



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_lowercase						  =		logging.get_logger(__name__)
_lowercase						  =		{"vocab_file": "spiece.model"}

_lowercase						  =		{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

_lowercase						  =		{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase						  =		"▁"



class     _UpperCAmelCase			(       lowercase__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




     def __init__(				self ,						a__ ,						a__=True ,						a__=True ,						a__=False ,						a__="[CLS]" ,						a__="[SEP]" ,						a__="<unk>" ,						a__="[SEP]" ,						a__="<pad>" ,						a__="[CLS]" ,						a__="[MASK]" ,						a__ = None ,						**a__ ,						):
         A__		 =					(
             AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase ,						normalized=__lowerCamelCase)
             if isinstance(__lowerCamelCase ,						__lowerCamelCase)
             else mask_token
         )

         A__		 =					{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=__lowerCamelCase ,						remove_space=__lowerCamelCase ,						keep_accents=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						sp_model_kwargs=self.sp_model_kwargs ,						**__lowerCamelCase ,						)

         A__		 =					do_lower_case
         A__		 =					remove_space
         A__		 =					keep_accents
         A__		 =					vocab_file

         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(__lowerCamelCase)




     @property
     def 		snake_case_		(				self):
         return len(self.sp_model)




     def 		snake_case_		(				self):
         A__		 =					{self.convert_ids_to_tokens(__lowerCamelCase): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab




     def __getstate__(				self):
         A__		 =					self.__dict__.copy()
         A__		 =					None
         return state




     def __setstate__(				self ,						a__):
         A__		 =					d

         # for backward compatibility
         if not hasattr(self ,						'''sp_model_kwargs'''):
             A__		 =					{}

         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)




     def 		snake_case_		(				self ,						a__):



         if self.remove_space:
             A__		 =					" ".join(inputs.strip().split())
         else:
             A__		 =					inputs
         A__		 =					outputs.replace('''``''' ,						'''\"''').replace('''\'\'''' ,						'''\"''')

         if not self.keep_accents:
             A__		 =					unicodedata.normalize('''NFKD''' ,						__lowerCamelCase)
             A__		 =					"".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase)])
         if self.do_lower_case:
             A__		 =					outputs.lower()

         return outputs




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.preprocess_text(__lowerCamelCase)
         A__		 =					self.sp_model.encode(__lowerCamelCase ,						out_type=__lowerCamelCase)
         A__		 =					[]
         for piece in pieces:
             if len(__lowerCamelCase) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                 A__		 =					self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase ,						''''''))
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]) == 1:
                         A__		 =					cur_pieces[1:]
                     else:
                         A__		 =					cur_pieces[0][1:]
                 cur_pieces.append(piece[-1])
                 new_pieces.extend(__lowerCamelCase)
             else:
                 new_pieces.append(__lowerCamelCase)

         return new_pieces




     def 		snake_case_		(				self ,						a__):
         return self.sp_model.PieceToId(__lowerCamelCase)




     def 		snake_case_		(				self ,						a__):
         return self.sp_model.IdToPiece(__lowerCamelCase)




     def 		snake_case_		(				self ,						a__):
         A__		 =					[]
         A__		 =					""
         A__		 =					False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(__lowerCamelCase) + token
                 A__		 =					True
                 A__		 =					[]
             else:
                 current_sub_tokens.append(__lowerCamelCase)
                 A__		 =					False
         out_string += self.sp_model.decode(__lowerCamelCase)
         return out_string.strip()




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					[self.sep_token_id]
         A__		 =					[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




     def 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = False):



         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__lowerCamelCase ,						token_ids_a=__lowerCamelCase ,						already_has_special_tokens=__lowerCamelCase)

         if token_ids_a is not None:
             return [1] + ([0] * len(__lowerCamelCase)) + [1] + ([0] * len(__lowerCamelCase)) + [1]
         return [1] + ([0] * len(__lowerCamelCase)) + [1]




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					[self.sep_token_id]
         A__		 =					[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




     def 		snake_case_		(				self ,						a__ ,						a__ = None):



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

         if os.path.abspath(self.vocab_file) != os.path.abspath(__lowerCamelCase) and os.path.isfile(self.vocab_file):
             copyfile(self.vocab_file ,						__lowerCamelCase)
         elif not os.path.isfile(self.vocab_file):
             with open(__lowerCamelCase ,						'''wb''') as fi:
                 A__		 =					self.sp_model.serialized_model_proto()
                 fi.write(__lowerCamelCase)

         return (out_vocab_file,)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		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=512,
       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__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       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						  =		parser.parse_args()

   _lowercase						  =		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)
