"""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


_a				:		Dict	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      str		 =		["audio_values", "audio_mask"]






     def __init__(		self    ,					a__=2048    ,					a__=1    ,					a__=[16, 16]    ,					a__=128    ,					a__=44100    ,					a__=86    ,					a__=2048    ,					a__=0.0    ,					**a__    ,					):
       super().__init__(
           feature_size=a__    ,					sampling_rate=a__    ,					padding_value=a__    ,					**a__    ,					)

       _lowerCAmelCase						:	Dict        =					spectrogram_length
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	Union[str, Any]        =					patch_size
       _lowerCAmelCase						:	Tuple        =					feature_size // self.patch_size[1]
       _lowerCAmelCase						:	Optional[Any]        =					n_fft
       _lowerCAmelCase						:	Union[str, Any]        =					sampling_rate // hop_length_to_sampling_rate
       _lowerCAmelCase						:	Any        =					sampling_rate
       _lowerCAmelCase						:	str        =					padding_value
       _lowerCAmelCase						:	Tuple        =					mel_filter_bank(
           num_frequency_bins=1 + n_fft // 2    ,					num_mel_filters=a__    ,					min_frequency=0.0    ,					max_frequency=2_2_0_5_0.0    ,					sampling_rate=a__    ,					norm="""slaney"""    ,					mel_scale="""slaney"""    ,					).T






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					spectrogram(
           a__    ,					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=8_0.0    ,					)
       _lowerCAmelCase						:	Union[str, Any]        =					log_spec[:, :-1]
       _lowerCAmelCase						:	Any        =					log_spec - 2_0.0
       _lowerCAmelCase						:	Any        =					np.clip(log_spec / 4_0.0    ,					-2.0    ,					0.0  ) + 1.0
       return log_spec






     def __call__(		self    ,					a__    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = False    ,					**a__    ,					):

       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."""  )

       _lowerCAmelCase						:	List[Any]        =					isinstance(a__    ,					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}"  )
       _lowerCAmelCase						:	str        =					is_batched_numpy or (
           isinstance(a__    ,					(list, tuple)  ) and (isinstance(raw_speech[0]    ,					(np.ndarray, tuple, list)  ))
       )
       if is_batched:
         _lowerCAmelCase						:	List[Any]        =					[np.asarray([speech]    ,					dtype=np.floataa  ).T for speech in raw_speech]
       elif not is_batched and not isinstance(a__    ,					np.ndarray  ):
         _lowerCAmelCase						:	Dict        =					np.asarray(a__    ,					dtype=np.floataa  )
       elif isinstance(a__    ,					np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
         _lowerCAmelCase						:	Optional[Any]        =					raw_speech.astype(np.floataa  )
       # always return batch
       if not is_batched:
         _lowerCAmelCase						:	Any        =					[np.asarray([raw_speech]  ).T]

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

       # Create audio attention mask
       _lowerCAmelCase						:	Dict        =					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:
         _lowerCAmelCase						:	Dict        =					[
             (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
         ]
         _lowerCAmelCase						:	Union[str, Any]        =					np.array(a__  ).astype(np.floataa  )

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

       # return as BatchFeature
       if return_attention_mask:
         _lowerCAmelCase						:	Any        =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					{"""audio_values""": padded_audio_features}

       _lowerCAmelCase						:	Any        =					BatchFeature(data=a__    ,					tensor_type=a__  )
       return encoded_inputs





"""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 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




