






"""simple docstring"""




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__=1				):



  '''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				):



  '''simple docstring'''
  _a  :       Any             =	[]
  for old_item in old_list:
    _a  :       Optional[Any]             =	old_item.replace("""in_layers.0"""     ,   """norm1"""				)
    _a  :       Optional[int]             =	new_item.replace("""in_layers.2"""     ,   """conv1"""				)

    _a  :       Any             =	new_item.replace("""out_layers.0"""     ,   """norm2"""				)
    _a  :       str             =	new_item.replace("""out_layers.3"""     ,   """conv2"""				)

    _a  :       Tuple             =	new_item.replace("""emb_layers.1"""     ,   """time_emb_proj"""				)
    _a  :       Dict             =	new_item.replace("""skip_connection"""     ,   """conv_shortcut"""				)

    _a  :       Dict             =	shave_segments(UpperCamelCase__     ,   n_shave_prefix_segments=UpperCamelCase__				)

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

  return mapping

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__=0				):



  '''simple docstring'''
  _a  :       Any             =	[]
  for old_item in old_list:
    _a  :       Tuple             =	old_item

    _a  :       Dict             =	new_item.replace("""norm.weight"""     ,   """group_norm.weight"""				)
    _a  :       Tuple             =	new_item.replace("""norm.bias"""     ,   """group_norm.bias"""				)

    _a  :       Optional[int]             =	new_item.replace("""proj_out.weight"""     ,   """proj_attn.weight"""				)
    _a  :       List[str]             =	new_item.replace("""proj_out.bias"""     ,   """proj_attn.bias"""				)

    _a  :       Any             =	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				):



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

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      _a  :       Union[str, Any]             =	old_checkpoint[path]
      _a  :       int             =	old_tensor.shape[0] // 3

      _a  :       Optional[int]             =	(-1, channels) if len(old_tensor.shape				) == 3 else (-1)

      _a  :       str             =	old_tensor.shape[0] // config["""num_head_channels"""] // 3

      _a  :       int             =	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
      _a      ,  _a      ,  _a  :       Union[str, Any]             =	old_tensor.split(channels // num_heads     ,   dim=1				)

      _a  :       List[Any]             =	query.reshape(UpperCamelCase__				)
      _a  :       Tuple             =	key.reshape(UpperCamelCase__				)
      _a  :       Tuple             =	value.reshape(UpperCamelCase__				)

  for path in paths:
    _a  :       Tuple             =	path["""new"""]

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    _a  :       str             =	new_path.replace("""middle_block.0"""     ,   """mid_block.resnets.0"""				)
    _a  :       Union[str, Any]             =	new_path.replace("""middle_block.1"""     ,   """mid_block.attentions.0"""				)
    _a  :       int             =	new_path.replace("""middle_block.2"""     ,   """mid_block.resnets.1"""				)

    if additional_replacements is not None:
      for replacement in additional_replacements:
        _a  :       Optional[int]             =	new_path.replace(replacement["""old"""]     ,   replacement["""new"""]				)

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      _a  :       int             =	old_checkpoint[path["""old"""]][:, :, 0]
    else:
      _a  :       Optional[int]             =	old_checkpoint[path["""old"""]]

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	{}

  _a  :       int             =	checkpoint["""time_embed.0.weight"""]
  _a  :       Optional[Any]             =	checkpoint["""time_embed.0.bias"""]
  _a  :       Dict             =	checkpoint["""time_embed.2.weight"""]
  _a  :       List[Any]             =	checkpoint["""time_embed.2.bias"""]

  _a  :       Dict             =	checkpoint["""input_blocks.0.0.weight"""]
  _a  :       List[str]             =	checkpoint["""input_blocks.0.0.bias"""]

  _a  :       int             =	checkpoint["""out.0.weight"""]
  _a  :       str             =	checkpoint["""out.0.bias"""]
  _a  :       int             =	checkpoint["""out.2.weight"""]
  _a  :       Optional[int]             =	checkpoint["""out.2.bias"""]

  # Retrieves the keys for the input blocks only
  _a  :       int             =	len({""".""".join(layer.split("""."""				)[:2]				) for layer in checkpoint if """input_blocks""" in layer}				)
  _a  :       List[Any]             =	{
      layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
      for layer_id in range(UpperCamelCase__				)
  }

  # Retrieves the keys for the middle blocks only
  _a  :       Union[str, Any]             =	len({""".""".join(layer.split("""."""				)[:2]				) for layer in checkpoint if """middle_block""" in layer}				)
  _a  :       Optional[int]             =	{
      layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
      for layer_id in range(UpperCamelCase__				)
  }

  # Retrieves the keys for the output blocks only
  _a  :       Optional[Any]             =	len({""".""".join(layer.split("""."""				)[:2]				) for layer in checkpoint if """output_blocks""" in layer}				)
  _a  :       List[str]             =	{
      layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
      for layer_id in range(UpperCamelCase__				)
  }

  for i in range(1     ,   UpperCamelCase__				):
    _a  :       int             =	(i - 1) // (config["""num_res_blocks"""] + 1)
    _a  :       Optional[Any]             =	(i - 1) % (config["""num_res_blocks"""] + 1)

    _a  :       Dict             =	[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
    _a  :       List[str]             =	[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

    if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
      _a  :       Any             =	checkpoint[
          F"""input_blocks.{i}.0.op.weight"""
      ]
      _a  :       int             =	checkpoint[
          F"""input_blocks.{i}.0.op.bias"""
      ]
      continue

    _a  :       Union[str, Any]             =	renew_resnet_paths(UpperCamelCase__				)
    _a  :       List[Any]             =	{"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
    _a  :       Union[str, Any]             =	{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
    assign_to_checkpoint(
        UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   additional_replacements=[meta_path, resnet_op]     ,   config=UpperCamelCase__				)

    if len(UpperCamelCase__				):
      _a  :       str             =	renew_attention_paths(UpperCamelCase__				)
      _a  :       List[str]             =	{
          """old""": F"""input_blocks.{i}.1""",
          """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
      }
      _a  :       Optional[Any]             =	{
          F"""input_blocks.{i}.1.qkv.bias""": {
              """key""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
              """query""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
              """value""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
          },
          F"""input_blocks.{i}.1.qkv.weight""": {
              """key""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
              """query""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
              """value""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
          },
      }
      assign_to_checkpoint(
          UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   additional_replacements=[meta_path]     ,   attention_paths_to_split=UpperCamelCase__     ,   config=UpperCamelCase__     ,   )

  _a  :       List[str]             =	middle_blocks[0]
  _a  :       int             =	middle_blocks[1]
  _a  :       Union[str, Any]             =	middle_blocks[2]

  _a  :       Tuple             =	renew_resnet_paths(UpperCamelCase__				)
  assign_to_checkpoint(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   config=UpperCamelCase__				)

  _a  :       Optional[int]             =	renew_resnet_paths(UpperCamelCase__				)
  assign_to_checkpoint(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   config=UpperCamelCase__				)

  _a  :       List[Any]             =	renew_attention_paths(UpperCamelCase__				)
  _a  :       Dict             =	{
      """middle_block.1.qkv.bias""": {
          """key""": """mid_block.attentions.0.key.bias""",
          """query""": """mid_block.attentions.0.query.bias""",
          """value""": """mid_block.attentions.0.value.bias""",
      },
      """middle_block.1.qkv.weight""": {
          """key""": """mid_block.attentions.0.key.weight""",
          """query""": """mid_block.attentions.0.query.weight""",
          """value""": """mid_block.attentions.0.value.weight""",
      },
  }
  assign_to_checkpoint(
      UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   attention_paths_to_split=UpperCamelCase__     ,   config=UpperCamelCase__				)

  for i in range(UpperCamelCase__				):
    _a  :       Any             =	i // (config["""num_res_blocks"""] + 1)
    _a  :       int             =	i % (config["""num_res_blocks"""] + 1)
    _a  :       Any             =	[shave_segments(UpperCamelCase__     ,   2				) for name in output_blocks[i]]
    _a  :       Any             =	{}

    for layer in output_block_layers:
      _a      ,  _a  :       Any             =	layer.split("""."""				)[0], shave_segments(UpperCamelCase__     ,   1				)
      if layer_id in output_block_list:
        output_block_list[layer_id].append(UpperCamelCase__				)
      else:
        _a  :       Optional[Any]             =	[layer_name]

    if len(UpperCamelCase__				) > 1:
      _a  :       int             =	[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
      _a  :       Union[str, Any]             =	[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

      _a  :       Optional[Any]             =	renew_resnet_paths(UpperCamelCase__				)
      _a  :       Any             =	renew_resnet_paths(UpperCamelCase__				)

      _a  :       List[str]             =	{"""old""": F"""output_blocks.{i}.0""", """new""": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
      assign_to_checkpoint(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   additional_replacements=[meta_path]     ,   config=UpperCamelCase__				)

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        _a  :       int             =	list(output_block_list.values()				).index(["""conv.weight""", """conv.bias"""]				)
        _a  :       int             =	checkpoint[
            F"""output_blocks.{i}.{index}.conv.weight"""
        ]
        _a  :       Optional[int]             =	checkpoint[
            F"""output_blocks.{i}.{index}.conv.bias"""
        ]

        # Clear attentions as they have been attributed above.
        if len(UpperCamelCase__				) == 2:
          _a  :       Dict             =	[]

      if len(UpperCamelCase__				):
        _a  :       Tuple             =	renew_attention_paths(UpperCamelCase__				)
        _a  :       Tuple             =	{
            """old""": F"""output_blocks.{i}.1""",
            """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
        }
        _a  :       Optional[int]             =	{
            F"""output_blocks.{i}.1.qkv.bias""": {
                """key""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                """query""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                """value""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
            },
            F"""output_blocks.{i}.1.qkv.weight""": {
                """key""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                """query""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                """value""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
            },
        }
        assign_to_checkpoint(
            UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   additional_replacements=[meta_path]     ,   attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions				) else None     ,   config=UpperCamelCase__     ,   )
    else:
      _a  :       Union[str, Any]             =	renew_resnet_paths(UpperCamelCase__     ,   n_shave_prefix_segments=1				)
      for path in resnet_0_paths:
        _a  :       List[Any]             =	""".""".join(["""output_blocks""", str(UpperCamelCase__				), path["""old"""]]				)
        _a  :       Optional[int]             =	""".""".join(["""up_blocks""", str(UpperCamelCase__				), """resnets""", str(UpperCamelCase__				), path["""new"""]]				)

        _a  :       List[str]             =	checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
 _snake_case          =   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.')

 _snake_case          =   parser.parse_args()

 _snake_case          =   torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _snake_case          =   json.loads(f.read())

 _snake_case          =   convert_ldm_checkpoint(checkpoint, config)

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

 _snake_case          =   UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _snake_case          =   DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  _snake_case          =   VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

  _snake_case          =   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)









"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[str]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
    if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
      return True
  return False

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
        _a  :       Any             =	distance[u] + w

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

 _snake_case          =   [{} for _ in range(E)]

 for i in range(E):
  print('Edge ', i + 1)
  _snake_case      , _snake_case      , _snake_case          =   (
      int(x)
      for x in input('Enter source, destination, weight: ').strip().split(' ')
  )
  _snake_case          =   {'src': src, 'dst': dest, 'weight': weight}

 _snake_case          =   int(input('\nEnter shortest path source:').strip())
 _snake_case          =   bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)

