

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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):




  @register_to_config
  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = False				,  )    ->					Union[str, Any]:
        '''simple docstring'''






        super().__init__()

        _lowercase      			=nn.Embedding(lowerCAmelCase				,  lowerCAmelCase   )

        _lowercase      			=nn.Embedding(lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=False

        _lowercase      			=nn.Dropout(p=lowerCAmelCase   )

        _lowercase      			=TaConfig(
            vocab_size=lowerCAmelCase				,  d_model=lowerCAmelCase				,  num_heads=lowerCAmelCase				,  d_kv=lowerCAmelCase				,  d_ff=lowerCAmelCase				,  dropout_rate=lowerCAmelCase				,  feed_forward_proj=lowerCAmelCase				,  is_decoder=lowerCAmelCase				,  is_encoder_decoder=lowerCAmelCase				,  )

        _lowercase      			=nn.ModuleList()
        for lyr_num in range(lowerCAmelCase   ):
              _lowercase      			=TaBlock(lowerCAmelCase   )
              self.encoders.append(lowerCAmelCase   )

        _lowercase      			=TaLayerNorm(lowerCAmelCase   )
        _lowercase      			=nn.Dropout(p=lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.token_embedder(lowerCAmelCase   )

        _lowercase      			=encoder_input_tokens.shape[1]
        _lowercase      			=torch.arange(lowerCAmelCase				,  device=encoder_input_tokens.device   )
        x += self.position_encoding(lowerCAmelCase   )

        _lowercase      			=self.dropout_pre(lowerCAmelCase   )

        # inverted the attention mask
        _lowercase      			=encoder_input_tokens.size()
        _lowercase      			=self.get_extended_attention_mask(lowerCAmelCase				,  lowerCAmelCase   )

        for lyr in self.encoders:
              _lowercase      			=lyr(lowerCAmelCase				,  lowerCAmelCase   )[0]
        _lowercase      			=self.layer_norm(lowerCAmelCase   )

        return self.dropout_post(lowerCAmelCase   ), encoder_inputs_mask





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowercase_   							=  datasets.utils.logging.get_logger(__name__)





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilderConfig				):
  _a		     =    None
  _a		     =    None





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilder				):
  _a		     =    datasets.Audio()
  _a		     =    """audio"""
  _a		     =    AudioFolderConfig
  _a		     =    42  # definition at the bottom of the script
  _a		     =    AudioClassification(audio_column="""audio"""     ,     label_column="""label"""				)
lowercase_   							=  [
    '.aiff',
    '.au',
    '.avr',
    '.caf',
    '.flac',
    '.htk',
    '.svx',
    '.mat4',
    '.mat5',
    '.mpc2k',
    '.ogg',
    '.paf',
    '.pvf',
    '.raw',
    '.rf64',
    '.sd2',
    '.sds',
    '.ircam',
    '.voc',
    '.w64',
    '.wav',
    '.nist',
    '.wavex',
    '.wve',
    '.xi',
    '.mp3',
    '.opus',
]
lowercase_   							=  AUDIO_EXTENSIONS


