"""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__     :Dict		) ->    Dict:
      '''simple docstring'''


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

      elif hor == 3_2:
            lowercase														=	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
            lowercase														=	(3_2, 6_4, 1_2_8, 2_5_6)
            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""": 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""",
      }
      lowercase														=	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()		)}'		)
      lowercase														=	dict(zip(model.state_dict().keys()					,		hf_value_function.state_dict().keys()		)		)
      for k, v in mapping.items():
            lowercase														=	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 			UpperCAmelCase__       (				) ->    Union[str, Any]:
      '''simple docstring'''


      lowercase														=	{
          """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""",
      }

      lowercase														=	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""		)
      lowercase														=	model
      lowercase														=	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()		)}'		)

      lowercase														=	dict(zip(state_dict.keys()					,		hf_value_function.state_dict().keys()		)		)
      for k, v in mapping.items():
            lowercase														=	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(3_2)
       # unet(128)
       value_function()




"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

__lowerCAmelCase    :       str					     =list[list[float | int]]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Matrix					,		lowerCAmelCase__     :Matrix		) ->    Matrix:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(size + 1		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for row in range(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	matrix[row][col]

            lowercase														=	vector[row][0]

      lowercase														=	0
      lowercase														=	0
      while row < size and col < size:
            # pivoting
            lowercase														=	max((abs(augmented[rowa][col]		), rowa) for rowa in range(lowerCAmelCase__					,		lowerCAmelCase__		)		)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowercase							,							lowercase														=	augmented[pivot_row], augmented[row]

            for rowa in range(row + 1					,		lowerCAmelCase__		):
                  lowercase														=	augmented[rowa][col] / augmented[row][col]
                  lowercase														=	0
                  for cola in range(col + 1					,		size + 1		):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1					,		lowerCAmelCase__		):
            for row in range(lowerCAmelCase__		):
                  lowercase														=	augmented[row][col] / augmented[col][col]
                  for cola in range(lowerCAmelCase__					,		size + 1		):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]					,		1_0		)] for row in range(lowerCAmelCase__		)
      ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[int]		) ->    Callable[[int], int]:
      '''simple docstring'''


      lowercase														=	len(lowerCAmelCase__		)
      lowercase														=	[[0 for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]
      lowercase														=	[[0] for _ in range(lowerCAmelCase__		)]
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42
      lowercase														=	42

      for x_val, y_val in enumerate(lowerCAmelCase__		):
            for col in range(lowerCAmelCase__		):
                  lowercase														=	(x_val + 1) ** (size - col - 1)
            lowercase														=	y_val

      lowercase														=	solve(lowerCAmelCase__					,		lowerCAmelCase__		)

      def interpolated_func(lowerCAmelCase__     :int		) -> int:
            return sum(
                round(coeffs[x_val][0]		) * (var ** (size - x_val - 1))
                for x_val in range(lowerCAmelCase__		)		)

      return interpolated_func


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Callable[[int], int] = question_function					,		lowerCAmelCase__     :int = 1_0		) ->    int:
      '''simple docstring'''


      lowercase														=	[func(lowerCAmelCase__		) for x_val in range(1					,		order + 1		)]

      lowercase														=	[
          interpolate(data_points[:max_coeff]		) for max_coeff in range(1					,		order + 1		)
      ]

      lowercase														=	0
      lowercase														=	42
      lowercase														=	42

      for poly in polynomials:
            lowercase														=	1
            while func(lowerCAmelCase__		) == poly(lowerCAmelCase__		):
                  x_val += 1

            ret += poly(lowerCAmelCase__		)

      return ret


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



