




'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
      from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

      from .test_feature_extraction_speechta import floats_list


lowercase			:      Optional[int]			=					get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")







@require_torch
class 				_a   (unittest.TestCase  ):





     '''simple docstring'''



     def  snake_case_			(							self       )			->							str:
          snake_case :			Optional[int]					   = tempfile.mkdtemp()

          snake_case :			Tuple					   = SpeechTaTokenizer(__a       )
          tokenizer.save_pretrained(self.tmpdirname       )

          snake_case :			List[Any]					   = {
              """feature_size""": 1,
              """padding_value""": 0.0,
              """sampling_rate""": 16_000,
              """do_normalize""": False,
              """num_mel_bins""": 80,
              """hop_length""": 16,
              """win_length""": 64,
              """win_function""": """hann_window""",
              """fmin""": 80,
              """fmax""": 7_600,
              """mel_floor""": 1E-10,
              """reduction_factor""": 2,
              """return_attention_mask""": True,
          }

          snake_case :			Optional[int]					   = os.path.join(self.tmpdirname       ,__a       )
          with open(self.feature_extraction_file       ,"""w"""       ,encoding="""utf-8"""       ) as fp:
               fp.write(json.dumps(__a       ) + """\n"""       )







     def  snake_case_			(							self       ,**__a       )			->							str:
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,**__a       )







     def  snake_case_			(							self       ,**__a       )			->							Optional[int]:
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,**__a       )







     def  snake_case_			(							self       )			->							Optional[Any]:
          shutil.rmtree(self.tmpdirname       )







     def  snake_case_			(							self       )			->							List[Any]:
          snake_case :			Tuple					   = self.get_tokenizer()
          snake_case :			str					   = self.get_feature_extractor()

          snake_case :			Dict					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          processor.save_pretrained(self.tmpdirname       )
          snake_case :			Union[str, Any]					   = SpeechTaProcessor.from_pretrained(self.tmpdirname       )

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

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







     def  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			int					   = SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()       )
          processor.save_pretrained(self.tmpdirname       )

          snake_case :			List[Any]					   = self.get_tokenizer(bos_token="""(BOS)"""       ,eos_token="""(EOS)"""       )
          snake_case :			Tuple					   = self.get_feature_extractor(do_normalize=__a       ,padding_value=1.0       )

          snake_case :			Tuple					   = SpeechTaProcessor.from_pretrained(
              self.tmpdirname       ,bos_token="""(BOS)"""       ,eos_token="""(EOS)"""       ,do_normalize=__a       ,padding_value=1.0       )

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

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







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			Optional[int]					   = self.get_feature_extractor()
          snake_case :			int					   = self.get_tokenizer()

          snake_case :			int					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			Dict					   = floats_list((3, 1_000)       )

          snake_case :			Optional[Any]					   = feature_extractor(audio=__a       ,return_tensors="""np"""       )
          snake_case :			int					   = processor(audio=__a       ,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  snake_case_			(							self       )			->							Union[str, Any]:
          snake_case :			Any					   = self.get_feature_extractor()
          snake_case :			Dict					   = self.get_tokenizer()

          snake_case :			List[str]					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			str					   = floats_list((3, 1_000)       )

          snake_case :			Tuple					   = feature_extractor(audio_target=__a       ,return_tensors="""np"""       )
          snake_case :			int					   = processor(audio_target=__a       ,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  snake_case_			(							self       )			->							Dict:
          snake_case :			Tuple					   = self.get_feature_extractor()
          snake_case :			str					   = self.get_tokenizer()

          snake_case :			Dict					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			Union[str, Any]					   = """This is a test string"""

          snake_case :			Optional[Any]					   = processor(text=__a       )
          snake_case :			Union[str, Any]					   = tokenizer(__a       )

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







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Dict					   = self.get_feature_extractor()
          snake_case :			Any					   = self.get_tokenizer()

          snake_case :			str					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			Tuple					   = """This is a test string"""

          snake_case :			Union[str, Any]					   = processor(text_target=__a       )
          snake_case :			int					   = tokenizer(__a       )

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







     def  snake_case_			(							self       )			->							Optional[int]:
          snake_case :			Dict					   = self.get_feature_extractor()
          snake_case :			Optional[int]					   = self.get_tokenizer()

          snake_case :			Optional[int]					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          snake_case :			List[Any]					   = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          snake_case :			str					   = processor.batch_decode(__a       )
          snake_case :			List[Any]					   = tokenizer.batch_decode(__a       )

          self.assertListEqual(__a       ,__a       )







     def  snake_case_			(							self       )			->							List[str]:
          snake_case :			Dict					   = self.get_feature_extractor()
          snake_case :			Any					   = self.get_tokenizer()

          snake_case :			Optional[Any]					   = SpeechTaProcessor(tokenizer=__a       ,feature_extractor=__a       )

          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

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase			:      Union[str, Any]			=					TypeVar("""T""", bound=bool)







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a       )			->							None:
          snake_case :			T					   = value
          snake_case :			SkewNode[T] | None					   = None
          snake_case :			SkewNode[T] | None					   = None







     @property
     def  snake_case_			(							self       )			->							T:
          return self._value







     @staticmethod
     def  snake_case_			(							__a       ,__a       )			->							SkewNode[T] | None:
          if not roota:
               return roota

          if not roota:
               return roota

          if roota.value > roota.value:
               snake_case   ,		snake_case :			Tuple					   = roota, roota

          snake_case :			Tuple					   = roota
          snake_case :			Union[str, Any]					   = roota.right
          snake_case :			Any					   = roota.left
          snake_case :			Union[str, Any]					   = SkewNode.merge(__a       ,__a       )

          return result







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a = ()       )			->							None:
          snake_case :			SkewNode[T] | None					   = None
          if data:
               for item in data:
                    self.insert(__a       )







     def __bool__(							self       )			->							bool:
          return self._root is not None







     def __iter__(							self       )			->							Iterator[T]:
          snake_case :			list[Any]					   = []
          while self:
               result.append(self.pop()       )

          # Pushing items back to the heap not to clear it.
          for item in result:
               self.insert(__a       )

          return iter(__a       )







     def  snake_case_			(							self       ,__a       )			->							None:
          snake_case :			List[Any]					   = SkewNode.merge(self._root       ,SkewNode(__a       )       )







     def  snake_case_			(							self       )			->							T | None:
          snake_case :			Tuple					   = self.top()
          snake_case :			int					   = (
              SkewNode.merge(self._root.left       ,self._root.right       ) if self._root else None
          )

          return result







     def  snake_case_			(							self       )			->							T:
          if not self._root:
               raise IndexError("""Can't get top element for the empty heap."""       )
          return self._root.value







     def  snake_case_			(							self       )			->							None:
          snake_case :			Any					   = None


if __name__ == "__main__":
      import doctest

      doctest.testmod()



