


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      if (
          (cp >= 0X4E00 and cp <= 0X9FFF)
          or (cp >= 0X3400 and cp <= 0X4DBF)  #
          or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
          or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
          or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
          or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
          or (cp >= 0XF900 and cp <= 0XFAFF)
          or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
      ):  #
            return True

      return False






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      for char in word:
            snake_case_ =   ord(SCREAMING_SNAKE_CASE      )
            if not _is_chinese_char(SCREAMING_SNAKE_CASE      ):
                  return 0
      return 1






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      snake_case_ =   set()

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      if not chinese_word_set:
            return bert_tokens
      snake_case_ =   max([len(SCREAMING_SNAKE_CASE      ) for w in chinese_word_set]      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      snake_case_ =   []

      for i in range(0      ,      len(SCREAMING_SNAKE_CASE      )      ,      100      ):
            snake_case_ =   ltp_tokenizer.seg(lines[i : i + 100]      )[0]
            snake_case_ =   [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      )

      snake_case_ =   []
      for i in range(0      ,      len(SCREAMING_SNAKE_CASE      )      ,      100      ):
            snake_case_ =   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      )

      snake_case_ =   []
      for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ):
            snake_case_ =   []
            for id in input_ids:
                  snake_case_ =   bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE      )
                  input_tokens.append(SCREAMING_SNAKE_CASE      )
            snake_case_ =   add_sub_symbol(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
            snake_case_ =   []
            # 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] == "##":
                        snake_case_ =   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					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

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

      snake_case_ =   prepare_ref(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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


if __name__ == "__main__":
   UpperCAmelCase    =					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""")

   UpperCAmelCase    =					parser.parse_args()
   main(args)



import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   hf_hub_download(
           repo_id='''nateraw/video-demo'''	,   filename='''archery.mp4'''	,   repo_type='''dataset'''      )
       snake_case_ =   VideoClassificationPipeline(model=_UpperCAmelCase	,   image_processor=_UpperCAmelCase	,   top_k=2      )
       snake_case_ =   [
           example_video_filepath,
           '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
       ]
       return video_classifier, examples



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       for example in examples:
             snake_case_ =   video_classifier(_UpperCAmelCase      )

             self.assertEqual(
                 _UpperCAmelCase	,   [
                     {'''score''': ANY(_UpperCAmelCase      ), '''label''': ANY(_UpperCAmelCase      )},
                     {'''score''': ANY(_UpperCAmelCase      ), '''label''': ANY(_UpperCAmelCase      )},
                 ]	,   )



 @require_torch
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
       snake_case_ =   VideoMAEFeatureExtractor(
           size={'''shortest_edge''': 10}	,   crop_size={'''height''': 10, '''width''': 10}      )
       snake_case_ =   pipeline(
           '''video-classification'''	,   model=_UpperCAmelCase	,   feature_extractor=_UpperCAmelCase	,   frame_sampling_rate=4      )

       snake_case_ =   hf_hub_download(repo_id='''nateraw/video-demo'''	,   filename='''archery.mp4'''	,   repo_type='''dataset'''      )
       snake_case_ =   video_classifier(_UpperCAmelCase	,   top_k=2      )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase	,   decimals=4      )	,   [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}]	,   )

       snake_case_ =   video_classifier(
           [
               video_file_path,
               video_file_path,
           ]	,   top_k=2	,   )
       self.assertEqual(
           nested_simplify(_UpperCAmelCase	,   decimals=4      )	,   [
               [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
               [{'''score''': 0.5_199, '''label''': '''LABEL_0'''}, {'''score''': 0.4_801, '''label''': '''LABEL_1'''}],
           ]	,   )





 @require_tf
 def 		UpperCamelCase__  (			self      ):
       pass