

import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


lowerCAmelCase_			      =      logging.get_logger(__name__)





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Tuple		     =							["input_values", "attention_mask"]







			def __init__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							int = 1				,  _UpperCamelCase					:							int = 1_6_0_0_0				,  _UpperCamelCase					:							float = 0.0				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							int = 8_0				,  _UpperCamelCase					:							int = 1_6				,  _UpperCamelCase					:							int = 6_4				,  _UpperCamelCase					:							str = "hann_window"				,  _UpperCamelCase					:							float = 1.0				,  _UpperCamelCase					:							float = 8_0				,  _UpperCamelCase					:							float = 7_6_0_0				,  _UpperCamelCase					:							float = 1e-10				,  _UpperCamelCase					:							int = 2				,  _UpperCamelCase					:							bool = True				,  **_UpperCamelCase					:							Dict				,  ) ->Any:
									super().__init__(feature_size=_UpperCamelCase				,  sampling_rate=_UpperCamelCase				,  padding_value=_UpperCamelCase				,  **_UpperCamelCase				)
									snake_case_  =      do_normalize
									snake_case_  =      return_attention_mask

									snake_case_  =      num_mel_bins
									snake_case_  =      hop_length
									snake_case_  =      win_length
									snake_case_  =      win_function
									snake_case_  =      frame_signal_scale
									snake_case_  =      fmin
									snake_case_  =      fmax
									snake_case_  =      mel_floor
									snake_case_  =      reduction_factor

									snake_case_  =      win_length * sampling_rate // 1_0_0_0
									snake_case_  =      hop_length * sampling_rate // 1_0_0_0
									snake_case_  =      optimal_fft_length(self.sample_size				)
									snake_case_  =      (self.n_fft // 2) + 1

									snake_case_  =      window_function(window_length=self.sample_size				,  name=self.win_function				,  periodic=_UpperCamelCase				)

									snake_case_  =      mel_filter_bank(
									    num_frequency_bins=self.n_freqs				,  num_mel_filters=self.num_mel_bins				,  min_frequency=self.fmin				,  max_frequency=self.fmax				,  sampling_rate=self.sampling_rate				,  norm='''slaney'''				,  mel_scale='''slaney'''				,  )

									if frame_signal_scale != 1.0:
															warnings.warn(
															    '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''				,  _UpperCamelCase				,  )
									if reduction_factor != 2.0:
															warnings.warn(
															    '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''				,  _UpperCamelCase				,  )







			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def 							snake_case__(	_UpperCamelCase					:							List[np.ndarray]				,  _UpperCamelCase					:							List[np.ndarray]				,  _UpperCamelCase					:							float = 0.0				) ->List[np.ndarray]:
									if attention_mask is not None:
															snake_case_  =      np.array(_UpperCamelCase				,  np.intaa				)
															snake_case_  =      []

															for vector, length in zip(_UpperCamelCase				,  attention_mask.sum(-1				)				):
																					snake_case_  =      (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7				)
																					if length < normed_slice.shape[0]:
																											snake_case_  =      padding_value

																					normed_input_values.append(_UpperCamelCase				)
									else:
															snake_case_  =      [(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

									return normed_input_values







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							np.ndarray				,  ) ->np.ndarray:
									snake_case_  =      spectrogram(
									    _UpperCamelCase				,  window=self.window				,  frame_length=self.sample_size				,  hop_length=self.sample_stride				,  fft_length=self.n_fft				,  mel_filters=self.mel_filters				,  mel_floor=self.mel_floor				,  log_mel='''log10'''				,  )
									return log_mel_spec.T







			def __call__(	self					:							Any				,  _UpperCamelCase					:							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None				,  _UpperCamelCase					:							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None				,  _UpperCamelCase					:							Union[bool, str, PaddingStrategy] = False				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							Optional[bool] = None				,  _UpperCamelCase					:							Optional[Union[str, TensorType]] = None				,  _UpperCamelCase					:							Optional[int] = None				,  **_UpperCamelCase					:							List[str]				,  ) ->BatchFeature:
									if audio is None and audio_target is None:
															raise ValueError('''You must provide either `audio` or `audio_target` values.'''				)

									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 audio 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.'''				)

									if audio is not None:
															snake_case_  =      self._process_audio(
															    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  **_UpperCamelCase				,  )
									else:
															snake_case_  =      None

									if audio_target is not None:
															snake_case_  =      self._process_audio(
															    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  **_UpperCamelCase				,  )

															if inputs is None:
																					return inputs_target
															else:
																					snake_case_  =      inputs_target['''input_values''']
																					snake_case_  =      inputs_target.get('''attention_mask'''				)
																					if decoder_attention_mask is not None:
																											snake_case_  =      decoder_attention_mask

									return inputs







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							Union[bool, str, PaddingStrategy] = False				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							Optional[bool] = None				,  _UpperCamelCase					:							Optional[Union[str, TensorType]] = None				,  **_UpperCamelCase					:							int				,  ) ->BatchFeature:
									snake_case_  =      isinstance(_UpperCamelCase				,  np.ndarray				) and len(speech.shape				) > 1
									if is_batched_numpy and len(speech.shape				) > 2:
															raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''				)
									snake_case_  =      is_batched_numpy or (
									    isinstance(_UpperCamelCase				,  (list, tuple)				) and (isinstance(speech[0]				,  (np.ndarray, tuple, list)				))
									)

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

									# always return batch
									if not is_batched:
															snake_case_  =      [speech]

									# needed to make pad() work on spectrogram inputs
									snake_case_  =      self.feature_size

									# convert into correct format for padding
									if is_target:
															snake_case_  =      [self._extract_mel_features(_UpperCamelCase				) for waveform in speech]
															snake_case_  =      BatchFeature({'''input_values''': features}				)
															snake_case_  =      self.num_mel_bins
									else:
															snake_case_  =      BatchFeature({'''input_values''': speech}				)

									snake_case_  =      self.pad(
									    _UpperCamelCase				,  padding=_UpperCamelCase				,  max_length=_UpperCamelCase				,  truncation=_UpperCamelCase				,  pad_to_multiple_of=_UpperCamelCase				,  return_attention_mask=_UpperCamelCase				,  **_UpperCamelCase				,  )

									snake_case_  =      feature_size_hack

									# convert input values to correct format
									snake_case_  =      padded_inputs['''input_values''']
									if not isinstance(input_values[0]				,  np.ndarray				):
															snake_case_  =      [np.asarray(_UpperCamelCase				,  dtype=np.floataa				) for array in input_values]
									elif (
									    not isinstance(_UpperCamelCase				,  np.ndarray				)
									    and isinstance(input_values[0]				,  np.ndarray				)
									    and input_values[0].dtype is np.dtype(np.floataa				)
									):
															snake_case_  =      [array.astype(np.floataa				) for array in input_values]
									elif isinstance(_UpperCamelCase				,  np.ndarray				) and input_values.dtype is np.dtype(np.floataa				):
															snake_case_  =      input_values.astype(np.floataa				)

									# convert attention_mask to correct format
									snake_case_  =      padded_inputs.get('''attention_mask'''				)
									if attention_mask is not None:
															snake_case_  =      [np.asarray(_UpperCamelCase				,  dtype=np.intaa				) for array in attention_mask]

									# zero-mean and unit-variance normalization
									if not is_target and self.do_normalize:
															snake_case_  =      (
															    attention_mask
															    if self._get_padding_strategies(_UpperCamelCase				,  max_length=_UpperCamelCase				) is not PaddingStrategy.DO_NOT_PAD
															    else None
															)
															snake_case_  =      self.zero_mean_unit_var_norm(
															    padded_inputs['''input_values''']				,  attention_mask=_UpperCamelCase				,  padding_value=self.padding_value				)

									if return_tensors is not None:
															snake_case_  =      padded_inputs.convert_to_tensors(_UpperCamelCase				)

									return padded_inputs







			def 							snake_case__(	self					:							Tuple				) ->Dict[str, Any]:
									snake_case_  =      super().to_dict()

									# Don't serialize these as they are derived from the other properties.
									snake_case_  =      ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
									for name in names:
															if name in output:
																					del output[name]

									return output



'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							None
		_a       					=							None
		_a       					=							None
		_a       					=							None



class   lowercase      (				A__   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_=1       ,  UpperCamelCase_=0       ,  UpperCamelCase_=2       ,  UpperCamelCase_=512       ,  UpperCamelCase_="cls"       ,  UpperCamelCase_=False       ,  UpperCamelCase_=True       ,  **UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__(pad_token_id=UpperCamelCase_       ,  bos_token_id=UpperCamelCase_       ,  eos_token_id=UpperCamelCase_       ,  **UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					project_dim
							UpperCamelCase__  :List[str]				   =					pooler_fn
							UpperCamelCase__  :Dict				   =					learn_encoder
							UpperCamelCase__  :Any				   =					use_attention_mask



class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							[R'pooler', R'logit_scale']
		_a       					=							[R'position_ids', R'predictions.decoder.bias']
		_a       					=							'roberta'
		_a       					=							RobertaSeriesConfig




		def __init__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							super().__init__(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel(UpperCamelCase_				)
							UpperCamelCase__  :Tuple				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
							UpperCamelCase__  :Union[str, Any]				   =					getattr(UpperCamelCase_       ,  '''has_pre_transformation'''       ,  UpperCamelCase_				)
							if self.has_pre_transformation:
												UpperCamelCase__  :Any				   =					nn.Linear(config.hidden_size       ,  config.project_dim				)
												UpperCamelCase__  :str				   =					nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps				)
							self.post_init()




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					return_dict if return_dict is not None else self.config.use_return_dict

							UpperCamelCase__  :str				   =					self.base_model(
							    input_ids=UpperCamelCase_       ,  attention_mask=UpperCamelCase_       ,  token_type_ids=UpperCamelCase_       ,  position_ids=UpperCamelCase_       ,  head_mask=UpperCamelCase_       ,  inputs_embeds=UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  encoder_attention_mask=UpperCamelCase_       ,  output_attentions=UpperCamelCase_       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=UpperCamelCase_       ,  )

							if self.has_pre_transformation:
												UpperCamelCase__  :Dict				   =					outputs['''hidden_states'''][-2]
												UpperCamelCase__  :Optional[int]				   =					self.pre_LN(UpperCamelCase_				)
												UpperCamelCase__  :Optional[Any]				   =					self.transformation_pre(UpperCamelCase_				)

												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
							else:
												UpperCamelCase__  :List[Any]				   =					self.transformation(outputs.last_hidden_state				)
												return TransformationModelOutput(
												    projection_state=UpperCamelCase_       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )