





"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->List[str]:





  """simple docstring"""


  return sorted(_lowercase  ,			key=lambda UpperCAmelCase_  : x[column]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->Union[str, Any]:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			_lowercase  ):
      __UpperCAmelCase    :						str       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						List[str]       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->Dict:





  """simple docstring"""


  for i in range(min(6  ,			points_counts - 1  )  ,			_lowercase  ):
    for j in range(max(0  ,			i - 6  )  ,			_lowercase  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Any       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


  if points_counts <= 3:
    return dis_between_closest_pair(_lowercase  ,			_lowercase  )

  # recursion
  __UpperCAmelCase    :						Optional[int]       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      _lowercase  ,			points_sorted_on_y[:mid]  ,			_lowercase  )
  __UpperCAmelCase    :						Optional[Any]       =						closest_pair_of_points_sqr(
      _lowercase  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						int       =						min(_lowercase  ,			_lowercase  )
  __UpperCAmelCase    :						List[str]       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(_lowercase  )

  __UpperCAmelCase    :						Any       =						dis_between_closest_in_strip(
      _lowercase  ,			len(_lowercase  )  ,			_lowercase  )
  return min(_lowercase  ,			_lowercase  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(_lowercase  ,			column=0  )
  __UpperCAmelCase    :						str       =						column_based_sort(_lowercase  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          _lowercase  ,			_lowercase  ,			_lowercase  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:List[str]				   =       [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print('Distance:', closest_pair_of_points(points, len(points)))






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						DDIMScheduler(
      timesteps=config.model.params.timesteps  ,			beta_schedule='''scaled_linear'''  ,			beta_start=config.model.params.linear_start  ,			beta_end=config.model.params.linear_end  ,			clip_sample=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  lowercase__				:Dict				   =       parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)