"""simple docstring"""




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    Dict	      =	"EncodecFeatureExtractor"
     _UpperCamelCase							:    Optional[int]	      =	("T5Tokenizer", "T5TokenizerFast")






     def __init__(	self       ,  a__       ,  a__						):
       super().__init__(a__       ,  a__						)
       _lowerCAmelCase		: Optional[Any]						    =  self.feature_extractor
       _lowerCAmelCase		: Dict						    =  False






     def 					__A						(	self       ,  a__=None       ,  a__=None       ,  a__=True						):
       return self.tokenizer.get_decoder_prompt_ids(task=a__       ,  language=a__       ,  no_timestamps=a__						)






     def __call__(	self       ,  *a__       ,  **a__						):
       # For backward compatibility
       if self._in_target_context_manager:
         return self.current_processor(*a__       ,  **a__						)

       _lowerCAmelCase		: Any						    =  kwargs.pop("""audio"""       ,  a__						)
       _lowerCAmelCase		: Dict						    =  kwargs.pop("""sampling_rate"""       ,  a__						)
       _lowerCAmelCase		: List[Any]						    =  kwargs.pop("""text"""       ,  a__						)
       if len(a__						) > 0:
         _lowerCAmelCase		: List[str]						    =  args[0]
         _lowerCAmelCase		: str						    =  args[1:]

       if audio is None and text is None:
         raise ValueError("""You need to specify either an `audio` or `text` input to process."""						)

       if text is not None:
         _lowerCAmelCase		: Union[str, Any]						    =  self.tokenizer(a__       ,  **a__						)

       if audio is not None:
         _lowerCAmelCase		: List[str]						    =  self.feature_extractor(a__       ,  *a__       ,  sampling_rate=a__       ,  **a__						)

       if audio is None:
         return inputs

       elif text is None:
         return audio_inputs

       else:
         _lowerCAmelCase		: Optional[int]						    =  audio_inputs["""input_values"""]
         if "padding_mask" in audio_inputs:
           _lowerCAmelCase		: Union[str, Any]						    =  audio_inputs["""padding_mask"""]
         return inputs






     def 					__A						(	self       ,  *a__       ,  **a__						):
       _lowerCAmelCase		: Optional[int]						    =  kwargs.pop("""audio"""       ,  a__						)
       _lowerCAmelCase		: Dict						    =  kwargs.pop("""padding_mask"""       ,  a__						)

       if len(a__						) > 0:
         _lowerCAmelCase		: Optional[int]						    =  args[0]
         _lowerCAmelCase		: str						    =  args[1:]

       if audio_values is not None:
         return self._decode_audio(a__       ,  padding_mask=a__						)
       else:
         return self.tokenizer.batch_decode(*a__       ,  **a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       return self.tokenizer.decode(*a__       ,  **a__						)






     def 					__A						(	self       ,  a__       ,  a__ = None						):
       _lowerCAmelCase		: Optional[Any]						    =  to_numpy(a__						)
       _lowerCAmelCase		: List[str]						    =  audio_values.shape

       if padding_mask is None:
         return list(a__						)

       _lowerCAmelCase		: Any						    =  to_numpy(a__						)

       # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
       # token (so that the generated audio values are **not** treated as padded tokens)
       _lowerCAmelCase		: List[str]						    =  seq_len - padding_mask.shape[-1]
       _lowerCAmelCase		: Optional[int]						    =  1 - self.feature_extractor.padding_value
       _lowerCAmelCase		: List[str]						    =  np.pad(a__       ,  ((0, 0), (0, difference))       ,  """constant"""       ,  constant_values=a__						)

       _lowerCAmelCase		: List[str]						    =  audio_values.tolist()
       for i in range(a__						):
         _lowerCAmelCase		: Union[str, Any]						    =  np.asarray(audio_values[i]						)[
             padding_mask[i][None, :] != self.feature_extractor.padding_value
         ]
         _lowerCAmelCase		: Optional[int]						    =  sliced_audio.reshape(a__       ,  -1						)

       return audio_values





"""simple docstring"""




import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class 		__A							(				unittest.TestCase	):
     _UpperCamelCase							:    str	      =	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     _UpperCamelCase							:    Any	      =	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  AudioClassificationPipeline(model=a__       ,  feature_extractor=a__						)

       # test with a raw waveform
       _lowerCAmelCase		: Optional[int]						    =  np.zeros((34000,)						)
       _lowerCAmelCase		: Optional[Any]						    =  np.zeros((14000,)						)
       return audio_classifier, [audioa, audio]






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  examples
       _lowerCAmelCase		: List[Any]						    =  audio_classifier(a__						)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )
       _lowerCAmelCase		: Tuple						    =  audio_classifier(a__       ,  top_k=1						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )

       self.run_torchaudio(a__						)






     @require_torchaudio
     def 					__A						(	self       ,  a__						):
       import datasets

       # test with a local file
       _lowerCAmelCase		: int						    =  datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""       ,  """clean"""       ,  split="""validation"""						)
       _lowerCAmelCase		: List[Any]						    =  dataset[0]["""audio"""]["""array"""]
       _lowerCAmelCase		: str						    =  audio_classifier(a__						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )






     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  """anton-l/wav2vec2-random-tiny-classifier"""

       _lowerCAmelCase		: Optional[Any]						    =  pipeline("""audio-classification"""       ,  model=a__						)

       _lowerCAmelCase		: Any						    =  np.ones((8000,)						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)

       _lowerCAmelCase		: List[str]						    =  [
           {"""score""": 0.0_8_4_2, """label""": """no"""},
           {"""score""": 0.0_8_3_8, """label""": """up"""},
           {"""score""": 0.0_8_3_7, """label""": """go"""},
           {"""score""": 0.0_8_3_4, """label""": """right"""},
       ]
       _lowerCAmelCase		: str						    =  [
           {"""score""": 0.0_8_4_5, """label""": """stop"""},
           {"""score""": 0.0_8_4_4, """label""": """on"""},
           {"""score""": 0.0_8_4_1, """label""": """right"""},
           {"""score""": 0.0_8_3_4, """label""": """left"""},
       ]
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

       _lowerCAmelCase		: int						    =  {"""array""": np.ones((8000,)						), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       _lowerCAmelCase		: int						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)






     @require_torch
     @slow
     def 					__A						(	self						):
       import datasets

       _lowerCAmelCase		: Optional[Any]						    =  """superb/wav2vec2-base-superb-ks"""

       _lowerCAmelCase		: List[str]						    =  pipeline("""audio-classification"""       ,  model=a__						)
       _lowerCAmelCase		: str						    =  datasets.load_dataset("""anton-l/superb_dummy"""       ,  """ks"""       ,  split="""test"""						)

       _lowerCAmelCase		: Optional[Any]						    =  np.array(dataset[3]["""speech"""]       ,  dtype=np.floataa						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertEqual(
           nested_simplify(a__       ,  decimals=3						)       ,  [
               {"""score""": 0.9_8_1, """label""": """go"""},
               {"""score""": 0.0_0_7, """label""": """up"""},
               {"""score""": 0.0_0_6, """label""": """_unknown_"""},
               {"""score""": 0.0_0_1, """label""": """down"""},
           ]       ,  )






     @require_tf
     @unittest.skip("""Audio classification is not implemented for TF"""						)
     def 					__A						(	self						):
       pass




