



"""simple docstring"""
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
						__a      =				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_  (			_lowercase	):







												'''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      =				parser.parse_args()

						__a      =				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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     lowercase__    :  int							    =	None


lowercase__    :  Any							    =	logging.get_logger(__name__)


lowercase__    :  List[str]							    =	'''▁'''

lowercase__    :  Optional[int]							    =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__    :  str							    =	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

lowercase__    :  List[Any]							    =	{
    '''google/pegasus-xsum''': 5_12,
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[str]							=		VOCAB_FILES_NAMES
 _lowerCAmelCase   :			List[str]							=		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase   :			Any							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase   :			Tuple							=		PegasusTokenizer
 _lowerCAmelCase   :			str							=		["""input_ids""", """attention_mask"""]

 def __init__(     self		:       Any  ,     lowercase_		:       Optional[Any]=None  ,     lowercase_		:       int=None  ,     lowercase_		:       Tuple="<pad>"  ,     lowercase_		:       int="</s>"  ,     lowercase_		:       Tuple="<unk>"  ,     lowercase_		:       str="<mask_2>"  ,     lowercase_		:       Optional[Any]="<mask_1>"  ,     lowercase_		:       str=None  ,     lowercase_		:       List[str]=103  ,     **lowercase_		:       List[Any]  ,     ):
     snake_case_ :     Dict				=	offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_  ,     lowercase_				):
             raise TypeError(
                 f"additional_special_tokens should be of type {type(lowercase_				)}, but is"
                 f" {type(lowercase_				)}"				)

         snake_case_ :     str				=	(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             f"<unk_{i}>" for i in range(len(lowercase_				)  ,     self.offset - 1				)
         ]

         if len(set(lowercase_				)				) != len(lowercase_				):
             raise ValueError(
                 '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                 f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."				)
         snake_case_ :     Union[str, Any]				=	additional_special_tokens_extended
     else:
         snake_case_ :     Dict				=	[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [f"<unk_{i}>" for i in range(2  ,     self.offset				)]

     super().__init__(
         lowercase_  ,     tokenizer_file=lowercase_  ,     pad_token=lowercase_  ,     eos_token=lowercase_  ,     unk_token=lowercase_  ,     mask_token=lowercase_  ,     mask_token_sent=lowercase_  ,     offset=lowercase_  ,     additional_special_tokens=lowercase_  ,     **lowercase_  ,     )
     snake_case_ :     List[Any]				=	vocab_file
     snake_case_ :     List[Any]				=	False if not self.vocab_file else True

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Union[str, Any]				):
     snake_case_ :     Any				=	set(self.all_special_ids				)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id				)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens				) + 3				)				):
         raise ValueError(
             '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
             f" {len(self.additional_special_tokens				)} additional_special_tokens, but got {all_special_ids}"				)

     return [1 if x in all_special_ids else 0 for x in seq]

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       List  ,     lowercase_		:       Optional[List] = None  ,     lowercase_		:       bool = False				):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_				)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_				) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a				) + [1]

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       str=None				):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]






 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[str] = None				):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''				)

     if not os.path.isdir(lowercase_				):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
         return
     snake_case_ :     Dict				=	os.path.join(
         lowercase_  ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

     if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				):
         copyfile(self.vocab_file  ,     lowercase_				)

     return (out_vocab_file,)





