




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_							( __lowerCAmelCase				):
  if hor == 1_28:
    lowerCAmelCase__										=      ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
    lowerCAmelCase__										=      (32, 1_28, 2_56)
    lowerCAmelCase__										=      ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

  elif hor == 32:
    lowerCAmelCase__										=      ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
    lowerCAmelCase__										=      (32, 64, 1_28, 2_56)
    lowerCAmelCase__										=      ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
  lowerCAmelCase__										=      torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
  lowerCAmelCase__										=      model.state_dict()
  lowerCAmelCase__										=      {
      '''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__										=      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__										=      dict(zip(model.state_dict().keys()   , hf_value_function.state_dict().keys()				)				)
  for k, v in mapping.items():
    lowerCAmelCase__										=      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 		a_							( ):
  lowerCAmelCase__										=      {
      '''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__										=      torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''				)
  lowerCAmelCase__										=      model
  lowerCAmelCase__										=      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__										=      dict(zip(state_dict.keys()   , hf_value_function.state_dict().keys()				)				)
  for k, v in mapping.items():
    lowerCAmelCase__										=      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()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__  :  List[str]					=       {
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Union[str, Any]					=       [
          """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """M2M100ForConditionalGeneration""",
          """M2M100Model""",
          """M2M100PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

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

