





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class        __SCREAMING_SNAKE_CASE      (			lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__ ):







       @register_to_config
       def __init__(				self					:     Any		, A					:     int		, A					:     int		, A					:     int		, A					:     float		, A					:     int		, A					:     int		, A					:     int		, A					:     int		, A					:     str		, A					:     bool = False		, )       ->Union[str, Any]:
         super().__init__()

         lowerCamelCase__       :						Any        =							nn.Embedding(__snake_case		, __snake_case      )

         lowerCamelCase__       :						int        =							nn.Embedding(__snake_case		, __snake_case      )
         lowerCamelCase__       :						Union[str, Any]        =							False

         lowerCamelCase__       :						Any        =							nn.Dropout(p=__snake_case      )

         lowerCamelCase__       :						List[str]        =							TaConfig(
             vocab_size=__snake_case		, d_model=__snake_case		, num_heads=__snake_case		, d_kv=__snake_case		, d_ff=__snake_case		, dropout_rate=__snake_case		, feed_forward_proj=__snake_case		, is_decoder=__snake_case		, is_encoder_decoder=__snake_case		, )

         lowerCamelCase__       :						Optional[int]        =							nn.ModuleList()
         for lyr_num in range(__snake_case      ):
           lowerCamelCase__       :						str        =							TaBlock(__snake_case      )
           self.encoders.append(__snake_case      )

         lowerCamelCase__       :						Any        =							TaLayerNorm(__snake_case      )
         lowerCamelCase__       :						List[Any]        =							nn.Dropout(p=__snake_case      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     Optional[Any]		, A					:     Union[str, Any]      )       ->Optional[Any]:
         lowerCamelCase__       :						int        =							self.token_embedder(__snake_case      )

         lowerCamelCase__       :						Dict        =							encoder_input_tokens.shape[1]
         lowerCamelCase__       :						Any        =							torch.arange(__snake_case		, device=encoder_input_tokens.device      )
         x += self.position_encoding(__snake_case      )

         lowerCamelCase__       :						Union[str, Any]        =							self.dropout_pre(__snake_case      )

         # inverted the attention mask
         lowerCamelCase__       :						str        =							encoder_input_tokens.size()
         lowerCamelCase__       :						int        =							self.get_extended_attention_mask(__snake_case		, __snake_case      )

         for lyr in self.encoders:
           lowerCamelCase__       :						Tuple        =							lyr(__snake_case		, __snake_case      )[0]
         lowerCamelCase__       :						int        =							self.layer_norm(__snake_case      )

         return self.dropout_post(__snake_case      ), encoder_inputs_mask










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 :   List[str]  			=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Any			   =   ["audio_values", "audio_mask"]







       def __init__(				self					:     Any		, A					:     Union[str, Any]=2_0_4_8		, A					:     Any=1		, A					:     int=[1_6, 1_6]		, A					:     Any=1_2_8		, A					:     List[Any]=4_4_1_0_0		, A					:     Dict=8_6		, A					:     Dict=2_0_4_8		, A					:     str=0.0		, **A					:     Union[str, Any]		, )       ->List[Any]:
          super().__init__(
              feature_size=A		, sampling_rate=A		, padding_value=A		, **A		, )

          lowerCamelCase__       :						Dict        =							spectrogram_length
          lowerCamelCase__       :						Optional[int]        =							num_channels
          lowerCamelCase__       :						str        =							patch_size
          lowerCamelCase__       :						Any        =							feature_size // self.patch_size[1]
          lowerCamelCase__       :						Union[str, Any]        =							n_fft
          lowerCamelCase__       :						Union[str, Any]        =							sampling_rate // hop_length_to_sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							padding_value
          lowerCamelCase__       :						Union[str, Any]        =							mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2		, num_mel_filters=A		, min_frequency=0.0		, max_frequency=2_20_50.0		, sampling_rate=A		, norm='''slaney'''		, mel_scale='''slaney'''		, ).T







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     np.array      )       ->np.ndarray:
          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=80.0		, )
          lowerCamelCase__       :						Any        =							log_spec[:, :-1]
          lowerCamelCase__       :						int        =							log_spec - 20.0
          lowerCamelCase__       :						int        =							np.clip(log_spec / 40.0		, -2.0		, 0.0      ) + 1.0
          return log_spec







       def __call__(				self					:     Union[str, Any]		, A					:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		, A					:     Optional[Union[str, TensorType]] = None		, A					:     Optional[bool] = True		, A					:     Optional[int] = None		, A					:     bool = False		, A					:     bool = False		, **A					:     str		, )       ->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.'''      )

          lowerCamelCase__       :						List[str]        =							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__       :						Any        =							is_batched_numpy or (
              isinstance(A		, (list, tuple)      ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list)      ))
          )
          if is_batched:
             lowerCamelCase__       :						Optional[int]        =							[np.asarray([speech]		, dtype=np.floataa      ).T for speech in raw_speech]
          elif not is_batched and not isinstance(A		, np.ndarray      ):
             lowerCamelCase__       :						Optional[int]        =							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__       :						int        =							[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__       :						Dict        =							[np.asarray(A		, dtype=np.floataa      ) for feature in audio_features]

          # Create audio attention mask
          lowerCamelCase__       :						Optional[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:
             lowerCamelCase__       :						List[Any]        =							[
                 (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__       :						List[str]        =							np.array(A      ).astype(np.floataa      )

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

          # return as BatchFeature
          if return_attention_mask:
             lowerCamelCase__       :						Optional[int]        =							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
          else:
             lowerCamelCase__       :						Tuple        =							{'''audio_values''': padded_audio_features}

          lowerCamelCase__       :						Union[str, Any]        =							BatchFeature(data=A		, tensor_type=A      )
          return encoded_inputs



