'''simple docstring'''


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


snake_case__	:     str      =    get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = tempfile.mkdtemp()

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

       UpperCAmelCase_				:  List[str]  = SpeechaTextTokenizer.from_pretrained(self.tmpdirname		)
       tokenizer.save_pretrained(self.tmpdirname		)

       UpperCAmelCase_				:  str  = {
           '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(snake_case_ ,						save_dir / FEATURE_EXTRACTOR_NAME		)


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname ,						**snake_case_		)


  def _UpperCamelCase	(					self ,						**snake_case_		):




       '''simple docstring'''

       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname ,						**snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       shutil.rmtree(self.tmpdirname		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.get_tokenizer()
       UpperCAmelCase_				:  int  = self.get_feature_extractor()

       UpperCAmelCase_				:  List[str]  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       processor.save_pretrained(self.tmpdirname		)
       UpperCAmelCase_				:  Dict  = SpeechaTextProcessor.from_pretrained(self.tmpdirname		)

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

       self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor.to_json_string()		)
       self.assertIsInstance(processor.feature_extractor ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = SpeechaTextProcessor(
           tokenizer=self.get_tokenizer() ,						feature_extractor=self.get_feature_extractor()		)
       processor.save_pretrained(self.tmpdirname		)

       UpperCAmelCase_				:  Optional[Any]  = self.get_tokenizer(bos_token='(BOS)' ,						eos_token='(EOS)'		)
       UpperCAmelCase_				:  Union[str, Any]  = self.get_feature_extractor(do_normalize=snake_case_ ,						padding_value=1.0		)

       UpperCAmelCase_				:  Any  = SpeechaTextProcessor.from_pretrained(
           self.tmpdirname ,						bos_token='(BOS)' ,						eos_token='(EOS)' ,						do_normalize=snake_case_ ,						padding_value=1.0		)

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

       self.assertEqual(processor.feature_extractor.to_json_string() ,						feature_extractor_add_kwargs.to_json_string()		)
       self.assertIsInstance(processor.feature_extractor ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Union[str, Any]  = self.get_tokenizer()

       UpperCAmelCase_				:  Union[str, Any]  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  List[Any]  = floats_list((3, 1_0_0_0)		)

       UpperCAmelCase_				:  Union[str, Any]  = feature_extractor(snake_case_ ,						return_tensors='np'		)
       UpperCAmelCase_				:  Any  = processor(snake_case_ ,						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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Tuple  = self.get_tokenizer()

       UpperCAmelCase_				:  Tuple  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Tuple  = 'This is a test string'

       UpperCAmelCase_				:  List[str]  = processor(text=snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = tokenizer(snake_case_		)

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Dict  = self.get_tokenizer()

       UpperCAmelCase_				:  Any  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

       UpperCAmelCase_				:  Dict  = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCAmelCase_				:  str  = processor.batch_decode(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.batch_decode(snake_case_		)

       self.assertListEqual(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.get_feature_extractor()
       UpperCAmelCase_				:  Union[str, Any]  = self.get_tokenizer()

       UpperCAmelCase_				:  int  = SpeechaTextProcessor(tokenizer=snake_case_ ,						feature_extractor=snake_case_		)

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







from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


if __name__ == "__main__":
      print(F"{solution() = }")