


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"""







				lowerCAmelCase 		= MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
				lowerCAmelCase 		= TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				List[Any]:
					A								=				AudioClassificationPipeline(model=a__				,   feature_extractor=a__   )

					# 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__   )	->				Union[str, Any]:
					A					,			A								=				examples
					A								=				audio_classifier(a__   )
					# by default a model is initialized with num_labels=2
					self.assertEqual(
					    a__				,   [
					        {"""score""": ANY(a__   ), """label""": ANY(a__   )},
					        {"""score""": ANY(a__   ), """label""": ANY(a__   )},
					    ]				,   )
					A								=				audio_classifier(a__				,   top_k=1   )
					self.assertEqual(
					    a__				,   [
					        {"""score""": ANY(a__   ), """label""": ANY(a__   )},
					    ]				,   )

					self.run_torchaudio(a__   )

				@require_torchaudio
				def      _UpperCAmelCase					(						self				,   a__   )	->				Any:
					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(a__   )
					self.assertEqual(
					    a__				,   [
					        {"""score""": ANY(a__   ), """label""": ANY(a__   )},
					        {"""score""": ANY(a__   ), """label""": ANY(a__   )},
					    ]				,   )

				@require_torch
				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				"""anton-l/wav2vec2-random-tiny-classifier"""

					A								=				pipeline("""audio-classification"""				,   model=a__   )

					A								=				np.ones((8000,)   )
					A								=				audio_classifier(a__				,   top_k=4   )

					A								=				[
					    {"""score""": 0.08_42, """label""": """no"""},
					    {"""score""": 0.08_38, """label""": """up"""},
					    {"""score""": 0.08_37, """label""": """go"""},
					    {"""score""": 0.08_34, """label""": """right"""},
					]
					A								=				[
					    {"""score""": 0.08_45, """label""": """stop"""},
					    {"""score""": 0.08_44, """label""": """on"""},
					    {"""score""": 0.08_41, """label""": """right"""},
					    {"""score""": 0.08_34, """label""": """left"""},
					]
					self.assertIn(nested_simplify(a__				,   decimals=4   )				,   [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )

					A								=				{"""array""": np.ones((8000,)   ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
					A								=				audio_classifier(a__				,   top_k=4   )
					self.assertIn(nested_simplify(a__				,   decimals=4   )				,   [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )

				@require_torch
				@slow
				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					import datasets

					A								=				"""superb/wav2vec2-base-superb-ks"""

					A								=				pipeline("""audio-classification"""				,   model=a__   )
					A								=				datasets.load_dataset("""anton-l/superb_dummy"""				,   """ks"""				,   split="""test"""   )

					A								=				np.array(dataset[3]["""speech"""]				,   dtype=np.floataa   )
					A								=				audio_classifier(a__				,   top_k=4   )
					self.assertEqual(
					    nested_simplify(a__				,   decimals=3   )				,   [
					        {"""score""": 0.9_81, """label""": """go"""},
					        {"""score""": 0.0_07, """label""": """up"""},
					        {"""score""": 0.0_06, """label""": """_unknown_"""},
					        {"""score""": 0.0_01, """label""": """down"""},
					    ]				,   )

				@require_tf
				@unittest.skip("""Audio classification is not implemented for TF"""   )
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					pass







from __future__ import annotations

from typing import Any






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__ = 0   )	->				None:
					A					,			A								=				row, column
					A								=				[[default_value for c in range(a__   )] for r in range(a__   )]

				def __str__(						self   )	->				str:
					A								=				f'Matrix consist of {self.row} rows and {self.column} columns\n'

					# Make string identifier
					A								=				0
					for row_vector in self.array:
						for obj in row_vector:
							A								=				max(a__				,   len(str(a__   )   )   )
					A								=				f'%{max_element_length}s'

					# Make string and return
					def single_line(a__   ) -> str:
						nonlocal string_format_identifier
						A								=				"""["""
						line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
						line += "]"
						return line

					s += "\n".join(single_line(a__   ) for row_vector in self.array   )
					return s

				def __repr__(						self   )	->				str:
					return str(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				bool:
					if not (isinstance(a__				,   (list, tuple)   ) and len(a__   ) == 2):
						return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
						return False
					else:
						return True

				def __getitem__(						self				,   a__   )	->				Any:
					assert self.validate_indicies(a__   )
					return self.array[loc[0]][loc[1]]

				def __setitem__(						self				,   a__				,   a__   )	->				None:
					assert self.validate_indicies(a__   )
					A								=				value

				def __add__(						self				,   a__   )	->				Matrix:
					assert isinstance(a__				,   a__   )
					assert self.row == another.row and self.column == another.column

					# Add
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c] + another[r, c]
					return result

				def __neg__(						self   )	->				Matrix:
					A								=				Matrix(self.row				,   self.column   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				-self[r, c]
					return result

				def __sub__(						self				,   a__   )	->				Matrix:
					return self + (-another)

				def __mul__(						self				,   a__   )	->				Matrix:

					if isinstance(a__				,   (int, float)   ):  # Scalar multiplication
						A								=				Matrix(self.row				,   self.column   )
						for r in range(self.row   ):
							for c in range(self.column   ):
								A								=				self[r, c] * another
						return result
					elif isinstance(a__				,   a__   ):  # Matrix multiplication
						assert self.column == another.row
						A								=				Matrix(self.row				,   another.column   )
						for r in range(self.row   ):
							for c in range(another.column   ):
								for i in range(self.column   ):
									result[r, c] += self[r, i] * another[i, c]
						return result
					else:
						A								=				f'Unsupported type given for another ({type(a__   )})'
						raise TypeError(a__   )

				def      _UpperCAmelCase					(						self   )	->				Matrix:
					A								=				Matrix(self.column				,   self.row   )
					for r in range(self.row   ):
						for c in range(self.column   ):
							A								=				self[r, c]
					return result

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Any:
					assert isinstance(a__				,   a__   ) and isinstance(a__				,   a__   )
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					A								=				v.transpose()
					A								=				(v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
						return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					A								=				Matrix(3							,					3							,					0						)
					for i in range(3						):
						A								=				1
					print(f'a^(-1) is {ainv}'						)
					# u, v
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				1, 2, -3
					A								=				Matrix(3							,					1							,					0						)
					A					,			A					,			A								=				4, -2, 5
					print(f'u is {u}'						)
					print(f'v is {v}'						)
					print(f'uv^T is {u * v.transpose()}'						)
					# Sherman Morrison
					print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(UpperCamelCase__							,					UpperCamelCase__						)}'						)





				def   _lowerCAmelCase				(   ) ->   None:





					"""simple docstring"""







					import doctest

					doctest.testmod()
				testa()



