
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					A     :	Union[str, Any]    = 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			(			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)

					A     :	int    = parser.parse_args()

					A     :	Optional[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 typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




