]
  __A :  str    							=				["OwlViTImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Tuple    							=				[
      "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OwlViTModel",
      "OwlViTPreTrainedModel",
      "OwlViTTextModel",
      "OwlViTVisionModel",
      "OwlViTForObjectDetection",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



