

'''simple docstring'''






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass






@dataclass
class     lowercase_			:




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[str]           =    4_2
  SCREAMING_SNAKE_CASE					:						str           =    4_2






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				[[] for _ in range(lowerCAmelCase__    )]
   __lowercase													=				size






  def __getitem__(   self     :  str      ,lowercase__     :  int    ):
   return iter(self._graph[vertex]    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   return self._size






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  int      ,lowercase__     :  int      ,lowercase__     :  int    ):
   if weight not in (0, 1):
    raise ValueError('''Edge weight must be either 0 or 1.'''    )

   if to_vertex < 0 or to_vertex >= self.size:
    raise ValueError('''Vertex indexes must be in [0; size).'''    )

   self._graph[from_vertex].append(Edge(lowerCAmelCase__      ,lowerCAmelCase__    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  int      ,lowercase__     :  int    ):
   __lowercase													=				deque([start_vertex]    )
   __lowercase													=				[None] * self.size
   __lowercase													=				0

   while queue:
    __lowercase													=				queue.popleft()
    __lowercase													=				distances[current_vertex]
    if current_distance is None:
     continue

    for edge in self[current_vertex]:
     __lowercase													=				current_distance + edge.weight
     __lowercase													=				distances[edge.destination_vertex]
     if (
         isinstance(lowerCAmelCase__      ,lowerCAmelCase__    )
         and new_distance >= dest_vertex_distance
     ):
      continue
     __lowercase													=				new_distance
     if edge.weight == 0:
      queue.appendleft(edge.destination_vertex    )
     else:
      queue.append(edge.destination_vertex    )

   if distances[finish_vertex] is None:
    raise ValueError('''No path from start_vertex to finish_vertex.'''    )

   return distances[finish_vertex]


if __name__ == "__main__":
 import doctest

 doctest.testmod()



'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase__      		=			False
lowerCAmelCase__      		=			True
lowerCAmelCase__      		=			False


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()

 parser.add_argument(
     '''--repo_path''',
     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.''')

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			{
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 lowerCAmelCase__      		=			{
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 lowerCAmelCase__      		=			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  lowerCAmelCase__      		=			reader.read()
  lowerCAmelCase__      		=			json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  lowerCAmelCase__      		=			UNetaDModel(**config)
 else:
  lowerCAmelCase__      		=			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  lowerCAmelCase__      		=			class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 lowerCAmelCase__      		=			dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    lowerCAmelCase__      		=			config[key]
    del config[key]

  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  lowerCAmelCase__      		=			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  lowerCAmelCase__      		=			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  lowerCAmelCase__      		=			{}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   lowerCAmelCase__      		=			False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     lowerCAmelCase__      		=			param_value
     lowerCAmelCase__      		=			True
   if not has_changed:
    lowerCAmelCase__      		=			param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))

