




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_ : Union[str, Any]       ):
				'''simple docstring'''
				if hor == 128:
								_lowerCAmelCase    :    Any										=      ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
								_lowerCAmelCase    :    Dict										=      (32, 128, 256)
								_lowerCAmelCase    :    str										=      ("""UpResnetBlock1D""", """UpResnetBlock1D""")

				elif hor == 32:
								_lowerCAmelCase    :    Dict										=      ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
								_lowerCAmelCase    :    List[Any]										=      (32, 64, 128, 256)
								_lowerCAmelCase    :    List[Any]										=      ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
				_lowerCAmelCase    :    List[Any]										=      torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"       )
				_lowerCAmelCase    :    str										=      model.state_dict()
				_lowerCAmelCase    :    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""": 14,
				    """out_channels""": 14,
				    """extra_in_channels""": 0,
				    """time_embedding_type""": """positional""",
				    """flip_sin_to_cos""": False,
				    """freq_shift""": 1,
				    """sample_size""": 65536,
				    """mid_block_type""": """MidResTemporalBlock1D""",
				    """act_fn""": """mish""",
				}
				_lowerCAmelCase    :    Tuple										=      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()       )}"       )
				_lowerCAmelCase    :    Union[str, Any]										=      dict(zip(model.state_dict().keys()      ,    hf_value_function.state_dict().keys()       )       )
				for k, v in mapping.items():
								_lowerCAmelCase    :    List[str]										=      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'''
				_lowerCAmelCase    :    str										=      {
				    """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""": 65536,
				    """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    :    Dict										=      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()       )}"       )

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







from __future__ import annotations

from decimal import Decimal

from numpy import array




def 							_UpperCAmelCase  (UpperCamelCase_ : list[list[float]]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      Decimal

				# Check if the provided matrix has 2 rows and 2 columns
				# since this implementation only works for 2x2 matrices
				if len(UpperCamelCase_       ) == 2 and len(matrix[0]       ) == 2 and len(matrix[1]       ) == 2:
								# Calculate the determinant of the matrix
								_lowerCAmelCase    :    Optional[Any]										=      float(
								    d(matrix[0][0]       ) * d(matrix[1][1]       ) - d(matrix[1][0]       ) * d(matrix[0][1]       )       )
								if determinant == 0:
												raise ValueError("""This matrix has no inverse."""       )

								# Creates a copy of the matrix with swapped positions of the elements
								_lowerCAmelCase    :    Union[str, Any]										=      [[0.0, 0.0], [0.0, 0.0]]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      matrix[1][1], matrix[0][0]
								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      -matrix[1][0], -matrix[0][1]

								# Calculate the inverse of the matrix
								return [
								    [(float(d(UpperCamelCase_       )       ) / determinant) or 0.0 for n in row] for row in swapped_matrix
								]
				elif (
				    len(UpperCamelCase_       ) == 3
				    and len(matrix[0]       ) == 3
				    and len(matrix[1]       ) == 3
				    and len(matrix[2]       ) == 3
				):
								# Calculate the determinant of the matrix using Sarrus rule
								_lowerCAmelCase    :    List[str]										=      float(
								    (
								        (d(matrix[0][0]       ) * d(matrix[1][1]       ) * d(matrix[2][2]       ))
								        + (d(matrix[0][1]       ) * d(matrix[1][2]       ) * d(matrix[2][0]       ))
								        + (d(matrix[0][2]       ) * d(matrix[1][0]       ) * d(matrix[2][1]       ))
								    )
								    - (
								        (d(matrix[0][2]       ) * d(matrix[1][1]       ) * d(matrix[2][0]       ))
								        + (d(matrix[0][1]       ) * d(matrix[1][0]       ) * d(matrix[2][2]       ))
								        + (d(matrix[0][0]       ) * d(matrix[1][2]       ) * d(matrix[2][1]       ))
								    )       )
								if determinant == 0:
												raise ValueError("""This matrix has no inverse."""       )

								# Creating cofactor matrix
								_lowerCAmelCase    :    Optional[Any]										=      [
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								    [d(0.0       ), d(0.0       ), d(0.0       )],
								]
								_lowerCAmelCase    :    Optional[Any]										=      (d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
								    d(matrix[1][2]       ) * d(matrix[2][1]       )
								)
								_lowerCAmelCase    :    str										=      -(
								    (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
								)
								_lowerCAmelCase    :    Any										=      (d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
								    d(matrix[1][1]       ) * d(matrix[2][0]       )
								)
								_lowerCAmelCase    :    List[str]										=      -(
								    (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
								)
								_lowerCAmelCase    :    Tuple										=      (d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
								    d(matrix[0][2]       ) * d(matrix[2][0]       )
								)
								_lowerCAmelCase    :    Any										=      -(
								    (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
								)
								_lowerCAmelCase    :    int										=      (d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
								    d(matrix[0][2]       ) * d(matrix[1][1]       )
								)
								_lowerCAmelCase    :    Optional[Any]										=      -(
								    (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
								)
								_lowerCAmelCase    :    Tuple										=      (d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
								    d(matrix[0][1]       ) * d(matrix[1][0]       )
								)

								# Transpose the cofactor matrix (Adjoint matrix)
								_lowerCAmelCase    :    List[str]										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																_lowerCAmelCase    :    List[Any]										=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								_lowerCAmelCase    :    Tuple										=      array(UpperCamelCase_       )
								for i in range(3       ):
												for j in range(3       ):
																inverse_matrix[i][j] /= d(UpperCamelCase_       )

        # Calculate the inverse of the matrix
								return [[float(d(UpperCamelCase_       )       ) or 0.0 for n in row] for row in inverse_matrix]
				raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""       )

