




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


a_      						=   get_tests_dir("fixtures/test_sentencepiece.model")

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

      """simple docstring"""

      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  tempfile.mkdtemp()

          __UpperCamelCase     						=  ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
          __UpperCamelCase     						=  dict(zip(snake_case       ,				range(len(snake_case ) ) ) )
          __UpperCamelCase     						=  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     						=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
          tokenizer.save_pretrained(self.tmpdirname )

          __UpperCamelCase     						=  {
              '''feature_size''': 24,
              '''num_mel_bins''': 24,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16000,
              '''return_attention_mask''': False,
              '''do_normalize''': True,
          }
          save_json(snake_case       ,				save_dir / FEATURE_EXTRACTOR_NAME )







      def 					snake_case		(   self    :       List[Any]       ,				**snake_case    :       Any ):
          return SpeechaTextTokenizer.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       Any       ,				**snake_case    :       Tuple ):
          return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       Optional[int] ):
          shutil.rmtree(self.tmpdirname )







      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  self.get_tokenizer()
          __UpperCamelCase     						=  self.get_feature_extractor()

          __UpperCamelCase     						=  SpeechaTextProcessor(tokenizer=snake_case       ,				feature_extractor=snake_case )

          processor.save_pretrained(self.tmpdirname )
          __UpperCamelCase     						=  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 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  SpeechaTextProcessor(
              tokenizer=self.get_tokenizer()       ,				feature_extractor=self.get_feature_extractor() )
          processor.save_pretrained(self.tmpdirname )

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

          __UpperCamelCase     						=  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 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  self.get_feature_extractor()
          __UpperCamelCase     						=  self.get_tokenizer()

          __UpperCamelCase     						=  SpeechaTextProcessor(tokenizer=snake_case       ,				feature_extractor=snake_case )

          __UpperCamelCase     						=  floats_list((3, 1000) )

          __UpperCamelCase     						=  feature_extractor(snake_case       ,				return_tensors='''np''' )
          __UpperCamelCase     						=  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 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  self.get_feature_extractor()
          __UpperCamelCase     						=  self.get_tokenizer()

          __UpperCamelCase     						=  SpeechaTextProcessor(tokenizer=snake_case       ,				feature_extractor=snake_case )

          __UpperCamelCase     						=  '''This is a test string'''

          __UpperCamelCase     						=  processor(text=snake_case )

          __UpperCamelCase     						=  tokenizer(snake_case )

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







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.get_feature_extractor()
          __UpperCamelCase     						=  self.get_tokenizer()

          __UpperCamelCase     						=  SpeechaTextProcessor(tokenizer=snake_case       ,				feature_extractor=snake_case )

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

          __UpperCamelCase     						=  processor.batch_decode(snake_case )
          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case )

          self.assertListEqual(snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  self.get_feature_extractor()
          __UpperCamelCase     						=  self.get_tokenizer()

          __UpperCamelCase     						=  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'''       ,				)







"""simple docstring"""


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


a							=TypeVar('T', bound='TaskTemplate')






@dataclass(frozen=__lowerCAmelCase	)
class    __UpperCAmelCase						(	abc.ABC	):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				A__      :   str
				A__      :   ClassVar[Features]
				A__      :   ClassVar[Features]

				def 		_a	(      self	, _lowerCamelCase      ):
										return copy.deepcopy(self      )

				@property
				def 		_a	(      self      ):
										return Features(**self.input_schema	, **self.label_schema      )

				@property
				@abc.abstractmethod
				def 		_a	(      self      ):
										raise NotImplementedError






				@classmethod
				def 		_a	(      cls	, _lowerCamelCase      ):
										lowerCamelCase__					={f.name for f in dataclasses.fields(cls      )}
										return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )


