



'''simple docstring'''
from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE			:   int							   =logging.get_logger(__name__)


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


	A__     :     str    =					['''audio_values''', '''audio_mask''']


	def __init__(		self       , A=20_48       , A=1       , A=[16, 16]       , A=1_28       , A=4_41_00       , A=86       , A=20_48       , A=0.0       , **A       , )			->				List[Any]:
				super().__init__(
				    feature_size=UpperCamelCase__       , sampling_rate=UpperCamelCase__       , padding_value=UpperCamelCase__       , **UpperCamelCase__       , )

				A:   int								= spectrogram_length
				A:   List[Any]								= num_channels
				A:   str								= patch_size
				A:   str								= feature_size // self.patch_size[1]
				A:   List[str]								= n_fft
				A:   int								= sampling_rate // hop_length_to_sampling_rate
				A:   List[str]								= sampling_rate
				A:   Union[str, Any]								= padding_value
				A:   Optional[Any]								= mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2       , num_mel_filters=UpperCamelCase__       , min_frequency=0.0       , max_frequency=2_20_50.0       , sampling_rate=UpperCamelCase__       , norm="""slaney"""       , mel_scale="""slaney"""       , ).T


	def   a__			(		self       , A      )			->				np.ndarray:
				A:   List[Any]								= spectrogram(
				    UpperCamelCase__       , window_function(self.n_fft       , """hann"""      )       , frame_length=self.n_fft       , hop_length=self.hop_length       , power=2.0       , mel_filters=self.mel_filters.T       , log_mel="""dB"""       , db_range=80.0       , )
				A:   Union[str, Any]								= log_spec[:, :-1]
				A:   str								= log_spec - 20.0
				A:   str								= np.clip(log_spec / 40.0       , -2.0       , 0.0      ) + 1.0
				return log_spec





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

				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    """This feature extractor is set to support sampling rate"""
										    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
										    f' with {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:   Union[str, Any]								= 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:   List[str]								= [np.asarray([speech]       , dtype=np.floataa      ).T 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:   int								= raw_speech.astype(np.floataa      )
				# always return batch
				if not is_batched:
							A:   Union[str, Any]								= [np.asarray([raw_speech]      ).T]

				# Convert audio signals to log mel spectrograms, truncate by time axis
				A:   Dict								= [
				    self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
				]
				if isinstance(audio_features[0]       , UpperCamelCase__      ):
							A:   Union[str, Any]								= [np.asarray(UpperCamelCase__       , dtype=np.floataa      ) for feature in audio_features]

				# Create audio attention mask
				A:   List[Any]								= max(
				    [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
				if return_attention_mask:
							A:   int								= [
							    (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
							    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
							    for feature in audio_features
							]
							A:   Optional[int]								= np.array(UpperCamelCase__      ).astype(np.floataa      )

				# convert into correct format for padding
				A:   List[Any]								= max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
				A:   Dict								= np.ones([len(UpperCamelCase__      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
				A:   Optional[Any]								= padded_audio_features * self.padding_value
				for i in range(len(UpperCamelCase__      )      ):
							A:   int								= audio_features[i]
							A:   Union[str, Any]								= feature

				# return as BatchFeature
				if return_attention_mask:
							A:   Dict								= {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
				else:
							A:   Optional[int]								= {"""audio_values""": padded_audio_features}

				A:   List[str]								= BatchFeature(data=UpperCamelCase__       , tensor_type=UpperCamelCase__      )
				return encoded_inputs






'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )



