
"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def lowercase_						(     _snake_case		):
       # 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 >= 0x4_e00 and cp <= 0x9_fff)
           or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
           or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
           or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
           or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
           or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
           or (cp >= 0xf_900 and cp <= 0xf_aff)
           or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
       ):  #
              return True

       return False

def lowercase_						(     _snake_case		):
       # word like '180' or '身高' or '神'
       for char in word:
              SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     ord(_snake_case		)
              if not _is_chinese_char(_snake_case		):
                     return 0
       return 1

def lowercase_						(     _snake_case		):
       SCREAMING_SNAKE_CASE__		: int     =     set()

       for token in tokens:
              SCREAMING_SNAKE_CASE__		: Any     =     len(_snake_case		) > 1 and is_chinese(_snake_case		)
              if chinese_word:
                     word_set.add(_snake_case		)
       SCREAMING_SNAKE_CASE__		: Optional[Any]     =     list(_snake_case		)
       return word_list

def lowercase_						(     _snake_case     ,_snake_case		):
       if not chinese_word_set:
              return bert_tokens
       SCREAMING_SNAKE_CASE__		: Dict     =     max([len(_snake_case		) for w in chinese_word_set]		)

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

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
       SCREAMING_SNAKE_CASE__		: Dict     =     []

       for i in range(0     ,len(_snake_case		)     ,100		):
              SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ltp_tokenizer.pipeline(lines[i : i + 100]     ,tasks=["""cws"""]		).cws
              SCREAMING_SNAKE_CASE__		: Dict     =     [get_chinese_word(_snake_case		) for r in res]
              ltp_res.extend(_snake_case		)
       assert len(_snake_case		) == len(_snake_case		)

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

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

       assert len(_snake_case		) == len(_snake_case		)

       return ref_ids

def lowercase_						(     _snake_case		):
       # 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:
              SCREAMING_SNAKE_CASE__		: Dict     =     f.readlines()
       SCREAMING_SNAKE_CASE__		: List[str]     =     [line.strip() for line in data if len(_snake_case		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       SCREAMING_SNAKE_CASE__		: Dict     =     LTP(args.ltp		)  # faster in GPU device
       SCREAMING_SNAKE_CASE__		: str     =     BertTokenizer.from_pretrained(args.bert		)

       SCREAMING_SNAKE_CASE__		: List[str]     =     prepare_ref(_snake_case     ,_snake_case     ,_snake_case		)

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


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

     UpperCAmelCase__   :					List[Any]												=				parser.parse_args()
     main(args)






"""simple docstring"""




from manim import *



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     Rectangle(height=0.5     , width=0.5					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Rectangle(height=0.46     , width=0.46					).set_stroke(width=0					)

											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: str     =     [mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     VGroup(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: int     =     Text("""CPU"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: Any     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											cpu.move_to([-2.5, -0.5, 0]					)
											self.add(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [mem.copy() for i in range(1					)]
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)
											SCREAMING_SNAKE_CASE__		: str     =     Text("""GPU"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: str     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											gpu.align_to(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
											gpu.set_x(gpu.get_x() - 1					)

											self.add(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: List[Any]     =     [mem.copy() for i in range(6					)]
											SCREAMING_SNAKE_CASE__		: List[Any]     =     VGroup(*SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Text("""Model"""     , font_size=24					)
											SCREAMING_SNAKE_CASE__		: Any     =     Group(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					).arrange(SCREAMING_SNAKE_CASE__     , buff=0.5     , aligned_edge=SCREAMING_SNAKE_CASE__					)
											model.move_to([3, -1.0, 0]					)

											self.play(
											    Create(SCREAMING_SNAKE_CASE__     , run_time=1					)     , Create(SCREAMING_SNAKE_CASE__     , run_time=1					)     , Create(SCREAMING_SNAKE_CASE__     , run_time=1					)     , )

											SCREAMING_SNAKE_CASE__		: Tuple     =     MarkupText(
											    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''     , font_size=24     , )

											SCREAMING_SNAKE_CASE__		: Dict     =     Square(side_length=2.2					)
											key.move_to([-5, 2, 0]					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     MarkupText(
											    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''     , font_size=18     , )

											key_text.move_to([-5, 2.4, 0]					)

											step_a.move_to([2, 2, 0]					)
											self.play(Write(SCREAMING_SNAKE_CASE__     , run_time=2.5					)     , Write(SCREAMING_SNAKE_CASE__					)     , Write(SCREAMING_SNAKE_CASE__					)					)

											self.add(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     []
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     []
											SCREAMING_SNAKE_CASE__		: str     =     []
											for i, rect in enumerate(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Rectangle(height=0.46     , width=0.46					).set_stroke(width=0.0					).set_fill(SCREAMING_SNAKE_CASE__     , opacity=0.7					)
																		cpu_target.move_to(SCREAMING_SNAKE_CASE__					)
																		cpu_target.generate_target()
																		SCREAMING_SNAKE_CASE__		: Tuple     =     0.46 / 4
																		SCREAMING_SNAKE_CASE__		: int     =     0.46 / 3

																		if i == 0:
																									cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)     , buff=0.02     , direction=SCREAMING_SNAKE_CASE__					)
																									cpu_target.target.set_x(cpu_target.target.get_x() + 0.1					)
																		elif i == 3:
																									cpu_target.target.next_to(cpu_targs[0].target     , direction=SCREAMING_SNAKE_CASE__     , buff=0.0					)
																		else:
																									cpu_target.target.next_to(cpu_targs[i - 1].target     , direction=SCREAMING_SNAKE_CASE__     , buff=0.0					)
																		cpu_targs.append(SCREAMING_SNAKE_CASE__					)

																		first_animations.append(rect.animate(run_time=0.5					).set_stroke(SCREAMING_SNAKE_CASE__					)					)
																		second_animations.append(MoveToTarget(SCREAMING_SNAKE_CASE__     , run_time=1.5					)					)

											self.play(*SCREAMING_SNAKE_CASE__					)
											self.play(*SCREAMING_SNAKE_CASE__					)

											self.wait()




