


'''simple docstring'''


from __future__ import annotations







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							TypeError(
              "Matrices must be formed from a list of zero or more lists containing at "
              "least one and the same number of values, each of which must be of type "
              "int or float."		)
          if len(lowerCamelCase		) != 0:
               __A	:       Optional[int]      =							len(rows[0]		)
               if cols == 0:
                    raise error
               for row in rows:
                    if len(lowerCamelCase		) != cols:
                         raise error
                    for value in row:
                         if not isinstance(lowerCamelCase			,     (int, float)		):
                              raise error
               __A	:       Tuple      =							rows
          else:
               __A	:       List[str]      =							[]


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return [[row[i] for row in self.rows] for i in range(len(self.rows[0]		)		)]


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return len(self.rows		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return len(self.rows[0]		)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return (self.num_rows, self.num_columns)


     @property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return self.order[0] == self.order[1]


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							[
              [0 if column_num != row_num else 1 for column_num in range(self.num_rows		)]
              for row_num in range(self.num_rows		)
          ]
          return Matrix(lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          if not self.is_square:
               return 0
          if self.order == (0, 0):
               return 1
          if self.order == (1, 1):
               return int(self.rows[0][0]		)
          if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])		)
          else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns		)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return bool(self.determinant()		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Dict      =							[
              [
                  self.rows[other_row][other_column]
                  for other_column in range(self.num_columns		)
                  if other_column != column
              ]
              for other_row in range(self.num_rows		)
              if other_row != row
          ]
          return Matrix(lowerCamelCase		).determinant()


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          if (row + column) % 2 == 0:
               return self.get_minor(lowerCamelCase			,     lowerCamelCase		)
          return -1 * self.get_minor(lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return Matrix(
              [
                  [self.get_minor(lowerCamelCase			,     lowerCamelCase		) for column in range(self.num_columns		)]
                  for row in range(self.num_rows		)
              ]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return Matrix(
              [
                  [
                      self.minors().rows[row][column]
                      if (row + column) % 2 == 0
                      else self.minors().rows[row][column] * -1
                      for column in range(self.minors().num_columns		)
                  ]
                  for row in range(self.minors().num_rows		)
              ]		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							[
              [self.cofactors().rows[column][row] for column in range(self.num_columns		)]
              for row in range(self.num_rows		)
          ]
          return Matrix(lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							self.determinant()
          if not determinant:
               raise TypeError("Only matrices with a non-zero determinant have an inverse"		)
          return self.adjugate() * (1 / determinant)


     def __repr__(   self		):


          '''simple docstring'''





          return str(self.rows		)


     def __str__(   self		):


          '''simple docstring'''





          if self.num_rows == 0:
               return "[]"
          if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]		)		) + "]]"
          return (
              "["
              + "\n ".join(
                  [
                      "[" + ". ".join([str(lowerCamelCase		) for value in row]		) + ".]"
                      for row in self.rows
                  ]		)
              + "]"
          )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          __A	:       Tuple      =							TypeError("Row must be a list containing all ints and/or floats"		)
          if not isinstance(lowerCamelCase			,     lowerCamelCase		):
               raise type_error
          for value in row:
               if not isinstance(lowerCamelCase			,     (int, float)		):
                    raise type_error
          if len(lowerCamelCase		) != self.num_columns:
               raise ValueError(
                   "Row must be equal in length to the other rows in the matrix"		)
          if position is None:
               self.rows.append(lowerCamelCase		)
          else:
               __A	:       Any      =							self.rows[0:position] + [row] + self.rows[position:]


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None		):


          '''simple docstring'''





          __A	:       List[str]      =							TypeError(
              "Column must be a list containing all ints and/or floats"		)
          if not isinstance(lowerCamelCase			,     lowerCamelCase		):
               raise type_error
          for value in column:
               if not isinstance(lowerCamelCase			,     (int, float)		):
                    raise type_error
          if len(lowerCamelCase		) != self.num_rows:
               raise ValueError(
                   "Column must be equal in length to the other columns in the matrix"		)
          if position is None:
               __A	:       Tuple      =							[self.rows[i] + [column[i]] for i in range(self.num_rows		)]
          else:
               __A	:       List[Any]      =							[
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows		)
               ]


     def __eq__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          if not isinstance(lowerCamelCase			,     lowerCamelCase		):
               return NotImplemented
          return self.rows == other.rows


     def __ne__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          return not self == other


     def __neg__(   self		):


          '''simple docstring'''





          return self * -1


     def __add__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          if self.order != other.order:
               raise ValueError("Addition requires matrices of the same order"		)
          return Matrix(
              [
                  [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns		)]
                  for i in range(self.num_rows		)
              ]		)


     def __sub__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          if self.order != other.order:
               raise ValueError("Subtraction requires matrices of the same order"		)
          return Matrix(
              [
                  [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns		)]
                  for i in range(self.num_rows		)
              ]		)


     def __mul__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          if isinstance(lowerCamelCase			,     (int, float)		):
               return Matrix(
                   [[int(element * other		) for element in row] for row in self.rows]		)
          elif isinstance(lowerCamelCase			,     lowerCamelCase		):
               if self.num_columns != other.num_rows:
                    raise ValueError(
                        "The number of columns in the first matrix must "
                        "be equal to the number of rows in the second"		)
               return Matrix(
                   [
                       [Matrix.dot_product(lowerCamelCase			,     lowerCamelCase		) for column in other.columns()]
                       for row in self.rows
                   ]		)
          else:
               raise TypeError(
                   "A Matrix can only be multiplied by an int, float, or another matrix"		)


     def __pow__(   self			,     lowerCamelCase		):


          '''simple docstring'''





          if not isinstance(lowerCamelCase			,     lowerCamelCase		):
               raise TypeError("A Matrix can only be raised to the power of an int"		)
          if not self.is_square:
               raise ValueError("Only square matrices can be raised to a power"		)
          if other == 0:
               return self.identity()
          if other < 0:
               if self.is_invertable():
                    return self.inverse() ** (-other)
               raise ValueError(
                   "Only invertable matrices can be raised to a negative power"		)
          __A	:       int      =							self
          for _ in range(other - 1		):
               result *= self
          return result


     @classmethod
     def lowerCAmelCase__  (   cls			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          return sum(row[i] * column[i] for i in range(len(lowerCamelCase		)		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





'''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 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       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": 6_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_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()							)}"							)
     __A	:       Dict      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "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",
     }

     __A	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_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()							)}"							)

     __A	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


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

