


import os

import numpy
import onnx





def       _UpperCamelCase       (a__				:Optional[int]				,     a__				:int    ):






							"""simple docstring"""





							UpperCamelCase__			       =     a.name
							UpperCamelCase__			       =     b.name

							UpperCamelCase__			       =     """"""
							UpperCamelCase__			       =     """"""

							UpperCamelCase__			       =     a == b

							UpperCamelCase__			       =     name_a
							UpperCamelCase__			       =     name_b

							return res





def       _UpperCamelCase       (a__				:Tuple				,     a__				:Union[str, Any]				,     a__				:str    ):






							"""simple docstring"""





							for i, input_name in enumerate(node_proto.input    ):
														if input_name == name:
																					node_proto.input.insert(_lowerCamelCase				,     _lowerCamelCase    )
																					node_proto.input.pop(i + 1    )

							if node_proto.op_type == "If":
														_graph_replace_input_with(node_proto.attribute[0].g				,     _lowerCamelCase				,     _lowerCamelCase    )
														_graph_replace_input_with(node_proto.attribute[1].g				,     _lowerCamelCase				,     _lowerCamelCase    )
							if node_proto.op_type == "Loop":
														_graph_replace_input_with(node_proto.attribute[0].g				,     _lowerCamelCase				,     _lowerCamelCase    )





def       _UpperCamelCase       (a__				:Tuple				,     a__				:Dict				,     a__				:Optional[int]    ):






							"""simple docstring"""





							for n in graph_proto.node:
														_node_replace_input_with(_lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase    )





def       _UpperCamelCase       (a__				:List[str]				,     a__				:List[Any]				,     a__				:Optional[Any]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     list(model.graph.initializer    )
							UpperCamelCase__			       =     list(model_without_ext.graph.initializer    )
							for i, ref_i in ind_to_replace:
														assert inits_with_data[i].name == inits[i].name
														assert inits_with_data[ref_i].name == inits[ref_i].name
														assert i > ref_i

														UpperCamelCase__			       =     inits[i].name
														UpperCamelCase__			       =     inits[ref_i].name

														model_without_ext.graph.initializer.remove(inits[i]    )

														# for n in model.graph.node:
														_graph_replace_input_with(model_without_ext.graph				,     _lowerCamelCase				,     _lowerCamelCase    )





def       _UpperCamelCase       (a__				:Union[str, Any]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     os.path.dirname(_lowerCamelCase    )
							UpperCamelCase__			       =     os.path.basename(_lowerCamelCase    )

							UpperCamelCase__			       =     onnx.load(os.path.join(_lowerCamelCase				,     _lowerCamelCase    )    )

							UpperCamelCase__			       =     list(model.graph.initializer    )

							UpperCamelCase__			       =     set()
							UpperCamelCase__			       =     {}
							UpperCamelCase__			       =     []

							UpperCamelCase__			       =     0

							for i in range(len(_lowerCamelCase    )    ):
														if i in dup_set:
																					continue

														for j in range(i + 1				,     len(_lowerCamelCase    )    ):
																					if j in dup_set:
																												continue
																					if _is_equal_tensor_proto(inits[i]				,     inits[j]    ):
																												dup_set.add(_lowerCamelCase    )
																												dup_set.add(_lowerCamelCase    )

																												UpperCamelCase__			       =     inits[j].data_type
																												UpperCamelCase__			       =     numpy.prod(inits[j].dims    )
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print("""unexpected data type: """				,     _lowerCamelCase    )
																												total_reduced_size += mem_size

																												UpperCamelCase__			       =     inits[i].name
																												UpperCamelCase__			       =     inits[j].name

																												if name_i in dup_map:
																																			dup_map[name_i].append(_lowerCamelCase    )
																												else:
																																			UpperCamelCase__			       =     [name_j]
																												ind_to_replace.append((j, i)    )

							print("""total reduced size: """				,     total_reduced_size / 1024 / 1024 / 1024				,     """GB"""    )

							UpperCamelCase__			       =     sorted(_lowerCamelCase    )
							_remove_dup_initializers_from_model(_lowerCamelCase				,     _lowerCamelCase				,     _lowerCamelCase    )

							UpperCamelCase__			       =     """optimized_""" + model_file_name
							UpperCamelCase__			       =     os.path.join(_lowerCamelCase				,     _lowerCamelCase    )
							onnx.save(_lowerCamelCase				,     _lowerCamelCase    )

							return new_model











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()




