
"""simple docstring"""





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        _UpperCAmelCase(       lowerCamelCase      ):
		# to overwrite at feature extractactor specific tests
		lowercase__         = None
		lowercase__         = None



		@property
		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							return self.feat_extract_tester.prepare_feat_extract_dict()



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							self.assertTrue(hasattr(__a							,						'''feature_size'''))
							self.assertTrue(hasattr(__a							,						'''sampling_rate'''))
							self.assertTrue(hasattr(__a							,						'''padding_value'''))



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							_UpperCamelCase									=    feat_extract.model_input_names[0]

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

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

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

							_UpperCamelCase									=    processed_features[input_name]

							if len(batch_features_input.shape) < 3:
												_UpperCamelCase									=    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)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a)
							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							_UpperCamelCase									=    feat_extract.model_input_names[0]

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

							_UpperCamelCase									=    processed_features[input_name]

							if len(batch_features_input.shape) < 3:
												_UpperCamelCase									=    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)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a)
							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							_UpperCamelCase									=    feat_extract.model_input_names[0]

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

							_UpperCamelCase									=    processed_features[input_name]

							if len(batch_features_input.shape) < 3:
												_UpperCamelCase									=    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							,						__a=False)			->			Union[str, Any]:






							'''simple docstring'''
							def _inputs_have_equal_length(__a):
												_UpperCamelCase									=    len(input[0])
												for input_slice in input[1:]:
																	if len(__a) != length:
																						return False
												return True

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

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

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

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

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

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

							_UpperCamelCase									=    feat_extract.pad(__a							,						padding='''longest''')
							_UpperCamelCase									=    input_a[input_name]

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

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

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

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

							self.assertFalse(_inputs_have_equal_length(__a))
							self.assertTrue(_inputs_have_equal_length(__a))
							self.assertTrue(_inputs_have_equal_length(__a))
							self.assertTrue(_inputs_are_equal(__a							,						__a))
							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
							_UpperCamelCase									=    feat_extract.pad(__a							,						pad_to_multiple_of=10)
							_UpperCamelCase									=    input_a[input_name]

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

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

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

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

							_UpperCamelCase									=    pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(__a) == 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
							_UpperCamelCase									=    (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							,						__a=False)			->			List[Any]:






							'''simple docstring'''
							def _inputs_have_equal_length(__a):
												_UpperCamelCase									=    len(input[0])
												for input_slice in input[1:]:
																	if len(__a) != length:
																						return False
												return True

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

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

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

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

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

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

							self.assertTrue(_inputs_have_equal_length(__a))
							self.assertFalse(_inputs_have_equal_length(__a))

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

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

							self.assertTrue(_inputs_have_equal_length(__a))
							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(__a))

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

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

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

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

							# 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(__a))
							self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

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

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

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

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

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

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

							# retrieve expected_length as multiple of pad_to_multiple_of
							_UpperCamelCase									=    len(speech_inputs[0])
							if expected_length % pad_to_multiple_of != 0:
												_UpperCamelCase									=    ((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(__a))
							self.assertFalse(_inputs_have_equal_length(__a))



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							self._check_padding(numpify=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							self._check_padding(numpify=__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							self._check_truncation(numpify=__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self._check_truncation(numpify=__a)



		@require_torch
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase									=    feat_extract.model_input_names[0]

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

							_UpperCamelCase									=    feat_extract.pad(__a							,						padding='''longest'''							,						return_tensors='''np''')[input_name]
							_UpperCamelCase									=    feat_extract.pad(__a							,						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)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feature_extraction_class(**self.feat_extract_dict)
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase									=    feat_extract.model_input_names[0]

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

							_UpperCamelCase									=    feat_extract.pad(__a							,						padding='''longest'''							,						return_tensors='''np''')[input_name]
							_UpperCamelCase									=    feat_extract.pad(__a							,						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)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_dict
							_UpperCamelCase									=    True
							_UpperCamelCase									=    self.feature_extraction_class(**__a)
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase									=    [len(__a) for x in speech_inputs]
							_UpperCamelCase									=    feat_extract.model_input_names[0]

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})

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




		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.feat_extract_dict
							_UpperCamelCase									=    True
							_UpperCamelCase									=    self.feature_extraction_class(**__a)
							_UpperCamelCase									=    self.feat_extract_tester.prepare_inputs_for_common()
							_UpperCamelCase									=    [len(__a) for x in speech_inputs]
							_UpperCamelCase									=    feat_extract.model_input_names[0]

							_UpperCamelCase									=    BatchFeature({input_name: speech_inputs})
							_UpperCamelCase									=    min(__a)

							_UpperCamelCase									=    feat_extract.pad(
							    __a							,						padding='''max_length'''							,						max_length=__a							,						truncation=__a							,						return_tensors='''np''')
							self.assertIn('''attention_mask'''							,						__a)
							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])








"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""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__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





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

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_UpperCamelCase									=    [get_chinese_word(__snake_case      ) for r in res]
										ltp_res.extend(__snake_case      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    bert_tokenizer(lines[i : i + 1_00],	add_special_tokens=__snake_case,	truncation=__snake_case,	max_length=5_12      )
										bert_res.extend(res['''input_ids''']      )
					assert len(__snake_case      ) == len(__snake_case      )

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

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





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

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

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


if __name__ == "__main__":
				_a								=						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""",
				)

				_a								=						parser.parse_args()
				main(args)






