'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


a=	get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	tempfile.mkdtemp()

         __UpperCamelCase						:						List[str]		     =	['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
         __UpperCamelCase						:						Optional[int]		     =	dict(zip(_A  ,      range(len(_A     )     )     )     )
         __UpperCamelCase						:						List[str]		     =	Path(self.tmpdirname     )
         save_json(_A  ,      save_dir / VOCAB_FILES_NAMES['vocab_file']     )
         if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
             copyfile(_A  ,      save_dir / VOCAB_FILES_NAMES['spm_file']     )

         __UpperCamelCase						:						Optional[int]		     =	SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
         tokenizer.save_pretrained(self.tmpdirname     )

         __UpperCamelCase						:						Optional[Any]		     =	{
             'feature_size': 2_4,
             'num_mel_bins': 2_4,
             'padding_value': 0.0,
             'sampling_rate': 1_6_0_0_0,
             'return_attention_mask': False,
             'do_normalize': True,
         }
         save_json(_A  ,      save_dir / FEATURE_EXTRACTOR_NAME     )







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         return SpeechaTextTokenizer.from_pretrained(self.tmpdirname  ,      **_A     )







     def lowerCAmelCase  (		self  ,      **_lowerCamelCase     ):
         return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname  ,      **_A     )







     def lowerCAmelCase  (		self     ):
         shutil.rmtree(self.tmpdirname     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Tuple		     =	self.get_tokenizer()
         __UpperCamelCase						:						str		     =	self.get_feature_extractor()

         __UpperCamelCase						:						Optional[Any]		     =	SpeechaTextProcessor(tokenizer=_A  ,      feature_extractor=_A     )

         processor.save_pretrained(self.tmpdirname     )
         __UpperCamelCase						:						List[Any]		     =	SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer.get_vocab()     )
         self.assertIsInstance(processor.tokenizer  ,      _A     )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor.to_json_string()     )
         self.assertIsInstance(processor.feature_extractor  ,      _A     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	SpeechaTextProcessor(
             tokenizer=self.get_tokenizer()  ,      feature_extractor=self.get_feature_extractor()     )
         processor.save_pretrained(self.tmpdirname     )

         __UpperCamelCase						:						Any		     =	self.get_tokenizer(bos_token='(BOS)'  ,      eos_token='(EOS)'     )
         __UpperCamelCase						:						List[Any]		     =	self.get_feature_extractor(do_normalize=_A  ,      padding_value=1.0     )

         __UpperCamelCase						:						int		     =	SpeechaTextProcessor.from_pretrained(
             self.tmpdirname  ,      bos_token='(BOS)'  ,      eos_token='(EOS)'  ,      do_normalize=_A  ,      padding_value=1.0     )

         self.assertEqual(processor.tokenizer.get_vocab()  ,      tokenizer_add_kwargs.get_vocab()     )
         self.assertIsInstance(processor.tokenizer  ,      _A     )

         self.assertEqual(processor.feature_extractor.to_json_string()  ,      feature_extractor_add_kwargs.to_json_string()     )
         self.assertIsInstance(processor.feature_extractor  ,      _A     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						str		     =	self.get_feature_extractor()
         __UpperCamelCase						:						int		     =	self.get_tokenizer()

         __UpperCamelCase						:						Any		     =	SpeechaTextProcessor(tokenizer=_A  ,      feature_extractor=_A     )

         __UpperCamelCase						:						Optional[int]		     =	floats_list((3, 1_0_0_0)     )

         __UpperCamelCase						:						int		     =	feature_extractor(_A  ,      return_tensors='np'     )
         __UpperCamelCase						:						List[Any]		     =	processor(_A  ,      return_tensors='np'     )

         for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()  ,      input_processor[key].sum()  ,      delta=1E-2     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	self.get_feature_extractor()
         __UpperCamelCase						:						List[str]		     =	self.get_tokenizer()

         __UpperCamelCase						:						List[Any]		     =	SpeechaTextProcessor(tokenizer=_A  ,      feature_extractor=_A     )

         __UpperCamelCase						:						int		     =	'This is a test string'

         __UpperCamelCase						:						List[Any]		     =	processor(text=_A     )

         __UpperCamelCase						:						int		     =	tokenizer(_A     )

         for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]  ,      encoded_processor[key]     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Tuple		     =	self.get_tokenizer()

         __UpperCamelCase						:						List[str]		     =	SpeechaTextProcessor(tokenizer=_A  ,      feature_extractor=_A     )

         __UpperCamelCase						:						Dict		     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __UpperCamelCase						:						List[str]		     =	processor.batch_decode(_A     )
         __UpperCamelCase						:						List[str]		     =	tokenizer.batch_decode(_A     )

         self.assertListEqual(_A  ,      _A     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	self.get_feature_extractor()
         __UpperCamelCase						:						Tuple		     =	self.get_tokenizer()

         __UpperCamelCase						:						int		     =	SpeechaTextProcessor(tokenizer=_A  ,      feature_extractor=_A     )

         self.assertListEqual(
             processor.model_input_names  ,      feature_extractor.model_input_names  ,      msg='`processor` and `feature_extractor` model input names do not match'  ,      )






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

lowerCamelCase   					=  logging.get_logger(__name__)


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				F"""{sampling_rate}"""
					__lowerCAmelCase       =				'1'
					__lowerCAmelCase       =				'f32le'
					__lowerCAmelCase       =				[
					    'ffmpeg',
					    '-i',
					    'pipe:0',
					    '-ac',
					    ac,
					    '-ar',
					    ar,
					    '-f',
					    format_for_conversion,
					    '-hide_banner',
					    '-loglevel',
					    'quiet',
					    'pipe:1',
					]

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

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





@add_end_docstrings(_a				)
class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    *_A       ,    **_A				):
												# Default, might be overriden by the model.config.
												__lowerCAmelCase       =				5
												super().__init__(*_A       ,    **_A				)

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

												self.check_model_type(_A				)




							def __call__(			self       ,    _A       ,    **_A       ,    ):
												return super().__call__(_A       ,    **_A				)




							def 							A__	(			self       ,    _A=None       ,    **_A				):
												# No parameters on this pipeline right now
												__lowerCAmelCase       =				{}
												if top_k is not None:
																	if top_k > self.model.config.num_labels:
																						__lowerCAmelCase       =				self.model.config.num_labels
																	__lowerCAmelCase       =				top_k
												return {}, {}, postprocess_params




							def 							A__	(			self       ,    _A				):
												if isinstance(_A       ,    _A				):
																	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       =				requests.get(_A				).content
																	else:
																						with open(_A       ,    'rb'				) as f:
																											__lowerCAmelCase       =				f.read()

												if isinstance(_A       ,    _A				):
																	__lowerCAmelCase       =				ffmpeg_read(_A       ,    self.feature_extractor.sampling_rate				)

												if isinstance(_A       ,    _A				):
																	# 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       =				inputs.pop('raw'       ,    _A				)
																	if _inputs is None:
																						# Remove path which will not be used from `datasets`.
																						inputs.pop('path'       ,    _A				)
																						__lowerCAmelCase       =				inputs.pop('array'       ,    _A				)
																	__lowerCAmelCase       =				inputs.pop('sampling_rate'				)
																	__lowerCAmelCase       =				_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       =				F.resample(
																						    torch.from_numpy(_A				)       ,    _A       ,    self.feature_extractor.sampling_rate				).numpy()

												if not isinstance(_A       ,    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       =				self.feature_extractor(
												    _A       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='pt'				)
												return processed




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				self.model(**_A				)
												return model_outputs




							def 							A__	(			self       ,    _A       ,    _A=5				):
												__lowerCAmelCase       =				model_outputs.logits[0].softmax(-1				)
												__lowerCAmelCase,						__lowerCAmelCase       =				probs.topk(_A				)

												__lowerCAmelCase       =				scores.tolist()
												__lowerCAmelCase       =				ids.tolist()

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

												return labels






