






"""simple docstring"""



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    _UpperCamelCase		:  Optional[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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





      '''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[int]						=		parser.parse_args()

    _UpperCamelCase		:  Union[str, 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)










"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'							,		_lowerCamelCase							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'       )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'       )

      super().__init__(_lowerCamelCase							,		_lowerCamelCase       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'       )

      if images is not None:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowercase							    =       encodings['input_ids']
        return inputs







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

      while tokens:
        lowercase							    =       re.search(R'<s_(.*?)>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        if start_token is None:
          break
        lowercase							    =       start_token.group(1       )
        lowercase							    =       re.search(RF'</s_{key}>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        lowercase							    =       start_token.group()
        if end_token is None:
          lowercase							    =       tokens.replace(_lowerCamelCase							,		''       )
        else:
          lowercase							    =       end_token.group()
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.search(F'{start_token_escaped}(.*?){end_token_escaped}'							,		_lowerCamelCase							,		re.IGNORECASE       )
          if content is not None:
            lowercase							    =       content.group(1       ).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowercase							    =       self.tokenajson(_lowerCamelCase							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )
              if value:
                if len(_lowerCamelCase       ) == 1:
                  lowercase							    =       value[0]
                lowercase							    =       value
            else:  # leaf nodes
              lowercase							    =       []
              for leaf in content.split(R'<sep/>'       ):
                lowercase							    =       leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowercase							    =       leaf[1:-2]  # for categorical special tokens
                output[key].append(_lowerCamelCase       )
              if len(output[key]       ) == 1:
                lowercase							    =       output[key][0]

          lowercase							    =       tokens[tokens.find(_lowerCamelCase       ) + len(_lowerCamelCase       ) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )

      if len(_lowerCamelCase       ):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor_class







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


