





'''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_  (						__lowerCamelCase					:					List[Any] ):
   if hor == 1_28:
      lowercase_			:Optional[int]    			= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
      lowercase_			:Any    			= (32, 1_28, 2_56)
      lowercase_			:Dict    			= ("UpResnetBlock1D", "UpResnetBlock1D")

   elif hor == 32:
      lowercase_			:Tuple    			= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
      lowercase_			:Optional[int]    			= (32, 64, 1_28, 2_56)
      lowercase_			:Tuple    			= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
   lowercase_			:Optional[int]    			= torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch' )
   lowercase_			:Dict    			= model.state_dict()
   lowercase_			:List[str]    			= {
       "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",
   }
   lowercase_			:Optional[Any]    			= 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() )}' )
   lowercase_			:int    			= dict(zip(model.state_dict().keys()	,hf_value_function.state_dict().keys() ) )
   for k, v in mapping.items():
      lowercase_			:str    			= 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 							UpperCAmelCase_  (						):
   lowercase_			:Optional[int]    			= {
       "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",
   }

   lowercase_			:List[Any]    			= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
   lowercase_			:Union[str, Any]    			= model
   lowercase_			:str    			= 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() )}' )

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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''XCLIPModel''',
     '''XCLIPPreTrainedModel''',
     '''XCLIPTextModel''',
     '''XCLIPVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_x_clip import (
     XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     XCLIPConfig,
     XCLIPTextConfig,
     XCLIPVisionConfig,
 )
 from .processing_x_clip import XCLIPProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_x_clip import (
      XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      XCLIPModel,
      XCLIPPreTrainedModel,
      XCLIPTextModel,
      XCLIPVisionModel,
  )

else:
 import sys

 lowerCAmelCase					:     str												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


