





'''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 				UpperCAmelCase_     (						lowerCAmelCase_				):

				"""simple docstring"""






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

				elif hor == 32:
								lowercase    =			("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
								lowercase    =			(32, 64, 128, 256)
								lowercase    =			("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
				lowercase    =			torch.load(f'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'				)
				lowercase    =			model.state_dict()
				lowercase    =			{
				    "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",
				}
				lowercase    =			UNetaDModel(**__UpperCamelCase				)
				print(f'length of state dict: {len(state_dict.keys()				)}'				)
				print(f'length of value function dict: {len(hf_value_function.state_dict().keys()				)}'				)
				lowercase    =			dict(zip(model.state_dict().keys()		,				hf_value_function.state_dict().keys()				)				)
				for k, v in mapping.items():
								lowercase    =			state_dict.pop(__UpperCamelCase				)
				hf_value_function.load_state_dict(__UpperCamelCase				)

				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(__UpperCamelCase		,				__UpperCamelCase				)







def 				UpperCAmelCase_     (						):

				"""simple docstring"""






				lowercase    =			{
				    "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",
				}

				lowercase    =			torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"				)
				lowercase    =			model
				lowercase    =			UNetaDModel(**__UpperCamelCase				)
				print(f'length of state dict: {len(state_dict.keys()				)}'				)
				print(f'length of value function dict: {len(hf_value_function.state_dict().keys()				)}'				)

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

				hf_value_function.load_state_dict(__UpperCamelCase				)

				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(__UpperCamelCase		,				__UpperCamelCase				)


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









SCREAMING_SNAKE_CASE__   			=   {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   list[str]:
			A__	  =     set()
			# keep track of all the paths to be checked
			A__	  =     [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						A__	  =     queue.pop(0       )
						# get the last node from the path
						A__	  =     path[-1]
						if node not in explored:
									A__	  =     graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												A__	  =     list(__UpperCamelCase       )
												new_path.append(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(__UpperCamelCase       )

    # in case there's no path between the 2 nodes
			return []


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			A__	  =     [start]
			A__	  =     set(__UpperCamelCase       )
			# Keep tab on distances from `start` node.
			A__	  =     {start: 0, target: -1}
			while queue:
						A__	  =     queue.pop(0       )
						if node == target:
									A__	  =     (
									    dist[node] if dist[target] == -1 else min(dist[target]			,						dist[node]       )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(__UpperCamelCase       )
												queue.append(__UpperCamelCase       )
												A__	  =     dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4




