





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{"vocab_file": "spiece.model"}

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

class  lowerCAmelCase_		(		__magic_name__		):




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

     _lowerCAmelCase						      =   {} 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      ,  additional_special_tokens=_lowerCAmelCase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  **_lowerCAmelCase      ,  )

     _lowerCAmelCase						      =   3

     _lowerCAmelCase						      =   do_lower_case
     _lowerCAmelCase						      =   remove_space
     _lowerCAmelCase						      =   keep_accents
     _lowerCAmelCase						      =   vocab_file

     _lowerCAmelCase						      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

     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."		)
     _lowerCAmelCase						      =   jieba
     _lowerCAmelCase						      =   str.maketrans(" \n"      ,  "\u2582\u2583"		)




 @property
 # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
 def 				_snake_case (						self		)     ->				Optional[int]:
     return len(self.sp_model		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   {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		)     ->				Tuple:
     _lowerCAmelCase						      =   self.__dict__.copy()
     _lowerCAmelCase						      =   None
     return state




 def __setstate__(						self      ,  _lowerCAmelCase		)     ->				Dict:
     _lowerCAmelCase						      =   d

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     if self.remove_space:
         _lowerCAmelCase						      =   " ".join(inputs.strip().split()		)
     else:
         _lowerCAmelCase						      =   inputs
     _lowerCAmelCase						      =   outputs.replace("``"      ,  "\""		).replace("''"      ,  "\""		)

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

     return outputs




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   self.preprocess_text(_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.sp_model.encode(_lowerCAmelCase      ,  out_type=_lowerCAmelCase		)
     _lowerCAmelCase						      =   []
     for piece in pieces:
         if len(_lowerCAmelCase		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
             _lowerCAmelCase						      =   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:
                     _lowerCAmelCase						      =   cur_pieces[1:]
                 else:
                     _lowerCAmelCase						      =   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      ,  _lowerCAmelCase		)     ->				str:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   "".join(_lowerCAmelCase		).replace(_lowerCAmelCase      ,  " "		).strip()
     return out_string




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False		)     ->				List[int]:

     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 ([0] * len(_lowerCAmelCase		)) + [1] + ([0] * len(_lowerCAmelCase		)) + [1, 1]
     return ([0] * len(_lowerCAmelCase		)) + [1, 1]




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase						      =   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:
             _lowerCAmelCase						      =   self.sp_model.serialized_model_proto()
             fi.write(_lowerCAmelCase		)

     return (out_vocab_file,)





 def 				_snake_case (						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   super()._decode(*_lowerCAmelCase      ,  **_lowerCAmelCase		)
     _lowerCAmelCase						      =   text.replace(" "      ,  ""		).replace("\u2582"      ,  " "		).replace("\u2583"      ,  "\n"		)
     return text








'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							a    :							List[str]										=    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
							parser.add_argument(
							    '--original_config_file',
							    default=None,
							    type=str,
							    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(
							    '--scheduler_type',
							    default='pndm',
							    type=str,
							    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
							)
							parser.add_argument(
							    '--pipeline_type',
							    default=None,
							    type=str,
							    help=(
							        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
							        '. If `None` pipeline will be automatically inferred.'
							    ),
							)
							parser.add_argument(
							    '--image_size',
							    default=None,
							    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(
							    '--prediction_type',
							    default=None,
							    type=str,
							    help=(
							        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
							        ' Diffusion v2 Base. Use \'v_prediction\' 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.)')
							parser.add_argument(
							    '--stable_unclip',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
							)
							parser.add_argument(
							    '--stable_unclip_prior',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
							)
							parser.add_argument(
							    '--clip_stats_path',
							    type=str,
							    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
							    required=False,
							)
							parser.add_argument(
							    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
							)
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--vae_path',
							    type=str,
							    default=None,
							    required=False,
							    help='Set to a path, hub id to an already converted vae to not convert it again.',
							)
							a    :							str										=    parser.parse_args()

							a    :							List[Any]										=    download_from_original_stable_diffusion_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    prediction_type=args.prediction_type,
							    model_type=args.pipeline_type,
							    extract_ema=args.extract_ema,
							    scheduler_type=args.scheduler_type,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    stable_unclip=args.stable_unclip,
							    stable_unclip_prior=args.stable_unclip_prior,
							    clip_stats_path=args.clip_stats_path,
							    controlnet=args.controlnet,
							    vae_path=args.vae_path,
							)

							if args.half:
														pipe.to(torch_dtype=torch.floataa)

							if args.controlnet:
														# only save the controlnet model
														pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
							else:
														pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

