





'''simple docstring'''






def      __A						(							a_						:  list[list]	):
		lowerCAmelCase     :    List[str]						=   current_set.copy()
		for row_index, row in enumerate(a_	):
				lowerCAmelCase     :    List[Any]						=   row[0]
				for column_index, column in enumerate(a_	):
						if magnitude == 0:
								lowerCAmelCase     :    str						=   column
								continue
						lowerCAmelCase     :    Optional[Any]						=   column / magnitude
    # Subtract to cancel term
		lowerCAmelCase     :    List[Any]						=   current_set[0]
		lowerCAmelCase     :    List[str]						=   [first_row]
		lowerCAmelCase     :    Any						=   current_set[1::]
		for row in current_set:
				lowerCAmelCase     :    Optional[Any]						=   []
				# If first term is 0, it is already in form we want, so we preserve it
				if row[0] == 0:
						final_set.append(a_	)
						continue
				for column_index in range(len(a_	)	):
						temp_row.append(first_row[column_index] - row[column_index]	)
				final_set.append(a_	)
		# Create next recursion iteration set
		if len(final_set[0]	) != 3:
				lowerCAmelCase     :    Union[str, Any]						=   final_set[0]
				lowerCAmelCase     :    Optional[int]						=   []
				lowerCAmelCase     :    List[str]						=   []
				for row in final_set[1::]:
						current_first_column.append(row[0]	)
						next_iteration.append(row[1::]	)
				lowerCAmelCase     :    Any						=   simplify(a_	)
				for i in range(len(a_	)	):
						resultant[i].insert(0      ,current_first_column[i]	)
				resultant.insert(0      ,a_	)
				lowerCAmelCase     :    str						=   resultant
		return final_set






def      __A						(							a_						:  list[list]	):
		if len(a_	) == 0:
				raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
		lowerCAmelCase     :    Optional[Any]						=   len(a_	) + 1
		if any(len(a_	) != _length for item in equations	):
				raise IndexError("solve_simultaneous() requires n lists of length n+1"	)
		for row in equations:
				if any(not isinstance(a_      ,(int, float)	) for column in row	):
						raise ValueError("solve_simultaneous() requires lists of integers"	)
		if len(a_	) == 1:
				return [equations[0][-1] / equations[0][0]]
		lowerCAmelCase     :    List[str]						=   equations.copy()
		if any(0 in row for row in data_set	):
				lowerCAmelCase     :    int						=   data_set.copy()
				lowerCAmelCase     :    Optional[int]						=   []
				for row_index, row in enumerate(a_	):
						if 0 not in row:
								lowerCAmelCase     :    List[Any]						=   data_set.pop(a_	)
								break
				if not full_row:
						raise ValueError("solve_simultaneous() requires at least 1 full equation"	)
				data_set.insert(0      ,a_	)
		lowerCAmelCase     :    Optional[int]						=   data_set.copy()
		lowerCAmelCase     :    List[str]						=   simplify(a_	)
		lowerCAmelCase     :    Optional[int]						=   simplified[::-1]
		lowerCAmelCase     :    list						=   []
		for row in simplified:
				lowerCAmelCase     :    List[str]						=   row[-1]
				if not solutions:
						if row[-2] == 0:
								solutions.append(0	)
								continue
						solutions.append(current_solution / row[-2]	)
						continue
				lowerCAmelCase     :    Union[str, Any]						=   row.copy()[: len(a_	) - 1 :]
				while temp_row[0] == 0:
						temp_row.pop(0	)
				if len(a_	) == 0:
						solutions.append(0	)
						continue
				lowerCAmelCase     :    int						=   temp_row[1::]
				lowerCAmelCase     :    Tuple						=   temp_row[::-1]
				for column_index, column in enumerate(a_	):
						current_solution -= column * solutions[column_index]
				solutions.append(a_	)
		lowerCAmelCase     :    Union[str, Any]						=   []
		for item in solutions:
				final.append(float(round(a_      ,5	)	)	)
		return final[::-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					lowerCAmelCase       		=      [
					    [2, 1, 1, 1, 1, 4],
					    [1, 2, 1, 1, 1, 5],
					    [1, 1, 2, 1, 1, 6],
					    [1, 1, 1, 2, 1, 7],
					    [1, 1, 1, 1, 2, 8],
					]
					print(solve_simultaneous(eq))
					print(solve_simultaneous([[4, 2]]))










'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					lowerCAmelCase       		=      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_12,
					    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      __A						(							a_						:  List[str]	):
							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)

					lowerCAmelCase       		=      parser.parse_args()

					lowerCAmelCase       		=      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)



