


import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  None
 __snake_case												=  None



 @property
 def 						UpperCamelCase__					(     self ):
       return self.feat_extract_tester.prepare_feat_extract_dict()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''feature_size''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''sampling_rate''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''padding_value''' ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       self.assertTrue(all(len(_UpperCAmelCase ) == len(_UpperCAmelCase ) for x, y in zip(_UpperCAmelCase		,				processed_features[input_name] ) ) )

       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''np''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''pt''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )



 @require_tf
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''tf''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

             for input_slice_a, input_slice_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
                   if not np.allclose(np.asarray(_UpperCAmelCase )		,				np.asarray(_UpperCAmelCase )		,				atol=1E-3 ):
                         return False
             return True

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				self.feat_extract_tester.seq_length_diff
       snake_case_	=				self.feat_extract_tester.max_seq_length + pad_diff
       snake_case_	=				self.feat_extract_tester.min_seq_length
       snake_case_	=				self.feat_extract_tester.batch_size
       snake_case_	=				self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest''' )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[-1] ) )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''max_length''' )[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )
       self.assertTrue(len(input_a[0] ) == pad_min_length )
       self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

       if feature_size > 1:
             self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

       self.assertTrue(all(len(_UpperCAmelCase ) % 10 == 0 for x in input_a ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )

       snake_case_	=				pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(_UpperCAmelCase ) == expected_mult_pad_length for x in input_a ) )
       self.assertEqual(input_a.shape[:2]		,				(batch_size, expected_mult_pad_length) )

       if feature_size > 1:
             self.assertTrue(input_a.shape[2] == feature_size )

       # Check padding value is correct
       snake_case_	=				(np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
           < 1E-3 )
       self.assertTrue(
           abs(
               np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
           < 1E-3 )
       self.assertTrue(
           abs(
               np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
           < 1E-3 )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1E-3 )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
           < 1E-3 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

             for input_slice_a, input_slice_a in zip(_UpperCAmelCase		,				_UpperCAmelCase ):
                   if not np.allclose(np.asarray(_UpperCAmelCase )		,				np.asarray(_UpperCAmelCase )		,				atol=1E-3 ):
                         return False
             return True

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       # truncate to smallest
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] ) )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )

       # truncate to smallest with np
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np'''		,				truncation=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )

       # truncate to middle
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				truncation=_UpperCAmelCase )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				truncation=_UpperCAmelCase )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				truncation=_UpperCAmelCase )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(_UpperCAmelCase ):
             feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				truncation=_UpperCAmelCase )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       snake_case_	=				12
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       snake_case_	=				len(speech_inputs[0] )
       if expected_length % pad_to_multiple_of != 0:
             snake_case_	=				((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0] ) == expected_length )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''pt''' )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )



 @require_tf
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''tf''' )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1E-2 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       self.assertListEqual(list(processed.attention_mask.shape )		,				list(processed[input_name].shape[:2] ) )
       self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()		,				_UpperCAmelCase )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )
       snake_case_	=				min(_UpperCAmelCase )

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape )		,				[processed_pad[input_name].shape[0], max_length] )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()		,				[max_length for x in speech_inputs] )



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Tuple:




      """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 )->  List[Any]:




      """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 )->  str:




      """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 )->  List[str]:




      """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.pipeline(lines[i : i + 100] , tasks=['''cws'''] ).cws
            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 )->  Tuple:




      """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""",
       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      =   parser.parse_args()
   main(args)