




'''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_(     snake_case							:	Tuple					):

							'''simple docstring'''






							if hor == 1_2_8:
														snake_case_      =				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
														snake_case_      =				(3_2, 1_2_8, 2_5_6)
														snake_case_      =				("UpResnetBlock1D", "UpResnetBlock1D")

							elif hor == 3_2:
														snake_case_      =				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
														snake_case_      =				(3_2, 6_4, 1_2_8, 2_5_6)
														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": 1_4,
							    "out_channels": 1_4,
							    "extra_in_channels": 0,
							    "time_embedding_type": "positional",
							    "flip_sin_to_cos": False,
							    "freq_shift": 1,
							    "sample_size": 6_5_5_3_6,
							    "mid_block_type": "MidResTemporalBlock1D",
							    "act_fn": "mish",
							}
							snake_case_      =				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()					)}'					)
							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(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 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				{
							    "in_channels": 1_4,
							    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
							    "up_block_types": (),
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
							    "layers_per_block": 1,
							    "downsample_each_block": True,
							    "sample_size": 6_5_5_3_6,
							    "out_channels": 1_4,
							    "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(**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(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(32)
		# unet(128)
		value_function()










'''simple docstring'''





_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"Alexander Joslin"

import operator as op

from .stack import Stack


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

							snake_case_      =				Stack()
							snake_case_      =				Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(snake_case					)					)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(snake_case					)
														elif i == ")":
																					# RULE 4
																					snake_case_      =				operator_stack.peek()
																					operator_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()

																					snake_case_      =				operators[opr](snake_case							,    snake_case					)
																					operand_stack.push(snake_case					)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")




