





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     ( lowerCAmelCase__						):
		if hor == 1_28:
				lowerCamelCase_	:     List[str] 						=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
				lowerCamelCase_	:     Dict 						=      (32, 1_28, 2_56)
				lowerCamelCase_	:     Union[str, Any] 						=      ('UpResnetBlock1D', 'UpResnetBlock1D')

		elif hor == 32:
				lowerCamelCase_	:     Tuple 						=      ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
				lowerCamelCase_	:     Any 						=      (32, 64, 1_28, 2_56)
				lowerCamelCase_	:     Union[str, Any] 						=      ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
		lowerCamelCase_	:     Dict 						=      torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"						)
		lowerCamelCase_	:     Tuple 						=      model.state_dict()
		lowerCamelCase_	:     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': 6_55_36,
		    'mid_block_type': 'MidResTemporalBlock1D',
		    'act_fn': 'mish',
		}
		lowerCamelCase_	:     Dict 						=      UNetaDModel(**lowerCAmelCase__						)
		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(model.state_dict().keys()							,hf_value_function.state_dict().keys()						)						)
		for k, v in mapping.items():
				lowerCamelCase_	:     List[Any] 						=      state_dict.pop(lowerCAmelCase__						)
		hf_value_function.load_state_dict(lowerCAmelCase__						)

		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(lowerCAmelCase__							,lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Tuple 						=      {
		    '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, 1_28, 2_56),
		    'layers_per_block': 1,
		    'downsample_each_block': True,
		    'sample_size': 6_55_36,
		    '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_	:     int 						=      torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'						)
		lowerCamelCase_	:     Optional[Any] 						=      model
		lowerCamelCase_	:     List[str] 						=      UNetaDModel(**lowerCAmelCase__						)
		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_	:     str 						=      state_dict.pop(lowerCAmelCase__						)

		hf_value_function.load_state_dict(lowerCAmelCase__						)

		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(lowerCAmelCase__							,lowerCAmelCase__						)


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







_lowercase		:  Any			       ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_lowercase		:  Union[str, Any]			       =["""a""", """b""", """c""", """d""", """e"""]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      start
		# add current to visited
		visited.append(lowerCAmelCase__						)
		lowerCamelCase_	:     Optional[int] 						=      edges[current]
		for neighbor in neighbors:
				# if neighbor not in visited, visit
				if neighbor not in visited:
						lowerCamelCase_	:     Dict 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # if all neighbors visited add current to sort
		sort.append(lowerCAmelCase__						)
		# if all vertices haven't been visited select a new one to visit
		if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
				for vertice in vertices:
						if vertice not in visited:
								lowerCamelCase_	:     Any 						=      topological_sort(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
    # return sort
		return sort


if __name__ == "__main__":
		_lowercase		:  str			       =topological_sort("""a""", [], [])
		print(sort)
