
'''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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE ):






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

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






def    __SCREAMING_SNAKE_CASE (    ):
							_snake_case  =    {
							    """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""",
							}

							_snake_case  =    torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch""" )
							_snake_case  =    model
							_snake_case  =    UNetaDModel(**_SCREAMING_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() )}""" )

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

							hf_value_function.load_state_dict(_SCREAMING_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(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE )


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

import warnings

from .generation import TFGenerationMixin







class 							lowerCAmelCase__   ( __lowercase						):
						# warning at import time
						warnings.warn(
						    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,						__lowercase		,						)



