

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					_UpperCAmelCase	:     Tuple		       = 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=512,
					    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       __lowerCamelCase			(				UpperCamelCase__   ):







												'''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)

					_UpperCAmelCase	:     Optional[Any]		       = parser.parse_args()

					_UpperCAmelCase	:     List[Any]		       = 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)


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				lowercase       ( lowercase_    ):




				def 					a							(      self ):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]




				def 					a							(      self ):
											snake_case_           =					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
											return Dataset.from_dict(snake_case )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertListEqual(dset.column_names   ,     ['col_1', 'col_2'] )
											for i, r in enumerate(snake_case ):
																		self.assertDictEqual(snake_case   ,     example_records[i] )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											snake_case_           =					Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
											self.assertEqual(dset.info   ,     dset_from_dict.info )




				def 					a							(      self ):  # checks what happens with missing columns
											snake_case_           =					[{'col_1': 1}, {'col_2': 'x'}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertDictEqual(dset[0]   ,     {'col_1': 1} )
											self.assertDictEqual(dset[1]   ,     {'col_1': None} )  # NB: first record is used for columns




				def 					a							(      self ):  # checks if the type can be inferred from the second record
											snake_case_           =					[{'col_1': []}, {'col_1': [1, 2]}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertEqual(dset.info.features['col_1']   ,     Sequence(Value('int64' ) ) )




				def 					a							(      self ):
											snake_case_           =					Dataset.from_list([] )
											self.assertEqual(len(snake_case )   ,     0 )
											self.assertListEqual(dset.column_names   ,     [] )




