






"""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"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	SCREAMING_SNAKE_CASE_:							int   			=					TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       AudioClassificationPipeline(model=_SCREAMING_SNAKE_CASE  ,					feature_extractor=_SCREAMING_SNAKE_CASE						)

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__ ,			A__							 =       examples
					A__							 =       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						)},
					    ]  ,					)
					A__							 =       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       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					import datasets

					# test with a local file
					A__							 =       datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy'  ,					'clean'  ,					split='validation'						)
					A__							 =       dataset[0]['audio']['array']
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'anton-l/wav2vec2-random-tiny-classifier'

					A__							 =       pipeline('audio-classification'  ,					model=_SCREAMING_SNAKE_CASE						)

					A__							 =       np.ones((8000,)						)
					A__							 =       audio_classifier(_SCREAMING_SNAKE_CASE  ,					top_k=4						)

					A__							 =       [
					    {'score': 0.0842, 'label': 'no'},
					    {'score': 0.0838, 'label': 'up'},
					    {'score': 0.0837, 'label': 'go'},
					    {'score': 0.0834, 'label': 'right'},
					]
					A__							 =       [
					    {'score': 0.0845, 'label': 'stop'},
					    {'score': 0.0844, 'label': 'on'},
					    {'score': 0.0841, 'label': 'right'},
					    {'score': 0.0834, 'label': 'left'},
					]
					self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE  ,					decimals=4						)  ,					[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

					A__							 =       {'array': np.ones((8000,)						), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					import datasets

					A__							 =       'superb/wav2vec2-base-superb-ks'

					A__							 =       pipeline('audio-classification'  ,					model=_SCREAMING_SNAKE_CASE						)
					A__							 =       datasets.load_dataset('anton-l/superb_dummy'  ,					'ks'  ,					split='test'						)

					A__							 =       np.array(dataset[3]['speech']  ,					dtype=np.floataa						)
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					pass










"""simple docstring"""






class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       name
					A__							 =       val


	def __str__(      self						):


					"""simple docstring"""

					return f'''{self.__class__.__name__}({self.name}, {self.val})'''






	def __lt__(      self  ,					__a						):


					"""simple docstring"""

					return self.val < other.val





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       {}
					A__							 =       {}
					A__							 =       self.build_heap(__a						)


	def __getitem__(      self  ,					__a						):


					"""simple docstring"""

					return self.get_value(__a						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return (idx - 1) // 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 1


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.heap_dict[key]


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       len(__a						) - 1
					A__							 =       self.get_parent_idx(__a						)

					for idx, i in enumerate(__a						):
									A__							 =       idx
									A__							 =       i.val

					for i in range(__a  ,					-1  ,					-1						):
									self.sift_down(__a  ,					__a						)
					return array


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					while True:
									A__							 =       self.get_left_child_idx(__a						)  # noqa: E741
									A__							 =       self.get_right_child_idx(__a						)

									A__							 =       idx
									if l < len(__a						) and array[l] < array[idx]:
													A__							 =       l
									if r < len(__a						) and array[r] < array[smallest]:
													A__							 =       r

									if smallest != idx:
													A__ ,			A__							 =       array[smallest], array[idx]
													(
													    (
													    A__
													) ,			(
													    A__
													) ,			
													)							 =       (
													    self.idx_of_element[array[smallest]],
													    self.idx_of_element[array[idx]],
													)
													A__							 =       smallest
									else:
													break


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       self.get_parent_idx(__a						)
					while p >= 0 and self.heap[p] > self.heap[idx]:
									A__ ,			A__							 =       self.heap[idx], self.heap[p]
									A__ ,			A__							 =       (
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									A__							 =       p
									A__							 =       self.get_parent_idx(__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.heap[0]


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__ ,			A__							 =       self.heap[-1], self.heap[0]
					A__ ,			A__							 =       (
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

					A__							 =       self.heap.pop()
					del self.idx_of_element[x]
					self.sift_down(0  ,					self.heap						)
					return x


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					self.heap.append(__a						)
					A__							 =       len(self.heap						) - 1
					A__							 =       node.val
					self.sift_up(len(self.heap						) - 1						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.heap						) == 0






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					A__							 =       new_value
					A__							 =       new_value
					self.sift_up(self.idx_of_element[node]						)





SCREAMING_SNAKE_CASE						:						List[str]					   =       Node('''R''', -1)
SCREAMING_SNAKE_CASE						:						Tuple					   =       Node('''B''', 6)
SCREAMING_SNAKE_CASE						:						List[Any]					   =       Node('''A''', 3)
SCREAMING_SNAKE_CASE						:						int					   =       Node('''X''', 1)
SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE						:						str					   =       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()


