



"""simple docstring"""







import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				int						)						->   List[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= checkpoint

  lowerCAmelCase_   :				List[Any]								= {}

  lowerCAmelCase_   :				int								= vae_state_dict['encoder.conv_in.weight']
  lowerCAmelCase_   :				Optional[int]								= vae_state_dict['encoder.conv_in.bias']
  lowerCAmelCase_   :				str								= vae_state_dict['encoder.conv_out.weight']
  lowerCAmelCase_   :				int								= vae_state_dict['encoder.conv_out.bias']
  lowerCAmelCase_   :				Tuple								= vae_state_dict['encoder.norm_out.weight']
  lowerCAmelCase_   :				str								= vae_state_dict['encoder.norm_out.bias']

  lowerCAmelCase_   :				Any								= vae_state_dict['decoder.conv_in.weight']
  lowerCAmelCase_   :				Optional[int]								= vae_state_dict['decoder.conv_in.bias']
  lowerCAmelCase_   :				str								= vae_state_dict['decoder.conv_out.weight']
  lowerCAmelCase_   :				List[Any]								= vae_state_dict['decoder.conv_out.bias']
  lowerCAmelCase_   :				str								= vae_state_dict['decoder.norm_out.weight']
  lowerCAmelCase_   :				str								= vae_state_dict['decoder.norm_out.bias']

  lowerCAmelCase_   :				int								= vae_state_dict['quant_conv.weight']
  lowerCAmelCase_   :				Union[str, Any]								= vae_state_dict['quant_conv.bias']
  lowerCAmelCase_   :				Union[str, Any]								= vae_state_dict['post_quant_conv.weight']
  lowerCAmelCase_   :				Union[str, Any]								= vae_state_dict['post_quant_conv.bias']

  # Retrieves the keys for the encoder down blocks only
  lowerCAmelCase_   :				Union[str, Any]								= len({'.'.join(layer.split('.'						)[:3]						) for layer in vae_state_dict if 'encoder.down' in layer}						)
  lowerCAmelCase_   :				Any								= {
      layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(lowerCAmelCase__						)
  }

  # Retrieves the keys for the decoder up blocks only
  lowerCAmelCase_   :				Union[str, Any]								= len({'.'.join(layer.split('.'						)[:3]						) for layer in vae_state_dict if 'decoder.up' in layer}						)
  lowerCAmelCase_   :				List[Any]								= {
      layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(lowerCAmelCase__						)
  }

  for i in range(lowerCAmelCase__						):
    lowerCAmelCase_   :				Optional[Any]								= [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]

    if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
      lowerCAmelCase_   :				Any								= vae_state_dict.pop(
          f"encoder.down.{i}.downsample.conv.weight"						)
      lowerCAmelCase_   :				List[str]								= vae_state_dict.pop(
          f"encoder.down.{i}.downsample.conv.bias"						)

    lowerCAmelCase_   :				Optional[Any]								= renew_vae_resnet_paths(lowerCAmelCase__						)
    lowerCAmelCase_   :				List[str]								= {'old': f"down.{i}.block", 'new': f"down_blocks.{i}.resnets"}
    assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)

  lowerCAmelCase_   :				Optional[int]								= [key for key in vae_state_dict if 'encoder.mid.block' in key]
  lowerCAmelCase_   :				Any								= 2
  for i in range(1    ,   num_mid_res_blocks + 1						):
    lowerCAmelCase_   :				Optional[Any]								= [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]

    lowerCAmelCase_   :				str								= renew_vae_resnet_paths(lowerCAmelCase__						)
    lowerCAmelCase_   :				int								= {'old': f"mid.block_{i}", 'new': f"mid_block.resnets.{i - 1}"}
    assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)

  lowerCAmelCase_   :				Optional[int]								= [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  lowerCAmelCase_   :				Any								= renew_vae_attention_paths(lowerCAmelCase__						)
  lowerCAmelCase_   :				Tuple								= {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)
  conv_attn_to_linear(lowerCAmelCase__						)

  for i in range(lowerCAmelCase__						):
    lowerCAmelCase_   :				int								= num_up_blocks - 1 - i
    lowerCAmelCase_   :				Optional[Any]								= [
        key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
    ]

    if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
      lowerCAmelCase_   :				int								= vae_state_dict[
          f"decoder.up.{block_id}.upsample.conv.weight"
      ]
      lowerCAmelCase_   :				List[Any]								= vae_state_dict[
          f"decoder.up.{block_id}.upsample.conv.bias"
      ]

    lowerCAmelCase_   :				Optional[Any]								= renew_vae_resnet_paths(lowerCAmelCase__						)
    lowerCAmelCase_   :				Optional[Any]								= {'old': f"up.{block_id}.block", 'new': f"up_blocks.{i}.resnets"}
    assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)

  lowerCAmelCase_   :				Dict								= [key for key in vae_state_dict if 'decoder.mid.block' in key]
  lowerCAmelCase_   :				Optional[int]								= 2
  for i in range(1    ,   num_mid_res_blocks + 1						):
    lowerCAmelCase_   :				int								= [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]

    lowerCAmelCase_   :				int								= renew_vae_resnet_paths(lowerCAmelCase__						)
    lowerCAmelCase_   :				int								= {'old': f"mid.block_{i}", 'new': f"mid_block.resnets.{i - 1}"}
    assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)

  lowerCAmelCase_   :				Tuple								= [key for key in vae_state_dict if 'decoder.mid.attn' in key]
  lowerCAmelCase_   :				Tuple								= renew_vae_attention_paths(lowerCAmelCase__						)
  lowerCAmelCase_   :				Any								= {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
  assign_to_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   additional_replacements=[meta_path]    ,   config=lowerCAmelCase__						)
  conv_attn_to_linear(lowerCAmelCase__						)
  return new_checkpoint

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str    ,   )						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				str								= requests.get(
      ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'						)
  lowerCAmelCase_   :				Union[str, Any]								= io.BytesIO(r.content						)

  lowerCAmelCase_   :				str								= OmegaConf.load(lowerCAmelCase__						)
  lowerCAmelCase_   :				str								= 512
  lowerCAmelCase_   :				int								= 'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('safetensors'						):
    from safetensors import safe_open

    lowerCAmelCase_   :				int								= {}
    with safe_open(lowerCAmelCase__    ,   framework='pt'    ,   device='cpu'						) as f:
      for key in f.keys():
        lowerCAmelCase_   :				Optional[Any]								= f.get_tensor(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				str								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)['state_dict']

  # Convert the VAE model.
  lowerCAmelCase_   :				List[str]								= create_vae_diffusers_config(lowerCAmelCase__    ,   image_size=lowerCAmelCase__						)
  lowerCAmelCase_   :				str								= custom_convert_ldm_vae_checkpoint(lowerCAmelCase__    ,   lowerCAmelCase__						)

  lowerCAmelCase_   :				Union[str, Any]								= AutoencoderKL(**lowerCAmelCase__						)
  vae.load_state_dict(lowerCAmelCase__						)
  vae.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				List[Any]          =	argparse.ArgumentParser()

     parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
     parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

     lowercase__		:				int          =	parser.parse_args()

     vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowerCAmelCase__						)						):
    if indegree[i] == 0:
      queue.append(lowerCAmelCase__						)

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(lowerCAmelCase__						)

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





