


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       lowerCamelCase__   (       unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     SCREAMING_SNAKE_CASE__      			=			TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =AudioClassificationPipeline(model=lowerCamelCase__		,     feature_extractor=lowerCamelCase__       )

      # test with a raw waveform
      _lowercase							      =np.zeros((3_4_0_0_0,)       )
      _lowercase							      =np.zeros((1_4_0_0_0,)       )
      return audio_classifier, [audioa, audio]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   str:
      _lowercase							      =examples
      _lowercase							      =audio_classifier(lowerCamelCase__       )
      # by default a model is initialized with num_labels=2
      self.assertEqual(
          lowerCamelCase__		,     [
              {'''score''': ANY(lowerCamelCase__       ), '''label''': ANY(lowerCamelCase__       )},
              {'''score''': ANY(lowerCamelCase__       ), '''label''': ANY(lowerCamelCase__       )},
          ]		,     )
      _lowercase							      =audio_classifier(lowerCamelCase__		,     top_k=1       )
      self.assertEqual(
          lowerCamelCase__		,     [
              {'''score''': ANY(lowerCamelCase__       ), '''label''': ANY(lowerCamelCase__       )},
          ]		,     )

      self.run_torchaudio(lowerCamelCase__       )





     @require_torchaudio
     def      __A					(self		,     UpperCAmelCase       )     ->   Any:
      import datasets

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





     @require_torch
     def      __A					(self       )     ->   List[Any]:
      _lowercase							      ="""anton-l/wav2vec2-random-tiny-classifier"""

      _lowercase							      =pipeline('''audio-classification'''		,     model=lowerCamelCase__       )

      _lowercase							      =np.ones((8_0_0_0,)       )
      _lowercase							      =audio_classifier(lowerCamelCase__		,     top_k=4       )

      _lowercase							      =[
          {"""score""": 0.0842, """label""": """no"""},
          {"""score""": 0.0838, """label""": """up"""},
          {"""score""": 0.0837, """label""": """go"""},
          {"""score""": 0.0834, """label""": """right"""},
      ]
      _lowercase							      =[
          {"""score""": 0.0845, """label""": """stop"""},
          {"""score""": 0.0844, """label""": """on"""},
          {"""score""": 0.0841, """label""": """right"""},
          {"""score""": 0.0834, """label""": """left"""},
      ]
      self.assertIn(nested_simplify(lowerCamelCase__		,     decimals=4       )		,     [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]       )

      _lowercase							      ={"""array""": np.ones((8_0_0_0,)       ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
      _lowercase							      =audio_classifier(lowerCamelCase__		,     top_k=4       )
      self.assertIn(nested_simplify(lowerCamelCase__		,     decimals=4       )		,     [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]       )





     @require_torch
     @slow
     def      __A					(self       )     ->   Union[str, Any]:
      import datasets

      _lowercase							      ="""superb/wav2vec2-base-superb-ks"""

      _lowercase							      =pipeline('''audio-classification'''		,     model=lowerCamelCase__       )
      _lowercase							      =datasets.load_dataset('''anton-l/superb_dummy'''		,     '''ks'''		,     split='''test'''       )

      _lowercase							      =np.array(dataset[3]['''speech''']		,     dtype=np.floataa       )
      _lowercase							      =audio_classifier(lowerCamelCase__		,     top_k=4       )
      self.assertEqual(
          nested_simplify(lowerCamelCase__		,     decimals=3       )		,     [
              {'''score''': 0.981, '''label''': '''go'''},
              {'''score''': 0.007, '''label''': '''up'''},
              {'''score''': 0.006, '''label''': '''_unknown_'''},
              {'''score''': 0.001, '''label''': '''down'''},
          ]		,     )





     @require_tf
     @unittest.skip('''Audio classification is not implemented for TF'''       )
     def      __A					(self       )     ->   Dict:
      pass






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_:List[Any]             =						TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict             =						TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__ ,						slots=SCREAMING_SNAKE_CASE__    )
class SCREAMING_SNAKE_CASE__     (		Generic[KEY, VAL]    ):




    '''simple docstring'''






    __lowerCamelCase			: KEY
    __lowerCamelCase			: VAL






class SCREAMING_SNAKE_CASE__     (		_Item    ):




    '''simple docstring'''






    def __init__(       self		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__		)






    def __bool__(       self		):
        return False


SCREAMING_SNAKE_CASE_:Optional[Any]             =						_DeletedItem()






class SCREAMING_SNAKE_CASE__     (		MutableMapping[KEY, VAL]    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 8,		lowerCamelCase__ = 0.75		):
        A :       List[str]          =   initial_block_size
        A :       list[_Item | None]          =   [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        A :       Dict          =   capacity_factor
        A :       Optional[Any]          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return hash(lowerCamelCase__		) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return (ind + 1) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       List[Any]          =   self._buckets[ind]
        if not stored:
            A :       Optional[int]          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            self._len += 1
            return True
        elif stored.key == key:
            A :       int          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            return True
        else:
            return False






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if len(self._buckets		) <= self._initial_block_size:
            return False
        A :       Optional[Any]          =   len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Dict          =   self._buckets
        A :       Optional[Any]          =   [None] * new_size
        A :       List[str]          =   0
        for item in old_buckets:
            if item:
                self._add_item(item.key,		item.val		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) * 2		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) // 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Union[str, Any]          =   self._get_bucket_index(lowerCamelCase__		)
        for _ in range(len(self._buckets		)		):
            yield ind
            A :       Dict          =   self._get_next_ind(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            if self._try_set(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                break






    def __setitem__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self._is_full():
            self._size_up()

        self._add_item(lowerCamelCase__,		lowerCamelCase__		)






    def __delitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       Tuple          =   self._buckets[ind]
            if item is None:
                raise KeyError(lowerCamelCase__		)
            if item is _deleted:
                continue
            if item.key == key:
                A :       int          =   _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()






    def __getitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       int          =   self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(lowerCamelCase__		)






    def __len__(       self		):
        return self._len






    def __iter__(       self		):
        yield from (item.key for item in self._buckets if item)






    def __repr__(       self		):
        A :       Union[str, Any]          =   """ ,""".join(
            f'''{item.key}: {item.val}''' for item in self._buckets if item		)
        return f'''HashMap({val_string})'''


