




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):
      UpperCAmelCase__      :	str    		=   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      UpperCAmelCase__      :	int    		=   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]   ):
             __lowerCamelCase   =						AudioClassificationPipeline(model=UpperCamelCase_    ,       feature_extractor=UpperCamelCase_   )

             # test with a raw waveform
             __lowerCamelCase   =						np.zeros((3_40_00,)   )
             __lowerCamelCase   =						np.zeros((1_40_00,)   )
             return audio_classifier, [audioa, audio]
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase,   __lowerCamelCase   =						examples
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_   )
             # by default a model is initialized with num_labels=2
             self.assertEqual(
                 UpperCamelCase_    ,       [
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                 ]    ,       )
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=1   )
             self.assertEqual(
                 UpperCamelCase_    ,       [
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                 ]    ,       )

             self.run_torchaudio(UpperCamelCase_   )
      @require_torchaudio
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: Union[str, Any]   ):
             import datasets

             # test with a local file
             __lowerCamelCase   =						datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,       """clean"""    ,       split="""validation"""   )
             __lowerCamelCase   =						dataset[0]["""audio"""]["""array"""]
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_   )
             self.assertEqual(
                 UpperCamelCase_    ,       [
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                     {"""score""": ANY(UpperCamelCase_   ), """label""": ANY(UpperCamelCase_   )},
                 ]    ,       )
      @require_torch
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						"""anton-l/wav2vec2-random-tiny-classifier"""

             __lowerCamelCase   =						pipeline("""audio-classification"""    ,       model=UpperCamelCase_   )

             __lowerCamelCase   =						np.ones((80_00,)   )
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=4   )

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

             __lowerCamelCase   =						{"""array""": np.ones((80_00,)   ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=4   )
             self.assertIn(nested_simplify(UpperCamelCase_    ,       decimals=4   )    ,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )
      @require_torch
      @slow
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             import datasets

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

             __lowerCamelCase   =						pipeline("""audio-classification"""    ,       model=UpperCamelCase_   )
             __lowerCamelCase   =						datasets.load_dataset("""anton-l/superb_dummy"""    ,       """ks"""    ,       split="""test"""   )

             __lowerCamelCase   =						np.array(dataset[3]["""speech"""]    ,       dtype=np.floataa   )
             __lowerCamelCase   =						audio_classifier(UpperCamelCase_    ,       top_k=4   )
             self.assertEqual(
                 nested_simplify(UpperCamelCase_    ,       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 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase_									=	get_tests_dir('fixtures')
UpperCAmelCase_									=	get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCAmelCase_									=	get_tests_dir('fixtures/dummy-config.json')





class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						0
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""   )
             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )
             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int   ):
             with tempfile.TemporaryDirectory() as tmpdirname:
                    __lowerCamelCase   =						WavaVecaConfig()

                    # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   ).to_dict()

                    config_dict.pop("""feature_extractor_type"""   )
                    __lowerCamelCase   =						WavaVecaFeatureExtractor(**UpperCamelCase_   )

                    # save in new folder
                    model_config.save_pretrained(UpperCamelCase_   )
                    config.save_pretrained(UpperCamelCase_   )

                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )

                    # make sure private variable is not incorrectly saved
                    __lowerCamelCase   =						json.loads(config.to_json_string()   )
                    self.assertTrue("""_processor_class""" not in dict_as_saved   )

             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )
             self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int   ):
             with self.assertRaisesRegex(
                 UpperCamelCase_    ,       """bert-base is not a local folder and is not a valid model identifier"""   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained("""bert-base"""   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             with self.assertRaisesRegex(
                 UpperCamelCase_    ,       r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_    ,       revision="""aaaaaa"""   )
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             with self.assertRaisesRegex(
                 UpperCamelCase_    ,       """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""    ,       ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""   )
      def 			lowerCAmelCase__						(				self: Tuple   ):
             # If remote code is not set, we will time out when asking whether to load the model.
             with self.assertRaises(UpperCamelCase_   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                        """hf-internal-testing/test_dynamic_feature_extractor"""   )
             # If remote code is disabled, we can't load this config.
             with self.assertRaises(UpperCamelCase_   ):
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                        """hf-internal-testing/test_dynamic_feature_extractor"""    ,       trust_remote_code=UpperCamelCase_   )

             __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                 """hf-internal-testing/test_dynamic_feature_extractor"""    ,       trust_remote_code=UpperCamelCase_   )
             self.assertEqual(feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )

             # Test feature extractor can be reloaded.
             with tempfile.TemporaryDirectory() as tmp_dir:
                    feature_extractor.save_pretrained(UpperCamelCase_   )
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_    ,       trust_remote_code=UpperCamelCase_   )
             self.assertEqual(reloaded_feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )
      def 			lowerCAmelCase__						(				self: Any   ):
             try:
                    AutoConfig.register("""custom"""    ,       UpperCamelCase_   )
                    AutoFeatureExtractor.register(UpperCamelCase_    ,       UpperCamelCase_   )
                    # Trying to register something existing in the Transformers library will raise an error
                    with self.assertRaises(UpperCamelCase_   ):
                           AutoFeatureExtractor.register(UpperCamelCase_    ,       UpperCamelCase_   )

                    # Now that the config is registered, it can be used as any other config with the auto-API
                    __lowerCamelCase   =						CustomFeatureExtractor.from_pretrained(UpperCamelCase_   )
                    with tempfile.TemporaryDirectory() as tmp_dir:
                           feature_extractor.save_pretrained(UpperCamelCase_   )
                           __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(UpperCamelCase_   )
                           self.assertIsInstance(UpperCamelCase_    ,       UpperCamelCase_   )

             finally:
                    if "custom" in CONFIG_MAPPING._extra_content:
                           del CONFIG_MAPPING._extra_content["custom"]
                    if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                           del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




      def 			lowerCAmelCase__						(				self: Dict   ):





             class     lowerCamelCase__(					__lowerCamelCase):
                   UpperCAmelCase__      :	str    		=   True

             try:
                    AutoConfig.register("""custom"""    ,       UpperCamelCase_   )
                    AutoFeatureExtractor.register(UpperCamelCase_    ,       UpperCamelCase_   )
                    # If remote code is not set, the default is to use local
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                        """hf-internal-testing/test_dynamic_feature_extractor"""   )
                    self.assertEqual(feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )
                    self.assertTrue(feature_extractor.is_local   )

                    # If remote code is disabled, we load the local one.
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                        """hf-internal-testing/test_dynamic_feature_extractor"""    ,       trust_remote_code=UpperCamelCase_   )
                    self.assertEqual(feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )
                    self.assertTrue(feature_extractor.is_local   )

                    # If remote is enabled, we load from the Hub
                    __lowerCamelCase   =						AutoFeatureExtractor.from_pretrained(
                        """hf-internal-testing/test_dynamic_feature_extractor"""    ,       trust_remote_code=UpperCamelCase_   )
                    self.assertEqual(feature_extractor.__class__.__name__    ,       """NewFeatureExtractor"""   )
                    self.assertTrue(not hasattr(UpperCamelCase_    ,       """is_local"""   )   )

             finally:
                    if "custom" in CONFIG_MAPPING._extra_content:
                           del CONFIG_MAPPING._extra_content["custom"]
                    if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                           del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]

