




import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

UpperCamelCase__						=		logging.get_logger(__name__)






def    snake_case   (				A		,  A     )				->				np.array:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				f"{sampling_rate}"
	lowerCamelCase_				:						List[str]				  =				'1'
	lowerCamelCase_				:						Optional[int]				  =				'f32le'
	lowerCamelCase_				:						Dict				  =				[
	    'ffmpeg',
	    '-i',
	    'pipe:0',
	    '-ac',
	    ac,
	    '-ar',
	    ar,
	    '-f',
	    format_for_conversion,
	    '-hide_banner',
	    '-loglevel',
	    'quiet',
	    'pipe:1',
	]

	try:
		lowerCamelCase_				:						Dict				  =				subprocess.Popen(SCREAMING_SNAKE_CASE__		,  stdin=subprocess.PIPE		,  stdout=subprocess.PIPE     )
	except FileNotFoundError:
		raise ValueError('ffmpeg was not found but is required to load audio files from filename'     )
	lowerCamelCase_				:						Union[str, Any]				  =				ffmpeg_process.communicate(SCREAMING_SNAKE_CASE__     )
	lowerCamelCase_				:						List[Any]				  =				output_stream[0]

	lowerCamelCase_				:						str				  =				np.frombuffer(SCREAMING_SNAKE_CASE__		,  np.floataa     )
	if audio.shape[0] == 0:
		raise ValueError('Malformed soundfile'     )
	return audio



@add_end_docstrings(_UpperCAmelCase    )
class 							__SCREAMING_SNAKE_CASE				(						_UpperCAmelCase    ):

					def __init__(							self					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				5
						super().__init__(*lowercase_					,			**lowercase_   )

						if self.framework != "pt":
							raise ValueError(f"The {self.__class__} is only available in PyTorch."   )

						self.check_model_type(lowercase_   )

					def __call__(							self					,			__lowercase					,			**__lowercase					,			):







						'''simple docstring'''




						return super().__call__(lowercase_					,			**lowercase_   )

					def       lowercase_				(							self					,			__lowercase=None					,			**__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				{}
						if top_k is not None:
							if top_k > self.model.config.num_labels:
								lowerCamelCase_				:						List[str]				  =				self.model.config.num_labels
							lowerCamelCase_				:						int				  =				top_k
						return {}, {}, postprocess_params

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						if isinstance(lowercase_					,			lowercase_   ):
							if inputs.startswith('http://'   ) or inputs.startswith('https://'   ):
								# We need to actually check for a real protocol, otherwise it's impossible to use a local file
								# like http_huggingface_co.png
								lowerCamelCase_				:						Dict				  =				requests.get(lowercase_   ).content
							else:
								with open(lowercase_					,			'rb'   ) as f:
									lowerCamelCase_				:						Any				  =				f.read()

						if isinstance(lowercase_					,			lowercase_   ):
							lowerCamelCase_				:						Dict				  =				ffmpeg_read(lowercase_					,			self.feature_extractor.sampling_rate   )

						if isinstance(lowercase_					,			lowercase_   ):
							# Accepting `"array"` which is the key defined in `datasets` for
							# better integration
							if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
								raise ValueError(
								    'When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '
								    '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
								    'containing the sampling_rate associated with that array'   )

							lowerCamelCase_				:						Optional[int]				  =				inputs.pop('raw'					,			lowercase_   )
							if _inputs is None:
								# Remove path which will not be used from `datasets`.
								inputs.pop('path'					,			lowercase_   )
								lowerCamelCase_				:						str				  =				inputs.pop('array'					,			lowercase_   )
							lowerCamelCase_				:						str				  =				inputs.pop('sampling_rate'   )
							lowerCamelCase_				:						Union[str, Any]				  =				_inputs
							if in_sampling_rate != self.feature_extractor.sampling_rate:
								import torch

								if is_torchaudio_available():
									from torchaudio import functional as F
								else:
									raise ImportError(
									    'torchaudio is required to resample audio samples in AudioClassificationPipeline. '
									    'The torchaudio package can be installed through: `pip install torchaudio`.'   )

								lowerCamelCase_				:						Optional[Any]				  =				F.resample(
								    torch.from_numpy(lowercase_   )					,			lowercase_					,			self.feature_extractor.sampling_rate   ).numpy()

						if not isinstance(lowercase_					,			np.ndarray   ):
							raise ValueError('We expect a numpy ndarray as input'   )
						if len(inputs.shape   ) != 1:
							raise ValueError('We expect a single channel audio input for AudioClassificationPipeline'   )

						lowerCamelCase_				:						List[str]				  =				self.feature_extractor(
						    lowercase_					,			sampling_rate=self.feature_extractor.sampling_rate					,			return_tensors='pt'   )
						return processed

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				self.model(**lowercase_   )
						return model_outputs





					def       lowercase_				(							self					,			__lowercase					,			__lowercase=5   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				model_outputs.logits[0].softmax(-1   )
						lowerCamelCase_  ,     lowerCamelCase_				:						Optional[int]				  =				probs.topk(lowercase_   )

						lowerCamelCase_				:						Union[str, Any]				  =				scores.tolist()
						lowerCamelCase_				:						str				  =				ids.tolist()

						lowerCamelCase_				:						int				  =				[{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(lowercase_					,			lowercase_   )]

						return labels



import unittest

from cer import CER


lowercase_									=    CER()


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  ['White House']
        A__        =  ['white house']
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.1_818_181_818						) < 1E-6						)


    def 		snake_case__	( self					:			str						)->       Dict:





        '''simple docstring'''







        A__        =  ['were wolf']
        A__        =  ['werewolf']
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.1_111_111						) < 1E-6						)

        A__        =  ['werewolf']
        A__        =  ['weae     wolf']
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.25						) < 1E-6						)

        # consecutive whitespaces case 1
        A__        =  ['were wolf']
        A__        =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.0						) < 1E-6						)

        # consecutive whitespaces case 2
        A__        =  ['were   wolf']
        A__        =  ['were               wolf']
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.0						) < 1E-6						)


    def 		snake_case__	( self					:			int						)->       str:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['weaewolf']
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			Optional[int]						)->       Optional[int]:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['wereawolf']
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			List[Any]						)->       Optional[Any]:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  ['wereolf']
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.125						) < 1E-6						)


    def 		snake_case__	( self					:			str						)->       Tuple:





        '''simple docstring'''







        A__        =  ['werewolf']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertEqual(lowercase_,0.0						)


    def 		snake_case__	( self					:			Optional[int]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  ['werewolf', 'I am your father']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertEqual(lowercase_,0.0						)

        A__        =  ['werewolf', 'I am your father', 'doge']
        A__        =  ['werxwolf', 'I       am your father', 'doge']
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.03_571_428						) < 1E-6						)


    def 		snake_case__	( self					:			List[str]						)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  ['My hovercraft', 'is full of eels']
        A__        =  ['My hovercraft is full', ' of eels']
        # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
        # whitespace at the front of " of eels" will be strip during preporcessing
        # so need to insert 2 whitespaces
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_,concatenate_texts=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.071_428						) < 1E-6						)


    def 		snake_case__	( self					:			Dict						)->       List[str]:





        '''simple docstring'''







        A__        =  ['我能吞下玻璃而不伤身体']
        A__        =  [' 能吞虾玻璃而 不霜身体啦']
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.4_545_454_545						) < 1E-6						)

        A__        =  ['我能吞下玻璃', '而不伤身体']
        A__        =  ['我    能 吞 下 玻 璃', '而不伤身体']
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertTrue(abs(char_error_rate - 0.454_545_454_545						) < 1E-6						)

        A__        =  ['我能吞下玻璃而不伤身体']
        A__        =  cer.compute(predictions=lowercase_,references=lowercase_						)
        self.assertFalse(lowercase_,0.0						)


    def 		snake_case__	( self					:			Any						)->       List[str]:





        '''simple docstring'''







        A__        =  ['']
        A__        =  ['Hypothesis']
        with self.assertRaises(lowercase_						):
            cer.compute(predictions=lowercase_,references=lowercase_						)


if __name__ == "__main__":
     unittest.main()

