



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE_:Tuple					=						False
SCREAMING_SNAKE_CASE_:Dict					=						True
SCREAMING_SNAKE_CASE_:int					=						False


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						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.""")

     SCREAMING_SNAKE_CASE_:List[Any]					=						parser.parse_args()

     SCREAMING_SNAKE_CASE_:str					=						{
         """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""",
     }

     SCREAMING_SNAKE_CASE_:Dict					=						{
         """time_steps""": """time_proj""",
         """mid""": """mid_block""",
         """downsample_blocks""": """down_blocks""",
         """upsample_blocks""": """up_blocks""",
     }

     SCREAMING_SNAKE_CASE_:str					=						"""""" 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:
          SCREAMING_SNAKE_CASE_:str					=						reader.read()
          SCREAMING_SNAKE_CASE_:Union[str, Any]					=						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"""):
          SCREAMING_SNAKE_CASE_:int					=						UNetaDModel(**config)
     else:
          SCREAMING_SNAKE_CASE_:List[Any]					=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
          SCREAMING_SNAKE_CASE_:Tuple					=						class_name(**config)

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

     SCREAMING_SNAKE_CASE_:Dict					=						dict(model.config)

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

          SCREAMING_SNAKE_CASE_:Dict					=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
          SCREAMING_SNAKE_CASE_:List[Any]					=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

     if do_only_weights:
          SCREAMING_SNAKE_CASE_:Any					=						torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

          SCREAMING_SNAKE_CASE_:Any					=						{}
          for param_key, param_value in state_dict.items():
               if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
                    continue
               SCREAMING_SNAKE_CASE_:int					=						False
               for key, new_key in key_parameters_to_change.items():
                    if not has_changed and param_key.split(""".""")[0] == key:
                         SCREAMING_SNAKE_CASE_:Dict					=						param_value
                         SCREAMING_SNAKE_CASE_:Any					=						True
               if not has_changed:
                    SCREAMING_SNAKE_CASE_:Union[str, Any]					=						param_value

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







from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A					,							A			:	Tuple										=       9, 14  # noqa: F841
    A			:	Optional[int]										=       [
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    A			:	int										=       defaultdict(_lowerCAmelCase		)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]		)
        adjancency[nodea].append([nodea, cost]		)

    A			:	List[str]										=       mst(_lowerCAmelCase		)

    A			:	str										=       [
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        A			:	Dict										=       tuple(answer[:2]		)
        A			:	List[str]										=       tuple(edge[::-1]		)
        assert edge in result or reverse in result


