






import copy
import re
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   "hp"
		snake_case    =   {}
		snake_case    =   None
		@classmethod
		def       A(  cls		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   int:


						'''simple docstring'''
						A	 =      prefix
						A	 =      defaults
						cls.build_naming_info()
		@staticmethod
		def       A(  _SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						if len(_SCREAMING_SNAKE_CASE		) == 0:
										return ""
						A	 =      None
						if any(char.isdigit() for char in word		):
										raise Exception(f'Parameters should not contain numbers: \'{word}\' contains a number'		)
						if word in info["short_word"]:
										return info["short_word"][word]
						for prefix_len in range(1						,len(_SCREAMING_SNAKE_CASE		) + 1		):
										A	 =      word[:prefix_len]
										if prefix in info["reverse_short_word"]:
														continue
										else:
														A	 =      prefix
														break

						if short_word is None:
										# Paranoid fallback
										def int_to_alphabetic(_SCREAMING_SNAKE_CASE		:       Optional[int]		):
														A	 =      ''
														while integer != 0:
																		A	 =      chr(ord('A'		) + integer % 1_0		) + s
																		integer //= 1_0
														return s

										A	 =      0
										while True:
														A	 =      word + '#' + int_to_alphabetic(_SCREAMING_SNAKE_CASE		)
														if sword in info["reverse_short_word"]:
																		continue
														else:
																		A	 =      sword
																		break

						A	 =      short_word
						A	 =      word
						return short_word
		@staticmethod
		def       A(  _SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   int:


						'''simple docstring'''
						A	 =      param_name.split('_'		)

						A	 =      [TrialShortNamer.shortname_for_word(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) for word in words]

						# We try to create a separatorless short name, but if there is a collision we have to fallback
						# to a separated short name
						A	 =      ['', '_']

						for separator in separators:
										A	 =      separator.join(_SCREAMING_SNAKE_CASE		)
										if shortname not in info["reverse_short_param"]:
														A	 =      shortname
														A	 =      param_name
														return shortname

						return param_name
		@staticmethod
		def       A(  _SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      TrialShortNamer.shortname_for_key(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						A	 =      short_name
						A	 =      param_name
		@classmethod
		def       A(  cls		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						if cls.NAMING_INFO is not None:
										return

						A	 =      {
						    'short_word': {},
						    'reverse_short_word': {},
						    'short_param': {},
						    'reverse_short_param': {},
						}

						A	 =      list(cls.DEFAULTS.keys()		)

						for k in field_keys:
										cls.add_new_param_name(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

						A	 =      info
		@classmethod
		def       A(  cls		:       List[str]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   List[Any]:


						'''simple docstring'''
						cls.build_naming_info()
						assert cls.PREFIX is not None
						A	 =      [copy.copy(cls.PREFIX		)]

						for k, v in params.items():
										if k not in cls.DEFAULTS:
														raise Exception(f'You should provide a default value for the param name {k} with value {v}'		)
										if v == cls.DEFAULTS[k]:
														# The default value is not added to the name
														continue

										A	 =      cls.NAMING_INFO['short_param'][k]

										if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														A	 =      1 if v else 0

										A	 =      '' if isinstance(_SCREAMING_SNAKE_CASE						,(int, float)		) else '-'
										A	 =      f'{key}{sep}{v}'
										name.append(_SCREAMING_SNAKE_CASE		)

						return "_".join(_SCREAMING_SNAKE_CASE		)







		@classmethod
		def       A(  cls		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						A	 =      repr[len(cls.PREFIX		) + 1 :]
						if repr == "":
										A	 =      []
						else:
										A	 =      repr.split('_'		)

						A	 =      {}

						for value in values:
										if "-" in value:
														A ,		A	 =      value.split('-'		)
										else:
														A	 =      re.sub('[0-9.]'						,''						,_SCREAMING_SNAKE_CASE		)
														A	 =      float(re.sub('[^0-9.]'						,''						,_SCREAMING_SNAKE_CASE		)		)

										A	 =      cls.NAMING_INFO['reverse_short_param'][p_k]

										A	 =      p_v

						for k in cls.DEFAULTS:
										if k not in parameters:
														A	 =      cls.DEFAULTS[k]

						return parameters













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"""



		snake_case    =   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		snake_case    =   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      AudioClassificationPipeline(model=_SCREAMING_SNAKE_CASE						,feature_extractor=_SCREAMING_SNAKE_CASE		)

						# test with a raw waveform
						A	 =      np.zeros((3_4_0_0_0,)		)
						A	 =      np.zeros((1_4_0_0_0,)		)
						return audio_classifier, [audioa, audio]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Union[str, Any]:


						'''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       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''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       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      'anton-l/wav2vec2-random-tiny-classifier'

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

						A	 =      np.ones((8_0_0_0,)		)
						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((8_0_0_0,)		), '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       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''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       A(  self		:       Any		)    ->   int:


						'''simple docstring'''
						pass





