


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    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     int							=					MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
       __UpperCamelCase      :     Any							=					TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		AudioClassificationPipeline(model=__SCREAMING_SNAKE_CASE ,					feature_extractor=__SCREAMING_SNAKE_CASE						)

              # test with a raw waveform
              UpperCamelCase    :	Any   	=		np.zeros((34_000,)						)
              UpperCamelCase    :	List[str]   	=		np.zeros((14_000,)						)
              return audio_classifier, [audioa, audio]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Optional[Any]   	=		examples
              UpperCamelCase    :	Union[str, Any]   	=		audio_classifier(__SCREAMING_SNAKE_CASE						)
              # by default a model is initialized with num_labels=2
              self.assertEqual(
                  __SCREAMING_SNAKE_CASE ,					[
                      {'''score''': ANY(__SCREAMING_SNAKE_CASE						), '''label''': ANY(__SCREAMING_SNAKE_CASE						)},
                      {'''score''': ANY(__SCREAMING_SNAKE_CASE						), '''label''': ANY(__SCREAMING_SNAKE_CASE						)},
                  ] ,					)
              UpperCamelCase    :	Tuple   	=		audio_classifier(__SCREAMING_SNAKE_CASE ,					top_k=1						)
              self.assertEqual(
                  __SCREAMING_SNAKE_CASE ,					[
                      {'''score''': ANY(__SCREAMING_SNAKE_CASE						), '''label''': ANY(__SCREAMING_SNAKE_CASE						)},
                  ] ,					)

              self.run_torchaudio(__SCREAMING_SNAKE_CASE						)






       @require_torchaudio
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              import datasets

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






       @require_torch
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		'''anton-l/wav2vec2-random-tiny-classifier'''

              UpperCamelCase    :	Tuple   	=		pipeline('''audio-classification''' ,					model=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Tuple   	=		np.ones((8_000,)						)
              UpperCamelCase    :	Optional[int]   	=		audio_classifier(__SCREAMING_SNAKE_CASE ,					top_k=4						)

              UpperCamelCase    :	int   	=		[
                  {'''score''': 0.0_842, '''label''': '''no'''},
                  {'''score''': 0.0_838, '''label''': '''up'''},
                  {'''score''': 0.0_837, '''label''': '''go'''},
                  {'''score''': 0.0_834, '''label''': '''right'''},
              ]
              UpperCamelCase    :	Optional[int]   	=		[
                  {'''score''': 0.0_845, '''label''': '''stop'''},
                  {'''score''': 0.0_844, '''label''': '''on'''},
                  {'''score''': 0.0_841, '''label''': '''right'''},
                  {'''score''': 0.0_834, '''label''': '''left'''},
              ]
              self.assertIn(nested_simplify(__SCREAMING_SNAKE_CASE ,					decimals=4						) ,					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

              UpperCamelCase    :	List[str]   	=		{'''array''': np.ones((8_000,)						), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
              UpperCamelCase    :	List[str]   	=		audio_classifier(__SCREAMING_SNAKE_CASE ,					top_k=4						)
              self.assertIn(nested_simplify(__SCREAMING_SNAKE_CASE ,					decimals=4						) ,					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)






       @require_torch
       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              import datasets

              UpperCamelCase    :	Dict   	=		'''superb/wav2vec2-base-superb-ks'''

              UpperCamelCase    :	List[str]   	=		pipeline('''audio-classification''' ,					model=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		datasets.load_dataset('''anton-l/superb_dummy''' ,					'''ks''' ,					split='''test'''						)

              UpperCamelCase    :	List[str]   	=		np.array(dataset[3]['''speech'''] ,					dtype=np.floataa						)
              UpperCamelCase    :	str   	=		audio_classifier(__SCREAMING_SNAKE_CASE ,					top_k=4						)
              self.assertEqual(
                  nested_simplify(__SCREAMING_SNAKE_CASE ,					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  _lowercase   (     self						):




              """simple docstring"""




              pass





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Tuple 			=				cva.imread(
      str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
  )
  # turn image in gray scale value
  __UpperCAmelCase : int 			=				cva.cvtColor(image, cva.COLOR_BGR2GRAY)
  # get image shape
  __UpperCAmelCase			,    __UpperCAmelCase : Tuple 			=				gray_img.shape

  # set different points to rotate image
  __UpperCAmelCase : Optional[int] 			=				np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
  __UpperCAmelCase : Optional[int] 			=				np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
  __UpperCAmelCase : Any 			=				np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
  __UpperCAmelCase : int 			=				np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      gray_img,
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
  ]

  # plot different image rotations
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
  for i, image in enumerate(images):
    plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
    plt.title(titles[i])
    plt.axis("off")
    plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
  plt.show()

