


"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def    _lowerCamelCase   (     lowerCamelCase__	:							Any  ):
 # 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 >= 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   (     lowerCamelCase__	:							str  ):
 # word like '180' or '身高' or '神'
 for char in word:
  lowercase__       :       List[Any]				=						ord(lowerCamelCase__  )
  if not _is_chinese_char(lowerCamelCase__  ):
   return 0
 return 1


def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]  ):
 lowercase__       :       Optional[Any]				=						set()

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


def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							set()  ):
 if not chinese_word_set:
  return bert_tokens
 lowercase__       :       Optional[Any]				=						max([len(lowerCamelCase__  ) for w in chinese_word_set]  )

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


def    _lowerCamelCase   (     lowerCamelCase__	:							List[str]       ,		lowerCamelCase__	:							LTP       ,		lowerCamelCase__	:							BertTokenizer  ):
 lowercase__       :       List[str]				=						[]

 for i in range(0       ,		len(lowerCamelCase__  )       ,		1_00  ):
  lowercase__       :       str				=						ltp_tokenizer.pipeline(lines[i : i + 1_00]       ,		tasks=["""cws"""]  ).cws
  lowercase__       :       List[Any]				=						[get_chinese_word(lowerCamelCase__  ) for r in res]
  ltp_res.extend(lowerCamelCase__  )
 assert len(lowerCamelCase__  ) == len(lowerCamelCase__  )

 lowercase__       :       Optional[Any]				=						[]
 for i in range(0       ,		len(lowerCamelCase__  )       ,		1_00  ):
  lowercase__       :       Dict				=						bert_tokenizer(lines[i : i + 1_00]       ,		add_special_tokens=lowerCamelCase__       ,		truncation=lowerCamelCase__       ,		max_length=5_12  )
  bert_res.extend(res["""input_ids"""]  )
 assert len(lowerCamelCase__  ) == len(lowerCamelCase__  )

 lowercase__       :       Optional[Any]				=						[]
 for input_ids, chinese_word in zip(lowerCamelCase__       ,		lowerCamelCase__  ):
  lowercase__       :       Tuple				=						[]
  for id in input_ids:
   lowercase__       :       int				=						bert_tokenizer._convert_id_to_token(lowerCamelCase__  )
   input_tokens.append(lowerCamelCase__  )
  lowercase__       :       str				=						add_sub_symbol(lowerCamelCase__       ,		lowerCamelCase__  )
  lowercase__       :       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] == "##":
    lowercase__       :       Tuple				=						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    _lowerCamelCase   (     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:
  lowercase__       :       Optional[Any]				=						f.readlines()
 lowercase__       :       List[Any]				=						[line.strip() for line in data if len(lowerCamelCase__  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
 lowercase__       :       Union[str, Any]				=						LTP(args.ltp  )  # faster in GPU device
 lowercase__       :       str				=						BertTokenizer.from_pretrained(args.bert  )

 lowercase__       :       Union[str, Any]				=						prepare_ref(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )

 with open(args.save_path       ,		"""w"""       ,		encoding="""utf-8"""  ) as f:
  lowercase__       :       Tuple				=						[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',
       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',
   )

   __snake_case    					=					parser.parse_args()
   main(args)



"""simple docstring"""


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


__snake_case    					=					get_tests_dir('fixtures')

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							str:
    # A mock response for an HTTP head request to emulate server down
    lowercase__       :       List[str]				=						mock.Mock()
    lowercase__       :       Dict				=						500
    lowercase__       :       Any				=						{}
    lowercase__       :       int				=						HTTPError
    lowercase__       :       Optional[int]				=						{}

    # Download this model to make sure it's in the cache.
    lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""						)
    # Under the mock environment we get a 500 error when trying to reach the model.
    with mock.patch("""requests.Session.request""" ,     return_value=lowerCamelCase__						) as mock_head:
     lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""						)
     # This check we did call the fake head request
     mock_head.assert_called()


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    # This test is for deprecated behavior and can be removed in v5
    lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained(
        """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""						)







   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    with self.assertRaises(lowerCamelCase__						):
     # config is in subfolder, the following should not work without specifying the subfolder
     lowercase__       :       List[str]				=						AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""						)

    lowercase__       :       Any				=						AutoImageProcessor.from_pretrained(
        """hf-internal-testing/stable-diffusion-all-variants""" ,     subfolder="""feature_extractor"""						)

    self.assertIsNotNone(lowerCamelCase__						)

@is_staging_test
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							int:
    lowercase__       :       str				=						TOKEN
    HfFolder.save_token(lowerCamelCase__						)


   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							List[str]:
    try:
     delete_repo(token=cls._token ,     repo_id="""test-image-processor"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""valid_org/test-image-processor-org"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""test-dynamic-image-processor"""						)
    except HTTPError:
     pass


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[str]				=						ViTImageProcessor.from_pretrained(lowerCamelCase__						)
    image_processor.push_to_hub("""test-image-processor""" ,     use_auth_token=self._token						)

    lowercase__       :       str				=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

    # Reset repo
    delete_repo(token=self._token ,     repo_id="""test-image-processor"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     image_processor.save_pretrained(
         lowerCamelCase__ ,     repo_id="""test-image-processor""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       Union[str, Any]				=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						ViTImageProcessor.from_pretrained(lowerCamelCase__						)
    image_processor.push_to_hub("""valid_org/test-image-processor""" ,     use_auth_token=self._token						)

    lowercase__       :       Any				=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

    # Reset repo
    delete_repo(token=self._token ,     repo_id="""valid_org/test-image-processor"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     image_processor.save_pretrained(
         lowerCamelCase__ ,     repo_id="""valid_org/test-image-processor-org""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       List[Any]				=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    CustomImageProcessor.register_for_auto_class()
    lowercase__       :       List[str]				=						CustomImageProcessor.from_pretrained(lowerCamelCase__						)

    image_processor.push_to_hub("""test-dynamic-image-processor""" ,     use_auth_token=self._token						)

    # This has added the proper auto_map field to the config
    self.assertDictEqual(
        image_processor.auto_map ,     {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""} ,     )

    lowercase__       :       Dict				=						AutoImageProcessor.from_pretrained(
        F'''{USER}/test-dynamic-image-processor''' ,     trust_remote_code=lowerCamelCase__						)
    # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
    self.assertEqual(new_image_processor.__class__.__name__ ,     """CustomImageProcessor"""						)