




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=1							)			->		Optional[Any]:





  """simple docstring"""





  if n_shave_prefix_segments >= 0:
    return ".".join(path.split('.'							)[n_shave_prefix_segments:]							)
  else:
    return ".".join(path.split('.'							)[:n_shave_prefix_segments]							)


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=0							)			->		Any:





  """simple docstring"""





  _lowercase: Any						     =    []
  for old_item in old_list:
    _lowercase: int						     =    old_item
    _lowercase: Optional[int]						     =    new_item.replace('block.'					,     'resnets.'							)
    _lowercase: List[Any]						     =    new_item.replace('conv_shorcut'					,     'conv1'							)
    _lowercase: Dict						     =    new_item.replace('in_shortcut'					,     'conv_shortcut'							)
    _lowercase: Optional[int]						     =    new_item.replace('temb_proj'					,     'time_emb_proj'							)

    _lowercase: Dict						     =    shave_segments(UpperCamelCase					,     n_shave_prefix_segments=UpperCamelCase							)

    mapping.append({'old': old_item, 'new': new_item}							)

  return mapping


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase=0					,     UpperCamelCase=False							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    []
  for old_item in old_list:
    _lowercase: List[Any]						     =    old_item

    # In `model.mid`, the layer is called `attn`.
    if not in_mid:
      _lowercase: Union[str, Any]						     =    new_item.replace('attn'					,     'attentions'							)
    _lowercase: Any						     =    new_item.replace('.k.'					,     '.key.'							)
    _lowercase: int						     =    new_item.replace('.v.'					,     '.value.'							)
    _lowercase: Any						     =    new_item.replace('.q.'					,     '.query.'							)

    _lowercase: Union[str, Any]						     =    new_item.replace('proj_out'					,     'proj_attn'							)
    _lowercase: List[Any]						     =    new_item.replace('norm'					,     'group_norm'							)

    _lowercase: List[str]						     =    shave_segments(UpperCamelCase					,     n_shave_prefix_segments=UpperCamelCase							)
    mapping.append({'old': old_item, 'new': new_item}							)

  return mapping


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase=None					,     UpperCamelCase=None					,     UpperCamelCase=None							)			->		Any:





  """simple docstring"""





  assert isinstance(UpperCamelCase					,     UpperCamelCase							), "Paths should be a list of dicts containing 'old' and 'new' keys."

  if attention_paths_to_split is not None:
    if config is None:
      raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'							)

    for path, path_map in attention_paths_to_split.items():
      _lowercase: str						     =    old_checkpoint[path]
      _lowercase: Optional[Any]						     =    old_tensor.shape[0] // 3

      _lowercase: Tuple						     =    (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

      _lowercase: Optional[Any]						     =    old_tensor.shape[0] // config.get('num_head_channels'					,     1							) // 3

      _lowercase: List[str]						     =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
      _lowercase     ,					_lowercase     ,					_lowercase: str						     =    old_tensor.split(channels // num_heads					,     dim=1							)

      _lowercase: List[str]						     =    query.reshape(UpperCamelCase							).squeeze()
      _lowercase: Any						     =    key.reshape(UpperCamelCase							).squeeze()
      _lowercase: Optional[int]						     =    value.reshape(UpperCamelCase							).squeeze()

  for path in paths:
    _lowercase: Dict						     =    path['new']

    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    _lowercase: Optional[int]						     =    new_path.replace('down.'					,     'down_blocks.'							)
    _lowercase: Dict						     =    new_path.replace('up.'					,     'up_blocks.'							)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _lowercase: Union[str, Any]						     =    new_path.replace(replacement['old']					,     replacement['new']							)

    if "attentions" in new_path:
      _lowercase: Tuple						     =    old_checkpoint[path['old']].squeeze()
    else:
      _lowercase: int						     =    old_checkpoint[path['old']]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Any:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    {}

  _lowercase: List[Any]						     =    checkpoint['temb.dense.0.weight']
  _lowercase: Dict						     =    checkpoint['temb.dense.0.bias']
  _lowercase: List[str]						     =    checkpoint['temb.dense.1.weight']
  _lowercase: Optional[int]						     =    checkpoint['temb.dense.1.bias']

  _lowercase: List[str]						     =    checkpoint['norm_out.weight']
  _lowercase: Dict						     =    checkpoint['norm_out.bias']

  _lowercase: Any						     =    checkpoint['conv_in.weight']
  _lowercase: Optional[int]						     =    checkpoint['conv_in.bias']
  _lowercase: Dict						     =    checkpoint['conv_out.weight']
  _lowercase: Any						     =    checkpoint['conv_out.bias']

  _lowercase: List[str]						     =    len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'down' in layer}							)
  _lowercase: Union[str, Any]						     =    {
      layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(UpperCamelCase							)
  }

  _lowercase: Union[str, Any]						     =    len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'up' in layer}							)
  _lowercase: Optional[Any]						     =    {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(UpperCamelCase							)}

  for i in range(UpperCamelCase							):
    _lowercase: Union[str, Any]						     =    (i - 1) // (config['layers_per_block'] + 1)

    if any('downsample' in layer for layer in down_blocks[i]							):
      _lowercase: Dict						     =    checkpoint[
          F'down.{i}.downsample.op.weight'
      ]
      _lowercase: Optional[Any]						     =    checkpoint[F'down.{i}.downsample.op.bias']
    #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
    #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

    if any('block' in layer for layer in down_blocks[i]							):
      _lowercase: int						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     2							).split('.'							)[:2]							) for layer in down_blocks[i] if 'block' in layer}							)
      _lowercase: Any						     =    {
          layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
          for layer_id in range(UpperCamelCase							)
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block']							):
          _lowercase: List[Any]						     =    renew_resnet_paths(blocks[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)

    if any('attn' in layer for layer in down_blocks[i]							):
      _lowercase: List[str]						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     2							).split('.'							)[:2]							) for layer in down_blocks[i] if 'attn' in layer}							)
      _lowercase: List[Any]						     =    {
          layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
          for layer_id in range(UpperCamelCase							)
      }

      if num_attn > 0:
        for j in range(config['layers_per_block']							):
          _lowercase: Tuple						     =    renew_attention_paths(attns[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     config=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    [key for key in checkpoint if 'mid.block_1' in key]
  _lowercase: Any						     =    [key for key in checkpoint if 'mid.block_2' in key]
  _lowercase: Optional[Any]						     =    [key for key in checkpoint if 'mid.attn_1' in key]

  # Mid new 2
  _lowercase: str						     =    renew_resnet_paths(UpperCamelCase							)
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]					,     )

  _lowercase: Optional[Any]						     =    renew_resnet_paths(UpperCamelCase							)
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]					,     )

  _lowercase: int						     =    renew_attention_paths(UpperCamelCase					,     in_mid=UpperCamelCase							)
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]					,     )

  for i in range(UpperCamelCase							):
    _lowercase: Optional[int]						     =    num_up_blocks - 1 - i

    if any('upsample' in layer for layer in up_blocks[i]							):
      _lowercase: str						     =    checkpoint[
          F'up.{i}.upsample.conv.weight'
      ]
      _lowercase: int						     =    checkpoint[F'up.{i}.upsample.conv.bias']

    if any('block' in layer for layer in up_blocks[i]							):
      _lowercase: List[Any]						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     2							).split('.'							)[:2]							) for layer in up_blocks[i] if 'block' in layer}							)
      _lowercase: str						     =    {
          layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(UpperCamelCase							)
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block'] + 1							):
          _lowercase: Dict						     =    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
          _lowercase: Optional[int]						     =    renew_resnet_paths(blocks[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[replace_indices]							)

    if any('attn' in layer for layer in up_blocks[i]							):
      _lowercase: Any						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     2							).split('.'							)[:2]							) for layer in up_blocks[i] if 'attn' in layer}							)
      _lowercase: int						     =    {
          layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(UpperCamelCase							)
      }

      if num_attn > 0:
        for j in range(config['layers_per_block'] + 1							):
          _lowercase: Dict						     =    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
          _lowercase: Any						     =    renew_attention_paths(attns[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[replace_indices]							)

  _lowercase: List[str]						     =    {k.replace('mid_new_2'					,     'mid_block'							): v for k, v in new_checkpoint.items()}
  return new_checkpoint


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Tuple						     =    {}

  _lowercase: str						     =    checkpoint['encoder.norm_out.weight']
  _lowercase: Dict						     =    checkpoint['encoder.norm_out.bias']

  _lowercase: List[str]						     =    checkpoint['encoder.conv_in.weight']
  _lowercase: Optional[Any]						     =    checkpoint['encoder.conv_in.bias']
  _lowercase: Union[str, Any]						     =    checkpoint['encoder.conv_out.weight']
  _lowercase: int						     =    checkpoint['encoder.conv_out.bias']

  _lowercase: Union[str, Any]						     =    checkpoint['decoder.norm_out.weight']
  _lowercase: Tuple						     =    checkpoint['decoder.norm_out.bias']

  _lowercase: str						     =    checkpoint['decoder.conv_in.weight']
  _lowercase: Optional[Any]						     =    checkpoint['decoder.conv_in.bias']
  _lowercase: List[Any]						     =    checkpoint['decoder.conv_out.weight']
  _lowercase: List[str]						     =    checkpoint['decoder.conv_out.bias']

  _lowercase: Tuple						     =    len({'.'.join(layer.split('.'							)[:3]							) for layer in checkpoint if 'down' in layer}							)
  _lowercase: Tuple						     =    {
      layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(UpperCamelCase							)
  }

  _lowercase: Tuple						     =    len({'.'.join(layer.split('.'							)[:3]							) for layer in checkpoint if 'up' in layer}							)
  _lowercase: Any						     =    {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(UpperCamelCase							)}

  for i in range(UpperCamelCase							):
    _lowercase: List[Any]						     =    (i - 1) // (config['layers_per_block'] + 1)

    if any('downsample' in layer for layer in down_blocks[i]							):
      _lowercase: List[Any]						     =    checkpoint[
          F'encoder.down.{i}.downsample.conv.weight'
      ]
      _lowercase: Optional[Any]						     =    checkpoint[
          F'encoder.down.{i}.downsample.conv.bias'
      ]

    if any('block' in layer for layer in down_blocks[i]							):
      _lowercase: Optional[Any]						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     3							).split('.'							)[:3]							) for layer in down_blocks[i] if 'block' in layer}							)
      _lowercase: List[str]						     =    {
          layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
          for layer_id in range(UpperCamelCase							)
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block']							):
          _lowercase: List[str]						     =    renew_resnet_paths(blocks[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)

    if any('attn' in layer for layer in down_blocks[i]							):
      _lowercase: Dict						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     3							).split('.'							)[:3]							) for layer in down_blocks[i] if 'attn' in layer}							)
      _lowercase: Dict						     =    {
          layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
          for layer_id in range(UpperCamelCase							)
      }

      if num_attn > 0:
        for j in range(config['layers_per_block']							):
          _lowercase: Tuple						     =    renew_attention_paths(attns[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     config=UpperCamelCase							)

  _lowercase: Optional[int]						     =    [key for key in checkpoint if 'mid.block_1' in key]
  _lowercase: Tuple						     =    [key for key in checkpoint if 'mid.block_2' in key]
  _lowercase: Tuple						     =    [key for key in checkpoint if 'mid.attn_1' in key]

  # Mid new 2
  _lowercase: Dict						     =    renew_resnet_paths(UpperCamelCase							)
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]					,     )

  _lowercase: str						     =    renew_resnet_paths(UpperCamelCase							)
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]					,     )

  _lowercase: List[Any]						     =    renew_attention_paths(UpperCamelCase					,     in_mid=UpperCamelCase							)
  assign_to_checkpoint(
      UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]					,     )

  for i in range(UpperCamelCase							):
    _lowercase: str						     =    num_up_blocks - 1 - i

    if any('upsample' in layer for layer in up_blocks[i]							):
      _lowercase: Optional[Any]						     =    checkpoint[
          F'decoder.up.{i}.upsample.conv.weight'
      ]
      _lowercase: Tuple						     =    checkpoint[
          F'decoder.up.{i}.upsample.conv.bias'
      ]

    if any('block' in layer for layer in up_blocks[i]							):
      _lowercase: List[str]						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     3							).split('.'							)[:3]							) for layer in up_blocks[i] if 'block' in layer}							)
      _lowercase: Union[str, Any]						     =    {
          layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(UpperCamelCase							)
      }

      if num_blocks > 0:
        for j in range(config['layers_per_block'] + 1							):
          _lowercase: Any						     =    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
          _lowercase: Dict						     =    renew_resnet_paths(blocks[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[replace_indices]							)

    if any('attn' in layer for layer in up_blocks[i]							):
      _lowercase: Tuple						     =    len(
          {'.'.join(shave_segments(UpperCamelCase					,     3							).split('.'							)[:3]							) for layer in up_blocks[i] if 'attn' in layer}							)
      _lowercase: Dict						     =    {
          layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(UpperCamelCase							)
      }

      if num_attn > 0:
        for j in range(config['layers_per_block'] + 1							):
          _lowercase: Any						     =    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
          _lowercase: List[str]						     =    renew_attention_paths(attns[j]							)
          assign_to_checkpoint(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     additional_replacements=[replace_indices]							)

  _lowercase: List[Any]						     =    {k.replace('mid_new_2'					,     'mid_block'							): v for k, v in new_checkpoint.items()}
  _lowercase: Optional[Any]						     =    checkpoint['quant_conv.weight']
  _lowercase: int						     =    checkpoint['quant_conv.bias']
  if "quantize.embedding.weight" in checkpoint:
    _lowercase: Tuple						     =    checkpoint['quantize.embedding.weight']
  _lowercase: int						     =    checkpoint['post_quant_conv.weight']
  _lowercase: Optional[int]						     =    checkpoint['post_quant_conv.bias']

  return new_checkpoint


if __name__ == "__main__":
   UpperCAmelCase__						:       Optional[int]							=    argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )

   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the architecture.',
   )

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

   UpperCAmelCase__						:       Tuple							=    parser.parse_args()
   UpperCAmelCase__						:       List[str]							=    torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      UpperCAmelCase__						:       Optional[int]							=    json.loads(f.read())

   # unet case
   UpperCAmelCase__						:       Dict							=    {key.split('.')[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
      UpperCAmelCase__						:       Union[str, Any]							=    convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
      UpperCAmelCase__						:       str							=    convert_ddpm_checkpoint(checkpoint, config)

   if "ddpm" in config:
      del config["ddpm"]

   if config["_class_name"] == "VQModel":
      UpperCAmelCase__						:       int							=    VQModel(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
      UpperCAmelCase__						:       Optional[Any]							=    AutoencoderKL(**config)
      model.load_state_dict(converted_checkpoint)
      model.save_pretrained(args.dump_path)
   else:
      UpperCAmelCase__						:       Union[str, Any]							=    UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      UpperCAmelCase__						:       Dict							=    DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

      UpperCAmelCase__						:       Tuple							=    DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)






from __future__ import annotations

from pathlib import Path


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		bool:





  """simple docstring"""





  _lowercase: tuple[int, int]						     =    (xa, ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: float						     =    -vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)
  _lowercase: float						     =    +vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)

  return a > 0 and b > 0 and a + b < 1


def 		_lowerCamelCase				(					UpperCamelCase = "p102_triangles.txt"							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    Path(UpperCamelCase							).parent.joinpath(UpperCamelCase							).read_text(encoding='utf-8'							)

  _lowercase: list[list[int]]						     =    []
  for line in data.strip().split('\n'							):
    triangles.append([int(UpperCamelCase							) for number in line.split(','							)]							)

  _lowercase: int						     =    0
  _lowercase: list[int]

  for triangle in triangles:
    ret += contains_origin(*UpperCamelCase							)

  return ret


if __name__ == "__main__":
   print(f'''{solution() = }''')
