


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	int:





   """simple docstring"""






   if (
       (cp >= 0X4E_00 and cp <= 0X9F_FF)
       or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
       or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
       or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
       or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
       or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
       or (cp >= 0XF9_00 and cp <= 0XFA_FF)
       or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
   ):  #
      return True

   return False


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	List[str]:





   """simple docstring"""






   for char in word:
      a__ :Tuple						  =    ord(__SCREAMING_SNAKE_CASE				)
      if not _is_chinese_char(__SCREAMING_SNAKE_CASE				):
         return 0
   return 1


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Dict:





   """simple docstring"""
   a__ :Optional[int]						  =    set()

   for token in tokens:
      a__ :Dict						  =    len(__SCREAMING_SNAKE_CASE				) > 1 and is_chinese(__SCREAMING_SNAKE_CASE				)
      if chinese_word:
         word_set.add(__SCREAMING_SNAKE_CASE				)
   a__ :int						  =    list(__SCREAMING_SNAKE_CASE				)
   return word_list


def 				lowerCamelCase__	(      a	:   int	,  a	:   Any				)    ->	Optional[int]:





   """simple docstring"""






   if not chinese_word_set:
      return bert_tokens
   a__ :Dict						  =    max([len(__SCREAMING_SNAKE_CASE				) for w in chinese_word_set]				)

   a__ :Optional[int]						  =    bert_tokens
   a__ :List[str]						  =    0, len(__SCREAMING_SNAKE_CASE				)
   while start < end:
      a__ :Dict						  =    True
      if is_chinese(bert_word[start]				):
         a__ :List[Any]						  =    min(end - start	,  __SCREAMING_SNAKE_CASE				)
         for i in range(__SCREAMING_SNAKE_CASE	,  1	,  -1				):
            a__ :Dict						  =    "".join(bert_word[start : start + i]				)
            if whole_word in chinese_word_set:
               for j in range(start + 1	,  start + i				):
                  a__ :Optional[Any]						  =    "##" + bert_word[j]
               a__ :str						  =    start + i
               a__ :Dict						  =    False
               break
      if single_word:
         start += 1
   return bert_word


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Any	,  a	:   List[Any]				)    ->	Any:





   """simple docstring"""
   a__ :Union[str, Any]						  =    []

   for i in range(0	,  len(__SCREAMING_SNAKE_CASE				)	,  100				):
      a__ :Tuple						  =    ltp_tokenizer.seg(lines[i : i + 100]				)[0]
      a__ :Optional[int]						  =    [get_chinese_word(__SCREAMING_SNAKE_CASE				) for r in res]
      ltp_res.extend(__SCREAMING_SNAKE_CASE				)
   assert len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				)

   a__ :Optional[Any]						  =    []
   for i in range(0	,  len(__SCREAMING_SNAKE_CASE				)	,  100				):
      a__ :str						  =    bert_tokenizer(lines[i : i + 100]	,  add_special_tokens=__SCREAMING_SNAKE_CASE	,  truncation=__SCREAMING_SNAKE_CASE	,  max_length=512				)
      bert_res.extend(res["input_ids"]				)
   assert len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				)

   a__ :int						  =    []
   for input_ids, chinese_word in zip(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE				):
      a__ :List[str]						  =    []
      for id in input_ids:
         a__ :List[str]						  =    bert_tokenizer._convert_id_to_token(__SCREAMING_SNAKE_CASE				)
         input_tokens.append(__SCREAMING_SNAKE_CASE				)
      a__ :Optional[Any]						  =    add_sub_symbol(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE				)
      a__ :Any						  =    []
      # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
      for i, token in enumerate(__SCREAMING_SNAKE_CASE				):
         if token[:2] == "##":
            a__ :List[str]						  =    token[2:]
            # save chinese tokens' pos
            if len(__SCREAMING_SNAKE_CASE				) == 1 and _is_chinese_char(ord(__SCREAMING_SNAKE_CASE				)				):
               ref_id.append(__SCREAMING_SNAKE_CASE				)
      ref_ids.append(__SCREAMING_SNAKE_CASE				)

   assert len(__SCREAMING_SNAKE_CASE				) == len(__SCREAMING_SNAKE_CASE				)

   return ref_ids


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	str:





   """simple docstring"""






   with open(args.file_name	,  "r"	,  encoding="utf-8"				) as f:
      a__ :List[Any]						  =    f.readlines()
   a__ :Dict						  =    [line.strip() for line in data if len(__SCREAMING_SNAKE_CASE				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   a__ :int						  =    LTP(args.ltp				)  # faster in GPU device
   a__ :Dict						  =    BertTokenizer.from_pretrained(args.bert				)

   a__ :List[str]						  =    prepare_ref(__SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE	,  __SCREAMING_SNAKE_CASE				)

   with open(args.save_path	,  "w"	,  encoding="utf-8"				) as f:
      a__ :str						  =    [json.dumps(__SCREAMING_SNAKE_CASE				) + "\n" for ref in ref_ids]
      f.writelines(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  snake_case__			 =						argparse.ArgumentParser(description='''prepare_chinese_ref''')
  parser.add_argument(
      '''--file_name''',
      type=str,
      default='''./resources/chinese-demo.txt''',
      help='''file need process, same as training data in lm''',
  )
  parser.add_argument(
      '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
  )
  parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
  parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

  snake_case__			 =						parser.parse_args()
  main(args)






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		snake_case__			 =						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.''',
		)
		snake_case__			 =						parser.parse_args()

		snake_case__			 =						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)


