


import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


UpperCamelCase			:					Optional[Any]         =							get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_torch
@require_torchaudio
@require_sentencepiece
class      __snake_case				(   unittest.TestCase):
					def 	a			(						self       :						int  )							->					int:



											'''simple docstring'''





											__magic_name__					=      tempfile.mkdtemp()

											__magic_name__					=      ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
											__magic_name__					=      dict(zip(_lowerCAmelCase	,      range(len(_lowerCAmelCase  )  )  )  )
											__magic_name__					=      Path(self.tmpdirname  )
											save_json(_lowerCAmelCase	,      save_dir / VOCAB_FILES_NAMES['vocab_file']  )
											if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																	copyfile(_lowerCAmelCase	,      save_dir / VOCAB_FILES_NAMES['spm_file']  )

											__magic_name__					=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
											tokenizer.save_pretrained(self.tmpdirname  )

											__magic_name__					=      {
											    'feature_size': 2_4,
											    'num_mel_bins': 2_4,
											    'padding_value': 0.0,
											    'sampling_rate': 1_6_0_0_0,
											    'return_attention_mask': False,
											    'do_normalize': True,
											}
											save_json(_lowerCAmelCase	,      save_dir / FEATURE_EXTRACTOR_NAME  )
					def 	a			(						self       :						Tuple	,      **_lowerCAmelCase       :						List[Any]  )							->					List[Any]:



											'''simple docstring'''





											return SpeechaTextTokenizer.from_pretrained(self.tmpdirname	,      **_lowerCAmelCase  )
					def 	a			(						self       :						Union[str, Any]	,      **_lowerCAmelCase       :						str  )							->					List[str]:



											'''simple docstring'''





											return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname	,      **_lowerCAmelCase  )
					def 	a			(						self       :						Any  )							->					str:



											'''simple docstring'''





											shutil.rmtree(self.tmpdirname  )
					def 	a			(						self       :						Optional[int]  )							->					List[str]:



											'''simple docstring'''





											__magic_name__					=      self.get_tokenizer()
											__magic_name__					=      self.get_feature_extractor()

											__magic_name__					=      SpeechaTextProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											processor.save_pretrained(self.tmpdirname  )
											__magic_name__					=      SpeechaTextProcessor.from_pretrained(self.tmpdirname  )

											self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer.get_vocab()  )
											self.assertIsInstance(processor.tokenizer	,      _lowerCAmelCase  )

											self.assertEqual(processor.feature_extractor.to_json_string()	,      feature_extractor.to_json_string()  )
											self.assertIsInstance(processor.feature_extractor	,      _lowerCAmelCase  )
					def 	a			(						self       :						int  )							->					Tuple:



											'''simple docstring'''





											__magic_name__					=      SpeechaTextProcessor(
											    tokenizer=self.get_tokenizer()	,      feature_extractor=self.get_feature_extractor()  )
											processor.save_pretrained(self.tmpdirname  )

											__magic_name__					=      self.get_tokenizer(bos_token='(BOS)'	,      eos_token='(EOS)'  )
											__magic_name__					=      self.get_feature_extractor(do_normalize=_lowerCAmelCase	,      padding_value=1.0  )

											__magic_name__					=      SpeechaTextProcessor.from_pretrained(
											    self.tmpdirname	,      bos_token='(BOS)'	,      eos_token='(EOS)'	,      do_normalize=_lowerCAmelCase	,      padding_value=1.0  )

											self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()  )
											self.assertIsInstance(processor.tokenizer	,      _lowerCAmelCase  )

											self.assertEqual(processor.feature_extractor.to_json_string()	,      feature_extractor_add_kwargs.to_json_string()  )
											self.assertIsInstance(processor.feature_extractor	,      _lowerCAmelCase  )
					def 	a			(						self       :						Optional[Any]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      SpeechaTextProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											__magic_name__					=      floats_list((3, 1_0_0_0)  )

											__magic_name__					=      feature_extractor(_lowerCAmelCase	,      return_tensors='np'  )
											__magic_name__					=      processor(_lowerCAmelCase	,      return_tensors='np'  )

											for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2  )
					def 	a			(						self       :						Optional[Any]  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      SpeechaTextProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											__magic_name__					=      'This is a test string'

											__magic_name__					=      processor(text=_lowerCAmelCase  )

											__magic_name__					=      tokenizer(_lowerCAmelCase  )

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]  )
					def 	a			(						self       :						Optional[int]  )							->					Any:



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      SpeechaTextProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											__magic_name__					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											__magic_name__					=      processor.batch_decode(_lowerCAmelCase  )
											__magic_name__					=      tokenizer.batch_decode(_lowerCAmelCase  )

											self.assertListEqual(_lowerCAmelCase	,      _lowerCAmelCase  )







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



											'''simple docstring'''





											__magic_name__					=      self.get_feature_extractor()
											__magic_name__					=      self.get_tokenizer()

											__magic_name__					=      SpeechaTextProcessor(tokenizer=_lowerCAmelCase	,      feature_extractor=_lowerCAmelCase  )

											self.assertListEqual(
											    processor.model_input_names	,      feature_extractor.model_input_names	,      msg='`processor` and `feature_extractor` model input names do not match'	,      )




"""simple docstring"""



from __future__ import annotations





def 			_snake_case  (     UpperCAmelCase_	:       list[list[int]]       , UpperCAmelCase_	:       list[list[int]]      ):
     assert len(UpperCAmelCase_      ) == len(UpperCAmelCase_      )

     A__				      =	len(UpperCAmelCase_      )
     A__				      =	list(range(UpperCAmelCase_      )      )
     A__				      =	[-1] * n  # who the donor has donated to
     A__				      =	[-1] * n  # who the recipient has received from
     A__				      =	[0] * n

     while unmatched_donors:
          A__				      =	unmatched_donors[0]
          A__				      =	donor_pref[donor]
          A__				      =	donor_preference[num_donations[donor]]
          num_donations[donor] += 1
          A__				      =	recipient_pref[recipient]
          A__				      =	rec_record[recipient]

          if prev_donor != -1:
               if rec_preference.index(UpperCAmelCase_      ) > rec_preference.index(UpperCAmelCase_      ):
                    A__				      =	donor
                    A__				      =	recipient
                    unmatched_donors.append(UpperCAmelCase_      )
                    unmatched_donors.remove(UpperCAmelCase_      )
          else:
               A__				      =	donor
               A__				      =	recipient
               unmatched_donors.remove(UpperCAmelCase_      )
     return donor_record






