
'''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 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		lowerCamelCase     							=						TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Optional[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       int     			=						AudioClassificationPipeline(model=UpperCamelCase__  ,	feature_extractor=UpperCamelCase__      )

				# test with a raw waveform
				snake_case  :       int     			=						np.zeros((3_4000,)      )
				snake_case  :       Dict     			=						np.zeros((1_4000,)      )
				return audio_classifier, [audioa, audio]



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Dict      )						->			Tuple:






				"""simple docstring"""







				snake_case			,snake_case  :       Optional[int]     			=						examples
				snake_case  :       Optional[Any]     			=						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__      )},
				    ]  ,	)
				snake_case  :       Optional[Any]     			=						audio_classifier(UpperCamelCase__  ,	top_k=1      )
				self.assertEqual(
				    UpperCamelCase__  ,	[
				        {'''score''': ANY(UpperCamelCase__      ), '''label''': ANY(UpperCamelCase__      )},
				    ]  ,	)

				self.run_torchaudio(UpperCamelCase__      )



		@require_torchaudio
		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			Union[str, Any]      )						->			List[str]:






				"""simple docstring"""







				import datasets

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



		@require_torch
		def       lowerCAmelCase				(      self				:			Tuple      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Any     			=						'''anton-l/wav2vec2-random-tiny-classifier'''

				snake_case  :       int     			=						pipeline('''audio-classification'''  ,	model=UpperCamelCase__      )

				snake_case  :       Any     			=						np.ones((8000,)      )
				snake_case  :       str     			=						audio_classifier(UpperCamelCase__  ,	top_k=4      )

				snake_case  :       Tuple     			=						[
				    {'''score''': 0.0_842, '''label''': '''no'''},
				    {'''score''': 0.0_838, '''label''': '''up'''},
				    {'''score''': 0.0_837, '''label''': '''go'''},
				    {'''score''': 0.0_834, '''label''': '''right'''},
				]
				snake_case  :       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(UpperCamelCase__  ,	decimals=4      )  ,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]      )

				snake_case  :       int     			=						{'''array''': np.ones((8000,)      ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
				snake_case  :       Any     			=						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				:			Any      )						->			str:






				"""simple docstring"""







				import datasets

				snake_case  :       Union[str, Any]     			=						'''superb/wav2vec2-base-superb-ks'''

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

				snake_case  :       str     			=						np.array(dataset[3]['''speech''']  ,	dtype=np.floataa      )
				snake_case  :       int     			=						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				:			List[Any]      )						->			Optional[int]:






				"""simple docstring"""







				pass










from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")




class 					UpperCamelCase_ (     Generic[T]							):



	'''simple docstring'''




	lowerCAmelCase						=					42  # Cache store of keys
	lowerCAmelCase						=					42  # References of the keys in cache
	lowerCAmelCase						=					1_0  # Maximum capacity of cache




	def __init__(			self			,     a   )			->       None:
								snake_case_           =				deque()
								snake_case_           =				set()
								if not n:
															snake_case_           =				sys.maxsize
								elif n < 0:
															raise ValueError('n should be an integer greater than 0.'   )
								else:
															snake_case_           =				n




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if x not in self.key_reference:
															if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
																						snake_case_           =				self.dq_store.pop()
																						self.key_reference.remove(a   )
								else:
															self.dq_store.remove(a   )

								self.dq_store.appendleft(a   )
								self.key_reference.add(a   )




	def 				_UpperCamelCase     (			self   )			->       None:
								for k in self.dq_store:
															print(a   )






	def __repr__(			self   )			->       str:
								return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store   )}'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowercase    				=   LRUCache(4)
	lru_cache.refer("A")
	lru_cache.refer(2)
	lru_cache.refer(3)
	lru_cache.refer("A")
	lru_cache.refer(4)
	lru_cache.refer(5)
	lru_cache.display()

	print(lru_cache)
	assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





