"""simple docstring"""




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

UpperCamelCase_									=random.Random()





def 	a_    (							_lowercase	,						_lowercase=1.0	,						_lowercase=None	,						_lowercase=None       ):
						if rng is None:
												_UpperCamelCase      : Union[str, Any]         =   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 			_a						(				unittest.TestCase    ):






				def __init__(			self		:   Optional[int],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Any=7,			lowerCAmelCase__		:   str=4_0_0,			lowerCAmelCase__		:   Tuple=2_0_0_0,			lowerCAmelCase__		:   List[str]=2_0_4_8,			lowerCAmelCase__		:   int=1_2_8,			lowerCAmelCase__		:   Union[str, Any]=1,			lowerCAmelCase__		:   Union[str, Any]=5_1_2,			lowerCAmelCase__		:   Optional[int]=3_0,			lowerCAmelCase__		:   int=4_4_1_0_0,			)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   parent
										_UpperCamelCase      : Any         =   batch_size
										_UpperCamelCase      : Union[str, Any]         =   min_seq_length
										_UpperCamelCase      : Any         =   max_seq_length
										_UpperCamelCase      : int         =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										_UpperCamelCase      : int         =   spectrogram_length
										_UpperCamelCase      : int         =   feature_size
										_UpperCamelCase      : List[Any]         =   num_audio_channels
										_UpperCamelCase      : int         =   hop_length
										_UpperCamelCase      : List[str]         =   chunk_length
										_UpperCamelCase      : Optional[Any]         =   sampling_rate






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Dict:




										'''simple docstring'''



										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     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Optional[Any]=False,			lowerCAmelCase__		:   List[str]=False							)     ->				str:




										'''simple docstring'''



										def _flatten(lowerCAmelCase__		:   Union[str, Any]							):
																return list(itertools.chain(*_A							)							)

										if equal_length:
																_UpperCamelCase      : Dict         =   [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
										else:
																# make sure that inputs increase in size
																_UpperCamelCase      : int         =   [
																    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      : Union[str, Any]         =   [np.asarray(_A							) for x in speech_inputs]
										return speech_inputs




@require_torch
@require_torchaudio
class 			_a						(				snake_case_     ,    unittest.TestCase    ):
				UpperCamelCase								=   TvltFeatureExtractor






				def     snake_case     (			self		:   int							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   TvltFeatureExtractionTester(self							)






				def     snake_case     (			self		:   Optional[Any]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self.feature_extraction_class(**self.feat_extract_dict							)
										self.assertTrue(hasattr(_A,			'''spectrogram_length'''							)							)
										self.assertTrue(hasattr(_A,			'''feature_size'''							)							)
										self.assertTrue(hasattr(_A,			'''num_audio_channels'''							)							)
										self.assertTrue(hasattr(_A,			'''hop_length'''							)							)
										self.assertTrue(hasattr(_A,			'''chunk_length'''							)							)
										self.assertTrue(hasattr(_A,			'''sampling_rate'''							)							)






				def     snake_case     (			self		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.feature_extraction_class(**self.feat_extract_dict							)

										with tempfile.TemporaryDirectory() as tmpdirname:
																_UpperCamelCase      : List[Any]         =   feat_extract_first.save_pretrained(_A							)[0]
																check_json_file_has_correct_format(_A							)
																_UpperCamelCase      : List[str]         =   self.feature_extraction_class.from_pretrained(_A							)

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






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   self.feature_extraction_class(**self.feat_extract_dict							)

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

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






				def     snake_case     (			self		:   Any							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.feature_extraction_class(**self.feat_extract_dict							)

										# create three inputs of length 800, 1000, and 1200
										_UpperCamelCase      : List[str]         =   [floats_list((1, x)							)[0] for x in range(8_0_0,			1_4_0_0,			2_0_0							)]
										_UpperCamelCase      : Tuple         =   [np.asarray(_A							) for speech_input in speech_inputs]

										# Test not batched input
										_UpperCamelCase      : List[Any]         =   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[Any]         =   feature_extractor(_A,			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      : Optional[Any]         =   feature_extractor(
										    _A,			return_tensors='''np''',			sampling_rate=4_4_1_0_0,			mask_audio=_A							).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      : List[Any]         =   np.asarray(_A							)
										_UpperCamelCase      : List[Any]         =   feature_extractor(_A,			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     snake_case     (			self		:   int,			lowerCAmelCase__		:   Dict							)     ->				Tuple:




										'''simple docstring'''



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

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






				def     snake_case     (			self		:   Any							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   self._load_datasamples(1							)
										_UpperCamelCase      : Tuple         =   TvltFeatureExtractor()
										_UpperCamelCase      : Optional[Any]         =   feature_extractor(_A,			return_tensors='''pt'''							).audio_values

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

										_UpperCamelCase      : Any         =   torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]							)
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],			_A,			atol=1e-4							)							)










"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# 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 >= 0X20000 and cp <= 0X2A6DF)  #
						    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
						    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
						    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
						    or (cp >= 0XF900 and cp <= 0XFAFF)
						    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
						):  #
												return True

						return False





def 	a_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

						for token in tokens:
												_UpperCamelCase      : int         =   len(_lowercase       ) > 1 and is_chinese(_lowercase       )
												if chinese_word:
																		word_set.add(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   list(_lowercase       )
						return word_list





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[int]         =   [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      : Optional[int]         =   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      : Optional[Any]         =   []
						for input_ids, chinese_word in zip(_lowercase	,						_lowercase       ):
												_UpperCamelCase      : str         =   []
												for id in input_ids:
																		_UpperCamelCase      : Dict         =   bert_tokenizer._convert_id_to_token(_lowercase       )
																		input_tokens.append(_lowercase       )
												_UpperCamelCase      : str         =   add_sub_symbol(_lowercase	,						_lowercase       )
												_UpperCamelCase      : List[str]         =   []
												# 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      : int         =   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 	a_    (							_lowercase       ):
						# 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:
												_UpperCamelCase      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

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


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)



