





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
      SCREAMING_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(
          """--original_config_file""",
          type=str,
          required=True,
          help="""The YAML config file corresponding to the original architecture.""",
      )
      parser.add_argument(
          """--num_in_channels""",
          default=None,
          type=int,
          help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
      )
      parser.add_argument(
          """--image_size""",
          default=5_1_2,
          type=int,
          help=(
              """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
              """ Base. Use 768 for Stable Diffusion v2."""
          ),
      )
      parser.add_argument(
          """--extract_ema""",
          action="""store_true""",
          help=(
              """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
              """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
              """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
          ),
      )
      parser.add_argument(
          """--upcast_attention""",
          action="""store_true""",
          help=(
              """Whether the attention computation should always be upcasted. This is necessary when running stable"""
              """ diffusion 2.1."""
          ),
      )
      parser.add_argument(
          """--from_safetensors""",
          action="""store_true""",
          help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
      )
      parser.add_argument(
          """--to_safetensors""",
          action="""store_true""",
          help="""Whether to store pipeline in safetensors format or not.""",
      )
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")



      def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Any:


            '''simple docstring'''


            if string == "True":
                  return True
            elif string == "False":
                  return False
            else:
                  raise ValueError(F"""could not parse string as bool {string}"""			)

      parser.add_argument(
          """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
      )

      parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = download_controlnet_from_original_ckpt(
          checkpoint_path=args.checkpoint_path,
          original_config_file=args.original_config_file,
          image_size=args.image_size,
          extract_ema=args.extract_ema,
          num_in_channels=args.num_in_channels,
          upcast_attention=args.upcast_attention,
          from_safetensors=args.from_safetensors,
          device=args.device,
          use_linear_projection=args.use_linear_projection,
          cross_attention_dim=args.cross_attention_dim,
      )



      controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









import pytest

from datasets import inspect_metric, list_metrics, load_metric



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings"""				,		set()			)



@pytest.fixture
def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      class 	UpperCamelCase__				:



           '''simple docstring'''

           def __init__(							self		:					int		,lowerCamelCase__		:					Dict )  ->	List[Any]:




                 '''simple docstring'''


                 SCREAMING_SNAKE_CASE	=		metric_id

      class 	UpperCamelCase__				:



           '''simple docstring'''

           __snake_case					:			List[str]				=    [MetricMock(lowerCAmelCase_     ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]






           def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	str:




                 '''simple docstring'''


                 return self._metrics
      monkeypatch.setattr("""datasets.inspect.huggingface_hub"""				,		HfhMock()			)



@pytest.mark.parametrize(
    """func, args"""				,		[(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))]			)
def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      if "tmp_path" in args:
            SCREAMING_SNAKE_CASE	=		tuple(arg if arg != """tmp_path""" else tmp_path for arg in args			)
      with pytest.warns(_SCREAMING_SNAKE_CASE				,		match="""https://huggingface.co/docs/evaluate"""			):
            func(*_SCREAMING_SNAKE_CASE			)


