
'''simple docstring'''



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def A (__lowerCamelCase :int						):
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0X4e00 and cp <= 0X9fff)
						    or (cp >= 0X3400 and cp <= 0X4dbf)  #
						    or (cp >= 0X20000 and cp <= 0X2a6df)  #
						    or (cp >= 0X2a700 and cp <= 0X2b73f)  #
						    or (cp >= 0X2b740 and cp <= 0X2b81f)  #
						    or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
						    or (cp >= 0Xf900 and cp <= 0Xfaff)
						    or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
						):  #
												return True

						return False


def A (__lowerCamelCase :str						):
						# word like '180' or '身高' or '神'
						for char in word:
												_lowerCAmelCase    		=					ord(__lowerCamelCase						)
												if not _is_chinese_char(__lowerCamelCase						):
																		return 0
						return 1


def A (__lowerCamelCase :List[str]						):
						_lowerCAmelCase    		=					set()

						for token in tokens:
												_lowerCAmelCase    		=					len(__lowerCamelCase						) > 1 and is_chinese(__lowerCamelCase						)
												if chinese_word:
																		word_set.add(__lowerCamelCase						)
						_lowerCAmelCase    		=					list(__lowerCamelCase						)
						return word_list


def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :set()						):
						if not chinese_word_set:
												return bert_tokens
						_lowerCAmelCase    		=					max([len(__lowerCamelCase						) for w in chinese_word_set]						)

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


def A (__lowerCamelCase :List[str]					,      __lowerCamelCase :LTP					,      __lowerCamelCase :BertTokenizer						):
						_lowerCAmelCase    		=					[]

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

						_lowerCAmelCase    		=					[]
						for i in range(0					,      len(__lowerCamelCase						)					,      100						):
												_lowerCAmelCase    		=					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    		=					[]
						for input_ids, chinese_word in zip(__lowerCamelCase					,      __lowerCamelCase						):
												_lowerCAmelCase    		=					[]
												for id in input_ids:
																		_lowerCAmelCase    		=					bert_tokenizer._convert_id_to_token(__lowerCamelCase						)
																		input_tokens.append(__lowerCamelCase						)
												_lowerCAmelCase    		=					add_sub_symbol(__lowerCamelCase					,      __lowerCamelCase						)
												_lowerCAmelCase    		=					[]
												# 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    		=					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 :List[Any]						):
						# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
						# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
						with open(args.file_name					,      """r"""					,      encoding="""utf-8"""						) as f:
												_lowerCAmelCase    		=					f.readlines()
						_lowerCAmelCase    		=					[line.strip() for line in data if len(__lowerCamelCase						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_lowerCAmelCase    		=					LTP(args.ltp						)  # faster in GPU device
						_lowerCAmelCase    		=					BertTokenizer.from_pretrained(args.bert						)

						_lowerCAmelCase    		=					prepare_ref(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)

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


if __name__ == "__main__":
						_lowercase          =						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""")

						_lowercase          =						parser.parse_args()
						main(args)


"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'				)

          if text is not None:
              UpperCAmelCase          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if text is not None and images is not None:
              UpperCAmelCase          =       image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_				) ,							tensor_type=lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



