






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


__snake_case							 =  get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")







@require_torch
class       UpperCAmelCase_						( unittest.TestCase			):



      """simple docstring"""
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          UpperCamelCase	:List[str]     							=       tempfile.mkdtemp()

          UpperCamelCase	:List[Any]     							=       SpeechTaTokenizer(SCREAMING_SNAKE_CASE_			)
          tokenizer.save_pretrained(self.tmpdirname			)

          UpperCamelCase	:Optional[Any]     							=       {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 1_6000,
              '''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,
          }

          UpperCamelCase	:Union[str, Any]     							=       os.path.join(self.tmpdirname   ,    SCREAMING_SNAKE_CASE_			)
          with open(self.feature_extraction_file   ,    '''w'''   ,    encoding='''utf-8'''			) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Union[str, Any]:
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Union[str, Any]:
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				str:
          shutil.rmtree(self.tmpdirname			)
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          UpperCamelCase	:Tuple     							=       self.get_tokenizer()
          UpperCamelCase	:Optional[int]     							=       self.get_feature_extractor()

          UpperCamelCase	:int     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_SNAKE_CASE_			)

          processor.save_pretrained(self.tmpdirname			)
          UpperCamelCase	:Dict     							=       SpeechTaProcessor.from_pretrained(self.tmpdirname			)

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

          self.assertEqual(processor.feature_extractor.to_json_string()   ,    feature_extractor.to_json_string()			)
          self.assertIsInstance(processor.feature_extractor   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:List[Any]     							=       SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,    feature_extractor=self.get_feature_extractor()			)
          processor.save_pretrained(self.tmpdirname			)

          UpperCamelCase	:Optional[int]     							=       self.get_tokenizer(bos_token='''(BOS)'''   ,    eos_token='''(EOS)'''			)
          UpperCamelCase	:str     							=       self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_   ,    padding_value=1.0			)

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

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

          self.assertEqual(processor.feature_extractor.to_json_string()   ,    feature_extractor_add_kwargs.to_json_string()			)
          self.assertIsInstance(processor.feature_extractor   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:List[Any]     							=       self.get_feature_extractor()
          UpperCamelCase	:Dict     							=       self.get_tokenizer()

          UpperCamelCase	:int     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:str     							=       floats_list((3, 1000)			)

          UpperCamelCase	:Optional[int]     							=       feature_extractor(audio=SCREAMING_SNAKE_CASE_   ,    return_tensors='''np'''			)
          UpperCamelCase	:Dict     							=       processor(audio=SCREAMING_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			)    ->				Dict:
          UpperCamelCase	:Any     							=       self.get_feature_extractor()
          UpperCamelCase	:Any     							=       self.get_tokenizer()

          UpperCamelCase	:Any     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Any     							=       floats_list((3, 1000)			)

          UpperCamelCase	:Any     							=       feature_extractor(audio_target=SCREAMING_SNAKE_CASE_   ,    return_tensors='''np'''			)
          UpperCamelCase	:Tuple     							=       processor(audio_target=SCREAMING_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			)    ->				int:
          UpperCamelCase	:Union[str, Any]     							=       self.get_feature_extractor()
          UpperCamelCase	:int     							=       self.get_tokenizer()

          UpperCamelCase	:Tuple     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Tuple     							=       '''This is a test string'''

          UpperCamelCase	:List[Any]     							=       processor(text=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       tokenizer(SCREAMING_SNAKE_CASE_			)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]   ,    encoded_processor[key]			)
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:Tuple     							=       self.get_feature_extractor()
          UpperCamelCase	:Any     							=       self.get_tokenizer()

          UpperCamelCase	:int     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Any     							=       '''This is a test string'''

          UpperCamelCase	:Optional[Any]     							=       processor(text_target=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer(SCREAMING_SNAKE_CASE_			)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]   ,    encoded_processor[key]			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:int     							=       self.get_feature_extractor()
          UpperCamelCase	:Optional[int]     							=       self.get_tokenizer()

          UpperCamelCase	:List[str]     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:int     							=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase	:Any     							=       processor.batch_decode(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

          self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)




      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Dict     							=       self.get_feature_extractor()
          UpperCamelCase	:int     							=       self.get_tokenizer()

          UpperCamelCase	:Dict     							=       SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_   ,    feature_extractor=SCREAMING_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 transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





