





'''simple docstring'''

from PIL import Image

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Image:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(259 * (level + 255)) / (255 * (259 - level))

   def contrast(__UpperCAmelCase   ) -> int:
      return int(128 + factor * (c - 128)   )

   return img.point(__UpperCAmelCase   )


if __name__ == "__main__":
    # Load image
    with Image.open("image_data/lena.jpg") as img:
        # Change contrast to 170
        a   	=    change_contrast(img, 170)
        cont_img.save("image_data/lena_high_contrast.png", format="png")












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      ):


	'''simple docstring'''





	_A													=				MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	_A													=				TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[Any]			):
			lowercase__					:      int      =		AudioClassificationPipeline(model=lowerCamelCase_,					feature_extractor=lowerCamelCase_			)

			# test with a raw waveform
			lowercase__					:      Dict      =		np.zeros((34000,)			)
			lowercase__					:      Optional[int]      =		np.zeros((14000,)			)
			return audio_classifier, [audioa, audio]

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						str			):
			lowercase__   ,				lowercase__					:      List[str]      =		examples
			lowercase__					:      int      =		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__					:      List[Any]      =		audio_classifier(lowerCamelCase_,					top_k=1			)
			self.assertEqual(
			    lowerCamelCase_,					[
			        {'score': ANY(lowerCamelCase_			), 'label': ANY(lowerCamelCase_			)},
			    ],					)

			self.run_torchaudio(lowerCamelCase_			)

	@require_torchaudio
	def    snake_case__(						self:						Dict,					lowerCamelCase_:						str			):
			import datasets

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

	@require_torch
	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      List[str]      =		'anton-l/wav2vec2-random-tiny-classifier'

			lowercase__					:      Tuple      =		pipeline('audio-classification',					model=lowerCamelCase_			)

			lowercase__					:      List[str]      =		np.ones((8000,)			)
			lowercase__					:      List[Any]      =		audio_classifier(lowerCamelCase_,					top_k=4			)

			lowercase__					:      List[str]      =		[
			    {'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'},
			]
			lowercase__					:      Optional[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(lowerCamelCase_,					decimals=4			),					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]			)

			lowercase__					:      Tuple      =		{'array': np.ones((8000,)			), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
			lowercase__					:      Any      =		audio_classifier(lowerCamelCase_,					top_k=4			)
			self.assertIn(nested_simplify(lowerCamelCase_,					decimals=4			),					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]			)

	@require_torch
	@slow
	def    snake_case__(						self:						Optional[int]			):
			import datasets

			lowercase__					:      str      =		'superb/wav2vec2-base-superb-ks'

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

			lowercase__					:      Optional[Any]      =		np.array(dataset[3]['speech'],					dtype=np.floataa			)
			lowercase__					:      Optional[int]      =		audio_classifier(lowerCamelCase_,					top_k=4			)
			self.assertEqual(
			    nested_simplify(lowerCamelCase_,					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    snake_case__(						self:						int			):
			pass


