


'''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


lowerCamelCase							:       Optional[Any]  						=     get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_torch
class   __lowerCAmelCase						(unittest.TestCase					):





      '''simple docstring'''






      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			tempfile.mkdtemp()

             lowercase__				      =			SpeechTaTokenizer(UpperCamelCase			)
             tokenizer.save_pretrained(self.tmpdirname			)

             lowercase__				      =			{
                 '''feature_size''': 1,
                 '''padding_value''': 0.0,
                 '''sampling_rate''': 16000,
                 '''do_normalize''': False,
                 '''num_mel_bins''': 80,
                 '''hop_length''': 16,
                 '''win_length''': 64,
                 '''win_function''': '''hann_window''',
                 '''fmin''': 80,
                 '''fmax''': 7600,
                 '''mel_floor''': 1E-10,
                 '''reduction_factor''': 2,
                 '''return_attention_mask''': True,
             }

             lowercase__				      =			os.path.join(self.tmpdirname       ,						UpperCamelCase			)
             with open(self.feature_extraction_file       ,						'''w'''       ,						encoding='''utf-8'''			) as fp:
                    fp.write(json.dumps(UpperCamelCase			) + '''\n'''			)



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						**UpperCamelCase   :   int			):

             '''simple docstring'''







             return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,						**UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Tuple       ,						**UpperCamelCase   :   Union[str, Any]			):

             '''simple docstring'''







             return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,						**UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







             shutil.rmtree(self.tmpdirname			)



      def 				UpperCamelCase__		(self   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			self.get_tokenizer()
             lowercase__				      =			self.get_feature_extractor()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

             processor.save_pretrained(self.tmpdirname			)
             lowercase__				      =			SpeechTaProcessor.from_pretrained(self.tmpdirname			)

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

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



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             lowercase__				      =			SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,						feature_extractor=self.get_feature_extractor()			)
             processor.save_pretrained(self.tmpdirname			)

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

             lowercase__				      =			SpeechTaProcessor.from_pretrained(
                 self.tmpdirname       ,						bos_token='''(BOS)'''       ,						eos_token='''(EOS)'''       ,						do_normalize=UpperCamelCase       ,						padding_value=1.0			)

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

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



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             lowercase__				      =			self.get_feature_extractor()
             lowercase__				      =			self.get_tokenizer()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

             lowercase__				      =			floats_list((3, 1000)			)

             lowercase__				      =			feature_extractor(audio=UpperCamelCase       ,						return_tensors='''np'''			)
             lowercase__				      =			processor(audio=UpperCamelCase       ,						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   :   int			):

             '''simple docstring'''







             lowercase__				      =			self.get_feature_extractor()
             lowercase__				      =			self.get_tokenizer()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

             lowercase__				      =			floats_list((3, 1000)			)

             lowercase__				      =			feature_extractor(audio_target=UpperCamelCase       ,						return_tensors='''np'''			)
             lowercase__				      =			processor(audio_target=UpperCamelCase       ,						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   :   Tuple			):

             '''simple docstring'''







             lowercase__				      =			self.get_feature_extractor()
             lowercase__				      =			self.get_tokenizer()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

             lowercase__				      =			'''This is a test string'''

             lowercase__				      =			processor(text=UpperCamelCase			)
             lowercase__				      =			tokenizer(UpperCamelCase			)

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



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             lowercase__				      =			self.get_feature_extractor()
             lowercase__				      =			self.get_tokenizer()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

             lowercase__				      =			'''This is a test string'''

             lowercase__				      =			processor(text_target=UpperCamelCase			)
             lowercase__				      =			tokenizer(UpperCamelCase			)

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



      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             lowercase__				      =			self.get_feature_extractor()
             lowercase__				      =			self.get_tokenizer()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

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

             lowercase__				      =			processor.batch_decode(UpperCamelCase			)
             lowercase__				      =			tokenizer.batch_decode(UpperCamelCase			)

             self.assertListEqual(UpperCamelCase       ,						UpperCamelCase			)






      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             lowercase__				      =			self.get_feature_extractor()
             lowercase__				      =			self.get_tokenizer()

             lowercase__				      =			SpeechTaProcessor(tokenizer=UpperCamelCase       ,						feature_extractor=UpperCamelCase			)

             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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase							:       Tuple  						=     {'configuration_bert_generation': ['BertGenerationConfig']}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Union[str, Any]  						=     ['BertGenerationTokenizer']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:       Dict  						=     [
     'BertGenerationDecoder',
     'BertGenerationEncoder',
     'BertGenerationPreTrainedModel',
     'load_tf_weights_in_bert_generation',
 ]


if TYPE_CHECKING:
 from .configuration_bert_generation import BertGenerationConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_bert_generation import BertGenerationTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bert_generation import (
      BertGenerationDecoder,
      BertGenerationEncoder,
      BertGenerationPreTrainedModel,
      load_tf_weights_in_bert_generation,
  )

else:
 import sys

 lowerCamelCase							:       int  						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




