
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Any      ,      a							: Collection[float] | None = None	)							->       None:

				"""simple docstring"""

				if components is None:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       []
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       list(a	)
			def __len__(			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return len(self.__components	)
			def __str__(			self							: str	)							->       str:

				"""simple docstring"""

				return "(" + ",".join(map(a      ,      self.__components	)	) + ")"
			def __add__(			self							: Optional[int]      ,      a							: Vector	)							->       Vector:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       len(self	)
				if size == len(a	):
					SCREAMING_SNAKE_CASE   :      Any				     =       [self.__components[i] + other.component(a	) for i in range(a	)]
					return Vector(a	)
				else:
					raise Exception("must have the same size"	)
			def __sub__(			self							: Optional[int]      ,      a							: Vector	)							->       Vector:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       len(self	)
				if size == len(a	):
					SCREAMING_SNAKE_CASE   :      int				     =       [self.__components[i] - other.component(a	) for i in range(a	)]
					return Vector(a	)
				else:  # error case
					raise Exception("must have the same size"	)
			@overload
			def __mul__(			self							: Optional[Any]      ,      a							: float	)							->       Vector:

				"""simple docstring"""

				...
			@overload
			def __mul__(			self							: Optional[int]      ,      a							: Vector	)							->       float:

				"""simple docstring"""

				...
			def __mul__(			self							: Any      ,      a							: float | Vector	)							->       float | Vector:

				"""simple docstring"""

				if isinstance(a      ,      (float, int)	):
					SCREAMING_SNAKE_CASE   :      int				     =       [c * other for c in self.__components]
					return Vector(a	)
				elif isinstance(a      ,      a	) and len(self	) == len(a	):
					SCREAMING_SNAKE_CASE   :      str				     =       len(self	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       [self.__components[i] * other.component(a	) for i in range(a	)]
					return sum(a	)
				else:  # error case
					raise Exception("invalid operand!"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       Vector:

				"""simple docstring"""

				return Vector(self.__components	)
			def 							__UpperCamelCase (			self							: Any      ,      a							: int	)							->       float:

				"""simple docstring"""

				if isinstance(a      ,      a	) and -len(self.__components	) <= i < len(self.__components	):
					return self.__components[i]
				else:
					raise Exception("index out of range"	)
			def 							__UpperCamelCase (			self							: int      ,      a							: int      ,      a							: float	)							->       None:

				"""simple docstring"""

				assert -len(self.__components	) <= pos < len(self.__components	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       value
			def 							__UpperCamelCase (			self							: List[Any]	)							->       float:

				"""simple docstring"""

				if len(self.__components	) == 0:
					raise Exception("Vector is empty"	)
				SCREAMING_SNAKE_CASE   :      int				     =       [c**2 for c in self.__components]
				return math.sqrt(sum(a	)	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Vector      ,      a							: bool = False	)							->       float:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self * other
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.euclidean_length() * other.euclidean_length()
				if deg:
					return math.degrees(math.acos(num / den	)	)
				else:
					return math.acos(num / den	)

def  lowerCamelCase__ (					_a):
	assert isinstance(_a							,			_a)
	return Vector([0] * dimension)

def  lowerCamelCase__ (					_a							,			_a):
	assert isinstance(_a							,			_a) and (isinstance(_a							,			_a))
	SCREAMING_SNAKE_CASE   :      int				     =       [0] * dimension
	SCREAMING_SNAKE_CASE   :      str				     =       1
	return Vector(_a)

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	assert (
	    isinstance(_a							,			_a)
	    and isinstance(_a							,			_a)
	    and (isinstance(_a							,			(int, float)))
	)
	return x * scalar + y

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	random.seed(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       [random.randint(_a							,			_a) for _ in range(_a)]
	return Vector(_a)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: list[list[float]]      ,      a							: int      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       matrix
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       w
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       h
			def __str__(			self							: str	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       ""
				for i in range(self.__height	):
					ans += "|"
					for j in range(self.__width	):
						if j < self.__width - 1:
							ans += str(self.__matrix[i][j]	) + ","
						else:
							ans += str(self.__matrix[i][j]	) + "|\n"
				return ans
			def __add__(			self							: Tuple      ,      a							: Matrix	)							->       Matrix:

				"""simple docstring"""

				if self.__width == other.width() and self.__height == other.height():
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
					for i in range(self.__height	):
						SCREAMING_SNAKE_CASE   :      int				     =       [
						    self.__matrix[i][j] + other.component(a      ,      a	)
						    for j in range(self.__width	)
						]
						matrix.append(a	)
					return Matrix(a      ,      self.__width      ,      self.__height	)
				else:
					raise Exception("matrix must have the same dimension!"	)
			def __sub__(			self							: str      ,      a							: Matrix	)							->       Matrix:

				"""simple docstring"""

				if self.__width == other.width() and self.__height == other.height():
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       []
					for i in range(self.__height	):
						SCREAMING_SNAKE_CASE   :      str				     =       [
						    self.__matrix[i][j] - other.component(a      ,      a	)
						    for j in range(self.__width	)
						]
						matrix.append(a	)
					return Matrix(a      ,      self.__width      ,      self.__height	)
				else:
					raise Exception("matrices must have the same dimension!"	)
			@overload
			def __mul__(			self							: Tuple      ,      a							: float	)							->       Matrix:

				"""simple docstring"""

				...
			@overload
			def __mul__(			self							: Optional[Any]      ,      a							: Vector	)							->       Vector:

				"""simple docstring"""

				...
			def __mul__(			self							: int      ,      a							: float | Vector	)							->       Vector | Matrix:

				"""simple docstring"""

				if isinstance(a      ,      a	):  # matrix-vector
					if len(a	) == self.__width:
						SCREAMING_SNAKE_CASE   :      int				     =       zero_vector(self.__height	)
						for i in range(self.__height	):
							SCREAMING_SNAKE_CASE   :      Tuple				     =       [
							    self.__matrix[i][j] * other.component(a	)
							    for j in range(self.__width	)
							]
							ans.change_component(a      ,      sum(a	)	)
						return ans
					else:
						raise Exception(
						    "vector must have the same size as the "
						    "number of columns of the matrix!"	)
				elif isinstance(a      ,      (int, float)	):  # matrix-scalar
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [
					    [self.__matrix[i][j] * other for j in range(self.__width	)]
					    for i in range(self.__height	)
					]
					return Matrix(a      ,      self.__width      ,      self.__height	)
				return None
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return self.__height
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       int:

				"""simple docstring"""

				return self.__width
			def 							__UpperCamelCase (			self							: int      ,      a							: int      ,      a							: int	)							->       float:

				"""simple docstring"""

				if 0 <= x < self.__height and 0 <= y < self.__width:
					return self.__matrix[x][y]
				else:
					raise Exception("change_component: indices out of bounds"	)
			def 							__UpperCamelCase (			self							: int      ,      a							: int      ,      a							: int      ,      a							: float	)							->       None:

				"""simple docstring"""

				if 0 <= x < self.__height and 0 <= y < self.__width:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       value
				else:
					raise Exception("change_component: indices out of bounds"	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: int      ,      a							: int	)							->       float:

				"""simple docstring"""

				if self.__height != self.__width:
					raise Exception("Matrix is not square"	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.__matrix[:x] + self.__matrix[x + 1 :]
				for i in range(len(a	)	):
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       minor[i][:y] + minor[i][y + 1 :]
				return Matrix(a      ,      self.__width - 1      ,      self.__height - 1	).determinant()
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: int      ,      a							: int	)							->       float:

				"""simple docstring"""

				if self.__height != self.__width:
					raise Exception("Matrix is not square"	)
				if 0 <= x < self.__height and 0 <= y < self.__width:
					return (-1) ** (x + y) * self.minor(a      ,      a	)
				else:
					raise Exception("Indices out of bounds"	)






			def 							__UpperCamelCase (			self							: Optional[int]	)							->       float:

				"""simple docstring"""

				if self.__height != self.__width:
					raise Exception("Matrix is not square"	)
				if self.__height < 1:
					raise Exception("Matrix has no element"	)
				elif self.__height == 1:
					return self.__matrix[0][0]
				elif self.__height == 2:
					return (
					    self.__matrix[0][0] * self.__matrix[1][1]
					    - self.__matrix[0][1] * self.__matrix[1][0]
					)
				else:
					SCREAMING_SNAKE_CASE   :      List[str]				     =       [
					    self.__matrix[0][y] * self.cofactor(0      ,      a	) for y in range(self.__width	)
					]
					return sum(a	)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      list[list[float]]				     =       [[0] * n for _ in range(_a)]
	return Matrix(_a							,			_a							,			_a)


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	random.seed(_a)
	SCREAMING_SNAKE_CASE   :      list[list[float]]				     =       [
	    [random.randint(_a							,			_a) for _ in range(_a)] for _ in range(_a)
	]
	return Matrix(_a							,			_a							,			_a)


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  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "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",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)


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