




import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A						:							Optional[int]							=  logging.get_logger(__name__)




class 			UpperCAmelCase_      (   UpperCAmelCase_       ):




							'''simple docstring'''



							a__						  =     ['input_ids', 'attention_mask']





							def __init__(					self       :	Optional[int]       ,     a       :	int="</s>"       ,     a       :	int="<unk>"       ,     a       :	Optional[Any]="<pad>"       ,     a       :	int=125       ,     a       :	List[Any]=None       ,     **a       :	Dict       ,     )			->					None:
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
													SCREAMING_SNAKE_CASE           =					[f"""<extra_id_{i}>""" for i in range(_lowercase			)]
										elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													SCREAMING_SNAKE_CASE           =					len(set(filter(lambda a			: bool("""extra_id""" in str(_lowercase			)			)       ,     _lowercase			)			)			)
													if extra_tokens != extra_ids:
																raise ValueError(
																    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																    """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
																    """ extra_ids tokens"""			)

										SCREAMING_SNAKE_CASE           =					AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase			) if isinstance(_lowercase       ,     _lowercase			) else pad_token
										SCREAMING_SNAKE_CASE           =					AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase			) if isinstance(_lowercase       ,     _lowercase			) else eos_token
										SCREAMING_SNAKE_CASE           =					AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase			) if isinstance(_lowercase       ,     _lowercase			) else unk_token

										super().__init__(
										    eos_token=_lowercase       ,     unk_token=_lowercase       ,     pad_token=_lowercase       ,     extra_ids=_lowercase       ,     additional_special_tokens=_lowercase       ,     **_lowercase       ,     )

										SCREAMING_SNAKE_CASE           =					extra_ids

										SCREAMING_SNAKE_CASE           =					2**8  # utf is 8 bits

										# define special tokens dict
										SCREAMING_SNAKE_CASE           =					{
										    self.pad_token: 0,
										    self.eos_token: 1,
										    self.unk_token: 2,
										}
										SCREAMING_SNAKE_CASE           =					len(self.special_tokens_encoder			)
										SCREAMING_SNAKE_CASE           =					len(_lowercase			)
										for i, token in enumerate(_lowercase			):
													SCREAMING_SNAKE_CASE           =					self.vocab_size + i - n
										SCREAMING_SNAKE_CASE           =					{v: k for k, v in self.special_tokens_encoder.items()}





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Dict:
										return self._utf_vocab_size + self._num_special_tokens + self._extra_ids





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Optional[Any]       ,     a       :	Any = None       ,     a       :	Optional[Any] = False			)			->					List[int]:
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowercase       ,     token_ids_a=_lowercase       ,     already_has_special_tokens=_lowercase			)

										# normal case: some special tokens
										if token_ids_a is None:
													return ([0] * len(_lowercase			)) + [1]
										return ([0] * len(_lowercase			)) + [1] + ([0] * len(_lowercase			)) + [1]





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Optional[Any]			)			->					List[int]:
										if len(_lowercase			) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
													    """ eos tokens being added."""			)
													return token_ids
										else:
													return token_ids + [self.eos_token_id]





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[Any]       ,     a       :	Optional[int] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id]

										if token_ids_a is None:
													return len(token_ids_a + eos			) * [0]
										return len(token_ids_a + eos + token_ids_a + eos			) * [0]





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Dict       ,     a       :	Union[str, Any] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					self._add_eos_if_not_present(_lowercase			)
										if token_ids_a is None:
													return token_ids_a
										else:
													SCREAMING_SNAKE_CASE           =					self._add_eos_if_not_present(_lowercase			)
													return token_ids_a + token_ids_a





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Tuple			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					[chr(_lowercase			) for i in text.encode("""utf-8"""			)]
										return tokens





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	Tuple			)			->					str:
										if token in self.special_tokens_encoder:
													SCREAMING_SNAKE_CASE           =					self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
													SCREAMING_SNAKE_CASE           =					self.added_tokens_encoder[token]
										elif len(_lowercase			) != 1:
													SCREAMING_SNAKE_CASE           =					self.unk_token_id
										else:
													SCREAMING_SNAKE_CASE           =					ord(_lowercase			) + self._num_special_tokens
										return token_id





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	List[Any]			)			->					List[Any]:
										if index in self.special_tokens_decoder:
													SCREAMING_SNAKE_CASE           =					self.special_tokens_decoder[index]
										else:
													SCREAMING_SNAKE_CASE           =					chr(index - self._num_special_tokens			)
										return token





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					B''
										for token in tokens:
													if token in self.special_tokens_decoder:
																SCREAMING_SNAKE_CASE           =					self.special_tokens_decoder[token].encode("""utf-8"""			)
													elif token in self.added_tokens_decoder:
																SCREAMING_SNAKE_CASE           =					self.special_tokens_decoder[token].encode("""utf-8"""			)
													elif token in self.special_tokens_encoder:
																SCREAMING_SNAKE_CASE           =					token.encode("""utf-8"""			)
													elif token in self.added_tokens_encoder:
																SCREAMING_SNAKE_CASE           =					token.encode("""utf-8"""			)
													else:
																SCREAMING_SNAKE_CASE           =					bytes([ord(_lowercase			)]			)
													bstring += tok_string
										SCREAMING_SNAKE_CASE           =					bstring.decode("""utf-8"""       ,     errors="""ignore"""			)
										return string







							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	Optional[Any]       ,     a       :	Tuple = None			)			->					Tuple[str]:
										return ()






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							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=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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''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)

		__A						:							Union[str, Any]							=  parser.parse_args()

		__A						:							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)






