
from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)
class      A__       (						_lowerCAmelCase    ):
							lowerCAmelCase__		:						str         =							["input_values", "attention_mask"]





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       Union[str, Any]=1					,			_UpperCAmelCase  :       int=1_60_00					,			_UpperCAmelCase  :       Any=1_28					,			_UpperCAmelCase  :       Optional[Any]=10_24					,			_UpperCAmelCase  :       Optional[Any]=0.0					,			_UpperCAmelCase  :       Tuple=True					,			_UpperCAmelCase  :       List[str]=-4.2_677_393					,			_UpperCAmelCase  :       Any=4.5_689_974					,			_UpperCAmelCase  :       List[Any]=False					,			**_UpperCAmelCase  :       str					,			)						->		List[Any]:



											"""simple docstring"""


											super().__init__(feature_size=_lowerCamelCase					,			sampling_rate=_lowerCamelCase					,			padding_value=_lowerCamelCase					,			**_lowerCamelCase    )
											__lowercase								= num_mel_bins
											__lowercase								= max_length
											__lowercase								= do_normalize
											__lowercase								= mean
											__lowercase								= std
											__lowercase								= return_attention_mask





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       np.ndarray					,			_UpperCAmelCase  :       int					,			)						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= torch.from_numpy(_lowerCamelCase    ).unsqueeze(0    )
											__lowercase								= ta_kaldi.fbank(
											    _lowerCamelCase					,			htk_compat=_lowerCamelCase					,			sample_frequency=self.sampling_rate					,			use_energy=_lowerCamelCase					,			window_type='hanning'					,			num_mel_bins=self.num_mel_bins					,			dither=0.0					,			frame_shift=10					,			)

											__lowercase								= fbank.shape[0]
											__lowercase								= max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
															__lowercase								= torch.nn.ZeroPadad((0, 0, 0, difference)    )
															__lowercase								= pad_module(_lowerCamelCase    )
											elif difference < 0:
															__lowercase								= fbank[0:max_length, :]

											__lowercase								= fbank.numpy()

											return fbank





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       np.ndarray    )						->		List[str]:



											"""simple docstring"""


											return (input_values - (self.mean)) / (self.std * 2)





							def __call__(			self  :       Dict					,			_UpperCAmelCase  :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,			_UpperCAmelCase  :       Optional[int] = None					,			_UpperCAmelCase  :       Optional[Union[str, TensorType]] = None					,			**_UpperCAmelCase  :       Optional[int]					,			)						->		str:



											"""simple docstring"""


											if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																			    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																			    f""" {self.sampling_rate} and not {sampling_rate}."""    )
											else:
															logger.warning(
															    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
															    'Failing to do so can result in silent errors that might be hard to debug.'    )

											__lowercase								= isinstance(_lowerCamelCase					,			np.ndarray    ) and len(raw_speech.shape    ) > 1
											if is_batched_numpy and len(raw_speech.shape    ) > 2:
															raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""    )
											__lowercase								= is_batched_numpy or (
											    isinstance(_lowerCamelCase					,			(list, tuple)    ) and (isinstance(raw_speech[0]					,			(np.ndarray, tuple, list)    ))
											)

											if is_batched:
															__lowercase								= [np.asarray(_lowerCamelCase					,			dtype=np.floataa    ) for speech in raw_speech]
											elif not is_batched and not isinstance(_lowerCamelCase					,			np.ndarray    ):
															__lowercase								= np.asarray(_lowerCamelCase					,			dtype=np.floataa    )
											elif isinstance(_lowerCamelCase					,			np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
															__lowercase								= raw_speech.astype(np.floataa    )

											# always return batch
											if not is_batched:
															__lowercase								= [raw_speech]

											# extract fbank features and pad/truncate to max_length
											__lowercase								= [self._extract_fbank_features(_lowerCamelCase					,			max_length=self.max_length    ) for waveform in raw_speech]

											# convert into BatchFeature
											__lowercase								= BatchFeature({'input_values': features}    )

											# make sure list is in array format
											__lowercase								= padded_inputs.get('input_values'    )
											if isinstance(input_values[0]					,			_lowerCamelCase    ):
															__lowercase								= [np.asarray(_lowerCamelCase					,			dtype=np.floataa    ) for feature in input_values]

											# normalization
											if self.do_normalize:
															__lowercase								= [self.normalize(_lowerCamelCase    ) for feature in input_values]

											if return_tensors is not None:
															__lowercase								= padded_inputs.convert_to_tensors(_lowerCamelCase    )

											return padded_inputs







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      A__       (						unittest.TestCase   ,						lowerCAmelCase__    ):





							def       a__			(			self  :       Any    )						->		Dict:



											"""simple docstring"""


											__lowercase								= load_tool('translation'    )
											self.tool.setup()
											__lowercase								= load_tool('translation'					,			remote=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.remote_tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Union[str, Any]    )						->		Tuple:



											"""simple docstring"""


											__lowercase								= self.tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.remote_tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       List[Any]    )						->		int:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											# There is a single output
											if len(self.tool.outputs    ) == 1:
															__lowercase								= [outputs]

											self.assertListEqual(output_types(_UpperCAmelCase    )					,			self.tool.outputs    )





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



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )

											for output, output_type in zip(_UpperCAmelCase					,			self.tool.outputs    ):
															__lowercase								= AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )    )





							def       a__			(			self  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']

											__lowercase								= []

											for _input, input_type in zip(_UpperCAmelCase					,			self.tool.inputs    ):
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )





