


'''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      (		UpperCamelCase__					):
			if hor == 1_2_8:
						UpperCAmelCase__  :					List[str]        =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
						UpperCAmelCase__  :					Optional[Any]        =     (3_2, 1_2_8, 2_5_6)
						UpperCAmelCase__  :					Tuple        =     ("""UpResnetBlock1D""", """UpResnetBlock1D""")

			elif hor == 3_2:
						UpperCAmelCase__  :					Optional[int]        =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
						UpperCAmelCase__  :					Optional[int]        =     (3_2, 6_4, 1_2_8, 2_5_6)
						UpperCAmelCase__  :					Tuple        =     ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
			UpperCAmelCase__  :					List[str]        =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''					)
			UpperCAmelCase__  :					List[str]        =     model.state_dict()
			UpperCAmelCase__  :					Optional[Any]        =     {
			    """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""",
			}
			UpperCAmelCase__  :					str        =     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()					)}'''					)
			UpperCAmelCase__  :					str        =     dict(zip(model.state_dict().keys() ,      hf_value_function.state_dict().keys()					)					)
			for k, v in mapping.items():
						UpperCAmelCase__  :					int        =     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      (		):
			UpperCAmelCase__  :					Optional[int]        =     {
			    """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""",
			}

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

			UpperCAmelCase__  :					str        =     dict(zip(state_dict.keys() ,      hf_value_function.state_dict().keys()					)					)
			for k, v in mapping.items():
						UpperCAmelCase__  :					Any        =     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()



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i + 1][j] + matrix[i][j]					)

			return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums					)


if __name__ == "__main__":
		print(f"""{solution() = }""")