# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):
# 
# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):


import numpy as np
import torch
import sif_evaluation
from ldif.util import file_util
from ldif.util.file_util import log
def ensure_are_tensors(named_tensors):
  for name, tensor in named_tensors.items():
    if not (torch.is_tensor(tensor)):
      raise ValueError(f'Argument {name} is not a tensor, it is a {type(tensor)}')

def ensure_type(v, t, name):
  if not isinstance(v, t):
    raise ValueError(f'Error: variable {name} has type {type(v)}, not the expected type {t}')

def _load_v1_txt(path):
  """Parses a SIF V1 text file, returning numpy arrays.
  
  Args:
    path: string containing the path to the ASCII file.
    
  Returns:
    A tuple of 4 elements:
      constants: A numpy array of shape (element_count). The constant
        associated with each SIF element.
      centers: A numpy array of shape (element_count, 3). The centers of the
        SIF elements.
      radii: A numpy array of shape (element_count, 3). The axis-aligned
        radii of the gaussian falloffs.
      rotations: A numpy array of shape (element_count, 3). The euler-angle
        rotations of the SIF elements.
      symmetry_count: An integer. The number of elements which are left-right
        symmetric.
      features: A numpy array of shape (element_count, implicit_len). The LDIF
        neural features, if they are present. 
  """

# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):
# 
# Path: ldif/util/file_util.py
# def readlines(p):
# def readbin(p):
# def writebin(p, s):
# def writetxt(p, s):
# def write_np(path, arr):
# def read_grd(path):
# def read_sif_v1(path, verbose=False):
# def read_lines(p):
# def read_image(p):
# def read_npz(p):
# def read_np(p):
# def read_txt_to_np(p):
# def read_py2_pkl(p):
# def write_mesh(path, mesh):
# def read_mesh(path):
# def read_csv(path):
# def read_normals(path_to_dir, im_count=20, leading='_depth'):
# def write_points(path_ext_optional, points):
# def write_grd(path, volume, world2grid=None):
# def write_depth_image(path, depth_image):


# Path: ldif/torch/sif.py
import numpy as np
import torch
import sif_evaluation
from ldif.util import file_util
from ldif.util.file_util import log

def ensure_are_tensors(named_tensors):
  for name, tensor in named_tensors.items():
    if not (torch.is_tensor(tensor)):
      raise ValueError(f'Argument {name} is not a tensor, it is a {type(tensor)}')

def ensure_type(v, t, name):
  if not isinstance(v, t):
    raise ValueError(f'Error: variable {name} has type {type(v)}, not the expected type {t}')

def _load_v1_txt(path):
  """Parses a SIF V1 text file, returning numpy arrays.
  
  Args:
    path: string containing the path to the ASCII file.
    
  Returns:
    A tuple of 4 elements:
      constants: A numpy array of shape (element_count). The constant
        associated with each SIF element.
      centers: A numpy array of shape (element_count, 3). The centers of the
        SIF elements.
      radii: A numpy array of shape (element_count, 3). The axis-aligned
        radii of the gaussian falloffs.
      rotations: A numpy array of shape (element_count, 3). The euler-angle
        rotations of the SIF elements.
      symmetry_count: An integer. The number of elements which are left-right
        symmetric.
      features: A numpy array of shape (element_count, implicit_len). The LDIF
        neural features, if they are present. 
  """

  lines = file_util.readlines(path)