
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				lowercase__							:							int									=			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 	A_ (							snake_case							:   Union[str, Any] )      ->    List[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)

				lowercase__							:							int									=			parser.parse_args()

				lowercase__							:							Tuple									=			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 collections import Counter
from timeit import timeit




def 	A_ (							snake_case							:   str = "" ,      )      ->    bool:



						'''simple docstring'''

						return sum(c % 2 for c in Counter(input_str.replace(''' ''' ,      '''''' ).lower() ).values() ) < 2




def 	A_ (							snake_case							:   str = "" )      ->    bool:



						'''simple docstring'''

						if len(snake_case ) == 0:
												return True
						__UpperCamelCase       =			input_str.replace(''' ''' ,      '''''' ).lower()
						# character_freq_dict: Stores the frequency of every character in the input string
						__UpperCamelCase       =			{}

						for character in lower_case_input_str:
												__UpperCamelCase       =			character_freq_dict.get(snake_case ,      0 ) + 1
						__UpperCamelCase       =			0

						for character_count in character_freq_dict.values():
												if character_count % 2:
																		odd_char += 1
						if odd_char > 1:
												return False
						return True




def 	A_ (							snake_case							:   str = "" )      ->    None:



						'''simple docstring'''

						print('''\nFor string = ''' ,      snake_case ,      ''':''' )
						print(
						    '''> can_string_be_rearranged_as_palindrome_counter()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome_counter(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )
						print(
						    '''> can_string_be_rearranged_as_palindrome()''' ,      '''\tans =''' ,      can_string_be_rearranged_as_palindrome(snake_case ) ,      '''\ttime =''' ,      timeit(
						        '''z.can_string_be_rearranged_as_palindrome(z.check_str)''' ,      setup='''import __main__ as z''' ,      ) ,      '''seconds''' ,      )


if __name__ == "__main__":
				lowercase__							:							Tuple									=			input(
				    "Enter string to determine if it can be rearranged as a palindrome or not: "
				).strip()
				benchmark(check_str)
				lowercase__							:							Dict									=			can_string_be_rearranged_as_palindrome_counter(check_str)
				print(F"{check_str} can {'' if status else 'not '}be rearranged as a palindrome")




