"""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							:      Union[str, Any]		 =		MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     _UpperCamelCase							:      Dict		 =		TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					AudioClassificationPipeline(model=a__    ,					feature_extractor=a__  )

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






     def    __A  (		self    ,					a__    ,					a__  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					examples
       _lowerCAmelCase						:	Dict        =					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						:	Optional[int]        =					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						:	str        =					datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,					"""clean"""    ,					split="""validation"""  )
       _lowerCAmelCase						:	int        =					dataset[0]["""audio"""]["""array"""]
       _lowerCAmelCase						:	List[Any]        =					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						:	Any        =					pipeline("""audio-classification"""    ,					model=a__  )

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

       _lowerCAmelCase						:	Dict        =					[
           {"""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						:	int        =					[
           {"""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						:	Optional[int]        =					{"""array""": np.ones((8000,)  ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       _lowerCAmelCase						:	str        =					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						:	List[str]        =					"""superb/wav2vec2-base-superb-ks"""

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

       _lowerCAmelCase						:	List[str]        =					np.array(dataset[3]["""speech"""]    ,					dtype=np.floataa  )
       _lowerCAmelCase						:	Any        =					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





"""simple docstring"""




import socket





def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Optional[int]        =					socket.socket(socket.AF_INET      ,socket.SOCK_STREAM     )
  _lowerCAmelCase						:	Optional[int]        =					socket.gethostname()
  _lowerCAmelCase						:	Tuple        =					12312

  sock.connect((host, port)     )
  sock.send(b"""Hello server!"""     )

  with open("""Received_file"""      ,"""wb"""     ) as out_file:
    print("""File opened"""     )
    print("""Receiving data..."""     )
    while True:
      _lowerCAmelCase						:	List[Any]        =					sock.recv(1024     )
      if not data:
        break
      out_file.write(_lowerCamelCase     )

  print("""Successfully received the file"""     )
  sock.close()
  print("""Connection closed"""     )


if __name__ == "__main__":
     main()




