


from typing import TYPE_CHECKING

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


UpperCamelCase__     =	{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	["DPTFeatureExtractor"]
					UpperCamelCase__     =	["DPTImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	[
					    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPTForDepthEstimation",
					    "DPTForSemanticSegmentation",
					    "DPTModel",
					    "DPTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_dpt import DPTFeatureExtractor
										from .image_processing_dpt import DPTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpt import (
										    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPTForDepthEstimation,
										    DPTForSemanticSegmentation,
										    DPTModel,
										    DPTPreTrainedModel,
										)


else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











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__				:int    ):






							"""simple docstring"""





							if hor == 128:
														UpperCamelCase__			       =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														UpperCamelCase__			       =     (32, 128, 256)
														UpperCamelCase__			       =     ("""UpResnetBlock1D""", """UpResnetBlock1D""")

							elif hor == 32:
														UpperCamelCase__			       =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														UpperCamelCase__			       =     (32, 64, 128, 256)
														UpperCamelCase__			       =     ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
							UpperCamelCase__			       =     torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""    )
							UpperCamelCase__			       =     model.state_dict()
							UpperCamelCase__			       =     {
							    """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__			       =     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()    )}"""    )
							UpperCamelCase__			       =     dict(zip(model.state_dict().keys()				,     hf_value_function.state_dict().keys()    )    )
							for k, v in mapping.items():
														UpperCamelCase__			       =     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"""





							UpperCamelCase__			       =     {
							    """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__			       =     torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""    )
							UpperCamelCase__			       =     model
							UpperCamelCase__			       =     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()    )}"""    )

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




