





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 SPIECE_UNDERLINE, logging


__A							:			str         =			logging.get_logger(__name__)

__A							:			Dict         =			{"vocab_file": "spiece.model"}

__A							:			str         =			{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}







class      lowerCamelCase(						__lowercase	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=False				,     snake_case_=True				,     snake_case_=False				,     snake_case_="<s>"				,     snake_case_="</s>"				,     snake_case_="<unk>"				,     snake_case_="<sep>"				,     snake_case_="<pad>"				,     snake_case_="<cls>"				,     snake_case_="<mask>"				,     snake_case_=["<eop>", "<eod>"]				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							AddedToken(_a				,     lstrip=_a				,     rstrip=_a							) if isinstance(_a				,     _a							) else mask_token

         _A     =							{} if sp_model_kwargs is None else sp_model_kwargs

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

         _A     =							3

         _A     =							do_lower_case
         _A     =							remove_space
         _A     =							keep_accents
         _A     =							vocab_file

         _A     =							spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(_a							)

         try:
             import jieba
         except ModuleNotFoundError as error:
             raise error.__class__(
                 'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
                 'See https://pypi.org/project/jieba/ for installation.'							)
         _A     =							jieba
         _A     =							str.maketrans(' \n'				,     '\u2582\u2583'							)




     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def        lowerCAmelCase__		(    self							):
         return len(self.sp_model							)




     def        lowerCAmelCase__		(    self							):
         _A     =							{self.convert_ids_to_tokens(_a							): 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				,     snake_case_							):
         _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        lowerCAmelCase__		(    self				,     snake_case_							):
         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'				,     _a							)
             _A     =							''''''.join([c for c in outputs if not unicodedata.combining(_a							)]							)
         if self.do_lower_case:
             _A     =							outputs.lower()

         return outputs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							self.preprocess_text(_a							)
         _A     =							self.sp_model.encode(_a				,     out_type=_a							)
         _A     =							[]
         for piece in pieces:
             if len(_a							) > 1 and piece[-1] == str(','							) and piece[-2].isdigit():
                 _A     =							self.sp_model.EncodeAsPieces(piece[:-1].replace(_a				,     ''							)							)
                 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(_a							)
             else:
                 new_pieces.append(_a							)

         return new_pieces




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return self.sp_model.PieceToId(_a							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return self.sp_model.IdToPiece(_a							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							''''''.join(_a							).replace(_a				,     ' '							).strip()
         return out_string




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




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

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

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




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							[self.sep_token_id]
         _A     =							[2]

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




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         if not os.path.isdir(_a							):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
             return
         _A     =							os.path.join(
             _a				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

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

         return (out_vocab_file,)




     def        lowerCAmelCase__		(    self				,     *snake_case_				,     **snake_case_							):
         _A     =							super()._decode(*_a				,     **_a							)
         _A     =							text.replace(' '				,     ''							).replace('\u2582'				,     ' '							).replace('\u2583'				,     '\n'							)
         return text








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=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   __UpperCAmelCase					(      __a :     Any      )     ->	List[Any]:

								"""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)



