
'''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  lowerCAmelCase__   (  unittest.TestCase     ):
					lowerCAmelCase_        =     MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
					lowerCAmelCase_        =     TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       AudioClassificationPipeline(model=__SCREAMING_SNAKE_CASE				,							feature_extractor=__SCREAMING_SNAKE_CASE		)

											# test with a raw waveform
											lowercase_	:     Tuple													=       np.zeros((3_40_00,)		)
											lowercase_	:     str													=       np.zeros((1_40_00,)		)
											return audio_classifier, [audioa, audio]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_						, lowercase_	:     List[str]													=       examples
											lowercase_	:     Tuple													=       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		)},
											    ]				,							)
											lowercase_	:     int													=       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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											import datasets

											# test with a local file
											lowercase_	:     Optional[int]													=       datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,							'''clean'''				,							split='''validation'''		)
											lowercase_	:     Any													=       dataset[0]['''audio''']['''array''']
											lowercase_	:     Tuple													=       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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       '''anton-l/wav2vec2-random-tiny-classifier'''

											lowercase_	:     str													=       pipeline('''audio-classification'''				,							model=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       np.ones((80_00,)		)
											lowercase_	:     List[Any]													=       audio_classifier(__SCREAMING_SNAKE_CASE				,							top_k=4		)

											lowercase_	:     Dict													=       [
											    {'''score''': 0.0_842, '''label''': '''no'''},
											    {'''score''': 0.0_838, '''label''': '''up'''},
											    {'''score''': 0.0_837, '''label''': '''go'''},
											    {'''score''': 0.0_834, '''label''': '''right'''},
											]
											lowercase_	:     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]		)

											lowercase_	:     List[str]													=       {'''array''': np.ones((80_00,)		), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
											lowercase_	:     Dict													=       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 	_snake_case    (   self		):


											"""simple docstring"""





											import datasets

											lowercase_	:     Any													=       '''superb/wav2vec2-base-superb-ks'''

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

											lowercase_	:     int													=       np.array(dataset[3]['''speech''']				,							dtype=np.floataa		)
											lowercase_	:     Optional[Any]													=       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 	_snake_case    (   self		):


											"""simple docstring"""





											pass










import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(UnCLIPScheduler,)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for variance in ["fixed_small_log", "learned_range"]:
					self.check_over_configs(variance_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample in [True, False]:
					self.check_over_configs(clip_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(prediction_type=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
					for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

						self.check_over_forward(time_step=__lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''fixed_small_log'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000E-10							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.054_9625							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.999_4987							)							) < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''learned_range'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		0.5

				assert scheduler._get_variance(1       ,  predicted_variance=__lowerCAmelCase							) - -10.171_2790 < 1E-5
				assert scheduler._get_variance(4_8_7       ,  predicted_variance=__lowerCAmelCase							) - -5.799_8052 < 1E-5
				assert scheduler._get_variance(9_9_9       ,  predicted_variance=__lowerCAmelCase							) - -0.001_0011 < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 252.268_2495							) < 1E-2
				assert abs(result_mean.item() - 0.328_4743							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(2_5							)

				lowerCamelCase__								=		scheduler.timesteps

				lowerCamelCase__								=		self.dummy_model()
				lowerCamelCase__								=		self.dummy_sample_deter
				lowerCamelCase__								=		torch.manual_seed(0							)

				for i, t in enumerate(__lowerCAmelCase							):
					# 1. predict noise residual
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  __lowerCAmelCase							)

					if i + 1 == timesteps.shape[0]:
						lowerCamelCase__								=		None
					else:
						lowerCamelCase__								=		timesteps[i + 1]

					# 2. predict previous mean of sample x_t-1
					lowerCamelCase__								=		scheduler.step(
					    __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  prev_timestep=__lowerCAmelCase       ,  generator=__lowerCAmelCase							).prev_sample

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 258.204_4983							) < 1E-2
				assert abs(result_mean.item() - 0.336_2038							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass



