



'''simple docstring'''
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 :      List[Any]             =logging.get_logger(__name__)


class   SCREAMING_SNAKE_CASE__				(   __SCREAMING_SNAKE_CASE					):
	"""simple docstring"""


	A__    :    Optional[Any]						=				['''input_values''', '''attention_mask''']


	def __init__(		self					,   A=1					,   A=1_60_00					,   A=1_28					,   A=10_24					,   A=0.0					,   A=True					,   A=-4.2677393					,   A=4.5689974					,   A=False					,   **A					,   ) ->							Any:
				super().__init__(feature_size=UpperCamelCase__					,   sampling_rate=UpperCamelCase__					,   padding_value=UpperCamelCase__					,   **UpperCamelCase__			)
				A:      Tuple														=   num_mel_bins
				A:      Union[str, Any]														=   max_length
				A:      List[Any]														=   do_normalize
				A:      str														=   mean
				A:      List[str]														=   std
				A:      Any														=   return_attention_mask


	def      a__ (		self					,   A					,   A					,   ) ->							np.ndarray:
				A:      int														=   torch.from_numpy(UpperCamelCase__			).unsqueeze(0			)
				A:      Dict														=   ta_kaldi.fbank(
				    UpperCamelCase__					,   htk_compat=UpperCamelCase__					,   sample_frequency=self.sampling_rate					,   use_energy=UpperCamelCase__					,   window_type="""hanning"""					,   num_mel_bins=self.num_mel_bins					,   dither=0.0					,   frame_shift=10					,   )

				A:      List[str]														=   fbank.shape[0]
				A:      Any														=   max_length - n_frames

				# pad or truncate, depending on difference
				if difference > 0:
							A:      Optional[Any]														=   torch.nn.ZeroPadad((0, 0, 0, difference)			)
							A:      Tuple														=   pad_module(UpperCamelCase__			)
				elif difference < 0:
							A:      Optional[int]														=   fbank[0:max_length, :]

				A:      Any														=   fbank.numpy()

				return fbank


	def      a__ (		self					,   A			) ->							np.ndarray:
				return (input_values - (self.mean)) / (self.std * 2)





	def __call__(		self					,   A					,   A = None					,   A = None					,   **A					,   ) ->							BatchFeature:







				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:      List[str]														=   isinstance(UpperCamelCase__					,   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:      Any														=   is_batched_numpy or (
				    isinstance(UpperCamelCase__					,   (list, tuple)			) and (isinstance(raw_speech[0]					,   (np.ndarray, tuple, list)			))
				)

				if is_batched:
							A:      Any														=   [np.asarray(UpperCamelCase__					,   dtype=np.floataa			) for speech in raw_speech]
				elif not is_batched and not isinstance(UpperCamelCase__					,   np.ndarray			):
							A:      int														=   np.asarray(UpperCamelCase__					,   dtype=np.floataa			)
				elif isinstance(UpperCamelCase__					,   np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
							A:      Tuple														=   raw_speech.astype(np.floataa			)

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

				# extract fbank features and pad/truncate to max_length
				A:      Tuple														=   [self._extract_fbank_features(UpperCamelCase__					,   max_length=self.max_length			) for waveform in raw_speech]

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

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

				# normalization
				if self.do_normalize:
							A:      int														=   [self.normalize(UpperCamelCase__			) for feature in input_values]

				if return_tensors is not None:
							A:      Union[str, Any]														=   padded_inputs.convert_to_tensors(UpperCamelCase__			)

				return padded_inputs










'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}


class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_					):
	"""simple docstring"""


	A__    :    List[str]						=				'''convnext'''


	def __init__(		self					,   A=3					,   A=4					,   A=4					,   A=None					,   A=None					,   A="gelu"					,   A=0.02					,   A=1e-12					,   A=1e-6					,   A=0.0					,   A=2_24					,   A=None					,   A=None					,   **A					,   ) ->							Dict:
				super().__init__(**A			)

				A:      List[str]														=   num_channels
				A:      Optional[Any]														=   patch_size
				A:      List[str]														=   num_stages
				A:      Tuple														=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
				A:      List[Any]														=   [3, 3, 9, 3] if depths is None else depths
				A:      int														=   hidden_act
				A:      int														=   initializer_range
				A:      Optional[int]														=   layer_norm_eps
				A:      Tuple														=   layer_scale_init_value
				A:      int														=   drop_path_rate
				A:      Tuple														=   image_size
				A:      Union[str, Any]														=   ["""stem"""] + [f'stage{idx}' for idx in range(1					,   len(self.depths			) + 1			)]
				A							,		A:      List[str]														=   get_aligned_output_features_output_indices(
				    out_features=A					,   out_indices=A					,   stage_names=self.stage_names			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Any						=				version.parse('''1.11'''					)


	@property
	def      a__ (		self			) ->							Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
				    ]			)





	@property
	def      a__ (		self			) ->							float:
				return 1e-5





