



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


lowerCamelCase__     =	logging.get_logger(__name__)






class 				SCREAMING_SNAKE_CASE     (							_lowerCAmelCase   ):
							__lowerCamelCase		:		str      =["input_values", "attention_mask"]





							def __init__(				self						:						List[str] ,			__lowercase						:						Union[str, Any]=1 ,			__lowercase						:						int=16000 ,			__lowercase						:						Any=128 ,			__lowercase						:						Optional[Any]=1024 ,			__lowercase						:						Optional[Any]=0.0 ,			__lowercase						:						Tuple=True ,			__lowercase						:						List[str]=-4.2677393 ,			__lowercase						:						Any=4.5689974 ,			__lowercase						:						List[Any]=False ,			**__lowercase						:						str ,			):

											'''simple docstring'''
											super().__init__(feature_size=_lowerCamelCase ,			sampling_rate=_lowerCamelCase ,			padding_value=_lowerCamelCase ,			**_lowerCamelCase		)
											__a					       =						num_mel_bins
											__a					       =						max_length
											__a					       =						do_normalize
											__a					       =						mean
											__a					       =						std
											__a					       =						return_attention_mask





							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						np.ndarray ,			__lowercase						:						int ,			):

											'''simple docstring'''
											__a					       =						torch.from_numpy(_lowerCamelCase		).unsqueeze(0		)
											__a					       =						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 ,			)

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

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

											__a					       =						fbank.numpy()

											return fbank





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						np.ndarray		):

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







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

											'''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."""		)

											__a					       =						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}"		)
											__a					       =						is_batched_numpy or (
											    isinstance(_lowerCamelCase ,			(list, tuple)		) and (isinstance(raw_speech[0] ,			(np.ndarray, tuple, list)		))
											)

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

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

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

											# convert into BatchFeature
											__a					       =						BatchFeature({"""input_values""": features}		)

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

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

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

											return padded_inputs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase__     =	{
    """configuration_oneformer""": ["""ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OneFormerConfig"""],
    """processing_oneformer""": ["""OneFormerProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	["""OneFormerImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__     =	[
						    """ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """OneFormerForUniversalSegmentation""",
						    """OneFormerModel""",
						    """OneFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
						from .processing_oneformer import OneFormerProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_oneformer import OneFormerImageProcessor
						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_oneformer import (
												    ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OneFormerForUniversalSegmentation,
												    OneFormerModel,
												    OneFormerPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
