



"""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 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			if hor == 128:
						__lowerCAmelCase      :  Dict												=       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase      :  Union[str, Any]												=       (32, 128, 256)
						__lowerCAmelCase      :  Dict												=       ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase      :  List[str]												=       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase      :  List[str]												=       (32, 64, 128, 256)
						__lowerCAmelCase      :  str												=       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase      :  Union[str, Any]												=       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			__lowerCAmelCase      :  Dict												=       model.state_dict()
			__lowerCAmelCase      :  Optional[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": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase      :  Union[str, Any]												=       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()	)}"""	)
			__lowerCAmelCase      :  Any												=       dict(zip(model.state_dict().keys()					,hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						__lowerCAmelCase      :  List[str]												=       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 			_lowercase				(  )						->   Union[str, Any]:
			__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, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65_536,
			    "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      :  Union[str, Any]												=       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"	)
			__lowerCAmelCase      :  Any												=       model
			__lowerCAmelCase      :  Union[str, Any]												=       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()	)}"""	)

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




"""simple docstring"""






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__snake_case							:   Optional[int]      =    {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
	from .config import (
	    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
	    OnnxConfig,
	    OnnxConfigWithPast,
	    OnnxSeqaSeqConfigWithPast,
	    PatchingSpec,
	)
	from .convert import export, validate_model_outputs
	from .features import FeaturesManager
	from .utils import ParameterFormat, compute_serialized_parameters_size

else:
	import sys

	__snake_case							:   Any      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)