
"""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						):
							if hor == 128:
														SCREAMING_SNAKE_CASE__						:       str           =   ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (32, 128, 256)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   ("""UpResnetBlock1D""", """UpResnetBlock1D""")

							elif hor == 32:
														SCREAMING_SNAKE_CASE__						:       Dict           =   ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														SCREAMING_SNAKE_CASE__						:       Tuple           =   (32, 64, 128, 256)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
							SCREAMING_SNAKE_CASE__						:       Dict           =   torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   model.state_dict()
							SCREAMING_SNAKE_CASE__						:       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""": 65_536,
							    """mid_block_type""": """MidResTemporalBlock1D""",
							    """act_fn""": """mish""",
							}
							SCREAMING_SNAKE_CASE__						:       Optional[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()						)}'''						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   dict(zip(model.state_dict().keys()   ,hf_value_function.state_dict().keys()						)						)
							for k, v in mapping.items():
														SCREAMING_SNAKE_CASE__						:       int           =   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_  (   ):
							SCREAMING_SNAKE_CASE__						:       Dict           =   {
							    """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""",
							}

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

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   dict(zip(state_dict.keys()   ,hf_value_function.state_dict().keys()						)						)
							for k, v in mapping.items():
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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(3_2)
					# unet(128)
					value_function()






"""simple docstring"""




def lowercase_  (   _snake_case   ,_snake_case						):
							return 1 if input_a == input_a else 0

def lowercase_  (   ):
							assert xnor_gate(0   ,0						) == 1
							assert xnor_gate(0   ,1						) == 0
							assert xnor_gate(1   ,0						) == 0
							assert xnor_gate(1   ,1						) == 1


if __name__ == "__main__":
					print(xnor_gate(0, 0))
					print(xnor_gate(0, 1))
					print(xnor_gate(1, 0))
					print(xnor_gate(1, 1))




