



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 				A				(					_lowerCamelCase  ):





							'''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 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							for char in word:
														_lowerCAmelCase     :							int    =    ord(_lowerCamelCase  )
														if not _is_chinese_char(_lowerCamelCase  ):
																					return 0
							return 1






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    set()

							for token in tokens:
														_lowerCAmelCase     :							Optional[Any]    =    len(_lowerCamelCase  ) > 1 and is_chinese(_lowerCamelCase  )
														if chinese_word:
																					word_set.add(_lowerCamelCase  )
							_lowerCAmelCase     :							Union[str, Any]    =    list(_lowerCamelCase  )
							return word_list






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if not chinese_word_set:
														return bert_tokens
							_lowerCAmelCase     :							Any    =    max([len(_lowerCamelCase  ) for w in chinese_word_set]  )

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






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    []

							for i in range(0				, len(_lowerCamelCase  )				, 100  ):
														_lowerCAmelCase     :							List[Any]    =    ltp_tokenizer.seg(lines[i : i + 100]  )[0]
														_lowerCAmelCase     :							Tuple    =    [get_chinese_word(_lowerCamelCase  ) for r in res]
														ltp_res.extend(_lowerCamelCase  )
							assert len(_lowerCamelCase  ) == len(_lowerCamelCase  )

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

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

							assert len(_lowerCamelCase  ) == len(_lowerCamelCase  )

							return ref_ids






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							_lowerCAmelCase     :							Optional[Any]    =    prepare_ref(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

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


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)





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    VersatileDiffusionDualGuidedPipeline,
						    VersatileDiffusionImageVariationPipeline,
						    VersatileDiffusionPipeline,
						    VersatileDiffusionTextToImagePipeline,
						)
else:
						from .modeling_text_unet import UNetFlatConditionModel
						from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
						from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
						from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
						from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
