






"""simple docstring"""


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if (
      (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
      or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
      or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
      or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
      or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
      or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
      or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
      or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
  ):  #
    return True

  return False



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  for char in word:
    __A									= ord(__UpperCamelCase )
    if not _is_chinese_char(__UpperCamelCase ):
      return 0
  return 1



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= set()

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



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







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

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



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= []

  for i in range(0		,       len(__UpperCamelCase )		,       1_0_0 ):
    __A									= ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
    __A									= [get_chinese_word(__UpperCamelCase ) for r in res]
    ltp_res.extend(__UpperCamelCase )
  assert len(__UpperCamelCase ) == len(__UpperCamelCase )

  __A									= []
  for i in range(0		,       len(__UpperCamelCase )		,       1_0_0 ):
    __A									= bert_tokenizer(lines[i : i + 1_0_0]		,       add_special_tokens=__UpperCamelCase		,       truncation=__UpperCamelCase		,       max_length=5_1_2 )
    bert_res.extend(res['''input_ids'''] )
  assert len(__UpperCamelCase ) == len(__UpperCamelCase )

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

  assert len(__UpperCamelCase ) == len(__UpperCamelCase )

  return ref_ids



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







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

  __A									= prepare_ref(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

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


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"""


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase_				     =     random.Random()

if is_torch_available():
       import torch



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase=1.0		,       __UpperCamelCase=None		,       __UpperCamelCase=None ):




  """simple docstring"""







  if rng is None:
    __A									= global_rng

  __A									= []
  for batch_idx in range(shape[0] ):
    values.append([] )
    for _ in range(shape[1] ):
      values[-1].append(rng.random() * scale )

  return values


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     Any,       _lowerCamelCase :     List[str],       _lowerCamelCase :     Any=7,       _lowerCamelCase :     Optional[int]=4_00,       _lowerCamelCase :     Optional[int]=20_00,       _lowerCamelCase :     Dict=1,       _lowerCamelCase :     Optional[Any]=0.0,       _lowerCamelCase :     int=1_60_00,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     Dict=True,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= min_seq_length
     __A									= max_seq_length
     __A									= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __A									= feature_size
     __A									= padding_value
     __A									= sampling_rate
     __A									= return_attention_mask
     __A									= do_normalize


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }






   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     Optional[Any]=False,       _lowerCamelCase :     int=False  ):

     '''simple docstring'''
     def _flatten(_lowerCamelCase :     List[str]  ):
       return list(itertools.chain(*_lowerCamelCase  )  )

     if equal_length:
       __A									= floats_list((self.batch_size, self.max_seq_length)  )
     else:
       # make sure that inputs increase in size
       __A									= [
           _flatten(floats_list((x, self.feature_size)  )  )
           for x in range(self.min_seq_length,       self.max_seq_length,       self.seq_length_diff  )
       ]

     if numpify:
       __A									= [np.asarray(_lowerCamelCase  ) for x in speech_inputs]

     return speech_inputs





@require_torch
@require_torchaudio
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	int						 = ASTFeatureExtractor


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ASTFeatureExtractionTester(self  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     # Tests that all call wrap to encode_plus and batch_encode_plus
     __A									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     # create three inputs of length 800, 1000, and 1200
     __A									= [floats_list((1, x)  )[0] for x in range(8_00,       14_00,       2_00  )]
     __A									= [np.asarray(_lowerCamelCase  ) for speech_input in speech_inputs]

     # Test not batched input
     __A									= feat_extract(speech_inputs[0],       return_tensors='''np'''  ).input_values
     __A									= feat_extract(np_speech_inputs[0],       return_tensors='''np'''  ).input_values
     self.assertTrue(np.allclose(_lowerCamelCase,       _lowerCamelCase,       atol=1e-3  )  )

     # Test batched
     __A									= feat_extract(_lowerCamelCase,       padding=_lowerCamelCase,       return_tensors='''np'''  ).input_values
     __A									= feat_extract(_lowerCamelCase,       padding=_lowerCamelCase,       return_tensors='''np'''  ).input_values
     for enc_seq_a, enc_seq_a in zip(_lowerCamelCase,       _lowerCamelCase  ):
       self.assertTrue(np.allclose(_lowerCamelCase,       _lowerCamelCase,       atol=1e-3  )  )

     # Test 2-D numpy arrays are batched.
     __A									= [floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
     __A									= np.asarray(_lowerCamelCase  )
     __A									= feat_extract(_lowerCamelCase,       return_tensors='''np'''  ).input_values
     __A									= feat_extract(_lowerCamelCase,       return_tensors='''np'''  ).input_values
     for enc_seq_a, enc_seq_a in zip(_lowerCamelCase,       _lowerCamelCase  ):
       self.assertTrue(np.allclose(_lowerCamelCase,       _lowerCamelCase,       atol=1e-3  )  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     import torch

     __A									= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
     __A									= np.random.rand(1_00  ).astype(np.floataa  )
     __A									= np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
       __A									= feature_extractor.pad([{'''input_values''': inputs}],       return_tensors='''np'''  )
       self.assertTrue(np_processed.input_values.dtype == np.floataa  )
       __A									= feature_extractor.pad([{'''input_values''': inputs}],       return_tensors='''pt'''  )
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     from datasets import load_dataset

     __A									= load_dataset('''hf-internal-testing/librispeech_asr_dummy''',       '''clean''',       split='''validation'''  )
     # automatic decoding with librispeech
     __A									= ds.sort('''id'''  ).select(range(_lowerCamelCase  )  )[:num_samples]['''audio''']

     return [x["array"] for x in speech_samples]






   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     # fmt: off
     __A									= torch.tensor(
         [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
          -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
          -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
          -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]  )
     # fmt: on

     __A									= self._load_datasamples(1  )
     __A									= ASTFeatureExtractor()
     __A									= feature_extractor(_lowerCamelCase,       return_tensors='''pt'''  ).input_values
     self.assertEquals(input_values.shape,       (1, 10_24, 1_28)  )
     self.assertTrue(torch.allclose(input_values[0, 0, :30],       _lowerCamelCase,       atol=1e-4  )  )




