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      ):
    '''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    (  SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







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



def lowerCamelCase    (  SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:Union[str, Any]         =						set()

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



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







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

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



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:List[Any]         =						[]

    for i in range(0    ,     len(lowercase_      )    ,     100      ):
        __UpperCamelCase	:str         =						ltp_tokenizer.pipeline(lines[i : i + 100]    ,     tasks=['''cws''']      ).cws
        __UpperCamelCase	:Dict         =						[get_chinese_word(lowercase_      ) for r in res]
        ltp_res.extend(lowercase_      )
    assert len(lowercase_      ) == len(lowercase_      )

    __UpperCamelCase	:Dict         =						[]
    for i in range(0    ,     len(lowercase_      )    ,     100      ):
        __UpperCamelCase	:Dict         =						bert_tokenizer(lines[i : i + 100]    ,     add_special_tokens=lowercase_    ,     truncation=lowercase_    ,     max_length=512      )
        bert_res.extend(res['''input_ids''']      )
    assert len(lowercase_      ) == len(lowercase_      )

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

    assert len(lowercase_      ) == len(lowercase_      )

    return ref_ids



def lowerCamelCase    (  SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







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

    __UpperCamelCase	:Union[str, Any]         =						prepare_ref(lowercase_    ,     lowercase_    ,     lowercase_      )

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


if __name__ == "__main__":
    __lowercase									=   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''',
    )

    __lowercase									=   parser.parse_args()
    main(args)









import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset

lowerCAmelCase       		=    random.Random()




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=1.0      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







						if rng is None:
												__UpperCAmelCase			:							str	    =	global_rng

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

						return values




class 							lowerCamelCase   (  unittest.TestCase ):


	def __init__(    self				,     lowercase__				,     lowercase__=7				,     lowercase__=4_0_0				,     lowercase__=2_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=1_2_8				,     lowercase__=1				,     lowercase__=5_1_2				,     lowercase__=3_0				,     lowercase__=4_4_1_0_0				,     ):
							__UpperCAmelCase			:							Optional[Any]	    =	parent
							__UpperCAmelCase			:							Any	    =	batch_size
							__UpperCAmelCase			:							int	    =	min_seq_length
							__UpperCAmelCase			:							List[str]	    =	max_seq_length
							__UpperCAmelCase			:							Dict	    =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__UpperCAmelCase			:							Any	    =	spectrogram_length
							__UpperCAmelCase			:							List[Any]	    =	feature_size
							__UpperCAmelCase			:							Union[str, Any]	    =	num_audio_channels
							__UpperCAmelCase			:							Optional[int]	    =	hop_length
							__UpperCAmelCase			:							Tuple	    =	chunk_length
							__UpperCAmelCase			:							Any	    =	sampling_rate


	def  A(    self):
							return {
							    "spectrogram_length": self.spectrogram_length,
							    "feature_size": self.feature_size,
							    "num_audio_channels": self.num_audio_channels,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "sampling_rate": self.sampling_rate,
							}


	def  A(    self				,     lowercase__=False				,     lowercase__=False):
							def _flatten(lowercase__):
													return list(itertools.chain(*lowercase__))

							if equal_length:
													__UpperCAmelCase			:							str	    =	[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
							else:
													# make sure that inputs increase in size
													__UpperCAmelCase			:							List[str]	    =	[
													    floats_list((x, self.feature_size))
													    for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff)
													]
							if numpify:
													__UpperCAmelCase			:							List[str]	    =	[np.asarray(lowercase__) for x in speech_inputs]
							return speech_inputs






@require_torch
@require_torchaudio
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[int]										=						TvltFeatureExtractor


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	TvltFeatureExtractionTester(self)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)
							self.assertTrue(hasattr(lowercase__				,     '''spectrogram_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''feature_size'''))
							self.assertTrue(hasattr(lowercase__				,     '''num_audio_channels'''))
							self.assertTrue(hasattr(lowercase__				,     '''hop_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''chunk_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''sampling_rate'''))


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							str	    =	feat_extract_first.save_pretrained(lowercase__)[0]
													check_json_file_has_correct_format(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class.from_pretrained(lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							Union[str, Any]	    =	os.path.join(lowercase__				,     '''feat_extract.json''')
													feat_extract_first.to_json_file(lowercase__)
													__UpperCAmelCase			:							str	    =	self.feature_extraction_class.from_json_file(lowercase__)

							__UpperCAmelCase			:							Any	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Tuple	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							List[str]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							# Initialize feature_extractor
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							# create three inputs of length 800, 1000, and 1200
							__UpperCAmelCase			:							Optional[int]	    =	[floats_list((1, x))[0] for x in range(8_0_0				,     1_4_0_0				,     2_0_0)]
							__UpperCAmelCase			:							int	    =	[np.asarray(lowercase__) for speech_input in speech_inputs]

							# Test not batched input
							__UpperCAmelCase			:							Dict	    =	feature_extractor(np_speech_inputs[0]				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test batched
							__UpperCAmelCase			:							List[str]	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test audio masking
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(
							    lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0				,     mask_audio=lowercase__).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test 2-D numpy arrays are batched.
							__UpperCAmelCase			:							Any	    =	[floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							__UpperCAmelCase			:							Optional[Any]	    =	np.asarray(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values
							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,     '''clean'''				,     split='''validation''')
							# automatic decoding with librispeech
							__UpperCAmelCase			:							int	    =	ds.sort('''id''').select(range(lowercase__))[:num_samples]['''audio''']

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


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self._load_datasamples(1)
							__UpperCAmelCase			:							Tuple	    =	TvltFeatureExtractor()
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''pt''').audio_values

							self.assertEquals(audio_values.shape				,     (1, 1, 1_9_2, 1_2_8))

							__UpperCAmelCase			:							int	    =	torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]])
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,     lowercase__				,     atol=1e-4))

