text: # Copyright 2021 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import unittest
  
  import numpy as np
  
  from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  from transformers.pipelines import AudioClassificationPipeline, pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      nested_simplify,
      require_datasets,
      require_tf,
      require_torch,
      slow,
  )
  
  from .test_pipelines_common import ANY, PipelineTestCaseMeta
  
  
  @is_pipeline_test
  @require_torch
  class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
      model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  
      @require_datasets
      @slow
      def run_pipeline_test(self, model, tokenizer, feature_extractor):
          import datasets
  
          audio_classifier = AudioClassificationPipeline(model=model, feature_extractor=feature_extractor)
  
          # test with a raw waveform
          audio = np.zeros((34000,))
          output = audio_classifier(audio)
          # by default a model is initialized with num_labels=2
          self.assertEqual(
              output,
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
          )
          output = audio_classifier(audio, top_k=1)
          self.assertEqual(
              output,
              [
                  {"score": ANY(float), "label": ANY(str)},
              ],
          )
  
          # test with a local file
          dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
          filename = dataset[0]["file"]
          output = audio_classifier(filename)
          self.assertEqual(
              output,
              [
                  {"score": ANY(float), "label": ANY(str)},
                  {"score": ANY(float), "label": ANY(str)},
              ],
          )
  
      @require_torch
      def test_small_model_pt(self):
          model = "anton-l/wav2vec2-random-tiny-classifier"
  
          audio_classifier = pipeline("audio-classification", model=model)
  
          audio = np.ones((8000,))
          output = audio_classifier(audio, top_k=4)
          self.assertEqual(
              nested_simplify(output, decimals=4),
              [
                  {"score": 0.0843, "label": "on"},
                  {"score": 0.0840, "label": "left"},
                  {"score": 0.0837, "label": "off"},
                  {"score": 0.0835, "label": "yes"},
              ],
          )
  
      @require_torch
      @require_datasets
      @slow
      def test_large_model_pt(self):
          import datasets
  
          model = "superb/wav2vec2-base-superb-ks"
  
          audio_classifier = pipeline("audio-classification", model=model)
          dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test")
  
          audio = np.array(dataset[3]["speech"], dtype=np.float32)
          output = audio_classifier(audio, top_k=4)
          self.assertEqual(
              nested_simplify(output, decimals=4),
              [
                  {"score": 0.9809, "label": "go"},
                  {"score": 0.0073, "label": "up"},
                  {"score": 0.0064, "label": "_unknown_"},
                  {"score": 0.0015, "label": "down"},
              ],
          )
  
      @require_tf
      @unittest.skip("Audio classification is not implemented for TF")
      def test_small_model_tf(self):
          pass
  