






import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__      :	Optional[int]       	=      logging.get_logger(__name__)

snake_case__      :	Dict       	=      {
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class 						_a       (  __UpperCAmelCase    ):






  """simple docstring"""





  A_						=   """data2vec-audio"""




  def __init__(     self		,		_UpperCAmelCase=32		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)		,		_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,		_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,		_UpperCAmelCase=False		,		_UpperCAmelCase=16		,		_UpperCAmelCase=19		,		_UpperCAmelCase=5		,		_UpperCAmelCase=0.0_5		,		_UpperCAmelCase=10		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=10		,		_UpperCAmelCase=0		,		_UpperCAmelCase="sum"		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=256		,		_UpperCAmelCase=(512, 512, 512, 512, 1500)		,		_UpperCAmelCase=(5, 3, 3, 1, 1)		,		_UpperCAmelCase=(1, 2, 3, 1, 1)		,		_UpperCAmelCase=512		,		_UpperCAmelCase=0		,		_UpperCAmelCase=1		,		_UpperCAmelCase=2		,		_UpperCAmelCase=False		,		_UpperCAmelCase=3		,		_UpperCAmelCase=2		,		_UpperCAmelCase=3		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Union[str, Any]:
     super().__init__(**lowerCAmelCase_		,		pad_token_id=lowerCAmelCase_		,		bos_token_id=lowerCAmelCase_		,		eos_token_id=lowerCAmelCase_ )
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     feat_extract_activation
     UpperCamelCase_									=     list(lowerCAmelCase_ )
     UpperCamelCase_									=     list(lowerCAmelCase_ )
     UpperCamelCase_									=     list(lowerCAmelCase_ )
     UpperCamelCase_									=     conv_bias
     UpperCamelCase_									=     num_conv_pos_embeddings
     UpperCamelCase_									=     num_conv_pos_embedding_groups
     UpperCamelCase_									=     conv_pos_kernel_size
     UpperCamelCase_									=     len(self.conv_dim )
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     hidden_dropout
     UpperCamelCase_									=     attention_dropout
     UpperCamelCase_									=     activation_dropout
     UpperCamelCase_									=     feat_proj_dropout
     UpperCamelCase_									=     final_dropout
     UpperCamelCase_									=     layerdrop
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     use_weighted_layer_sum

     if (
         (len(self.conv_stride ) != self.num_feat_extract_layers)
         or (len(self.conv_kernel ) != self.num_feat_extract_layers)
         or (len(self.conv_dim ) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
            ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
            f""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
            f""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     UpperCamelCase_									=     mask_time_prob
     UpperCamelCase_									=     mask_time_length
     UpperCamelCase_									=     mask_time_min_masks
     UpperCamelCase_									=     mask_feature_prob
     UpperCamelCase_									=     mask_feature_length
     UpperCamelCase_									=     mask_feature_min_masks

     # ctc loss
     UpperCamelCase_									=     ctc_loss_reduction
     UpperCamelCase_									=     ctc_zero_infinity

     # adapter
     UpperCamelCase_									=     add_adapter
     UpperCamelCase_									=     adapter_kernel_size
     UpperCamelCase_									=     adapter_stride
     UpperCamelCase_									=     num_adapter_layers
     UpperCamelCase_									=     output_hidden_size or hidden_size

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     UpperCamelCase_									=     classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     UpperCamelCase_									=     list(lowerCAmelCase_ )
     UpperCamelCase_									=     list(lowerCAmelCase_ )
     UpperCamelCase_									=     list(lowerCAmelCase_ )
     UpperCamelCase_									=     xvector_output_dim




  @property
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return math.prod(self.conv_stride )













import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__      :	Any       	=      logging.get_logger(__name__)


@dataclass
class 						_a       :






  """simple docstring"""





  A_						=   field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()    )}    )
  A_						=   field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}    )
  A_						=   field(
      default=128						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.task_name.lower()




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """train"""
  A_						=   """dev"""
  A_						=   """test"""




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = Split.train		,		_UpperCAmelCase = None		,		)			-> Tuple:
     warnings.warn(
         'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
         'library. You can have a look at this example script for pointers: '
         'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'		,		_UpperCAmelCase		,		)
     UpperCamelCase_									=     args
     UpperCamelCase_									=     glue_processors[args.task_name]()
     UpperCamelCase_									=     glue_output_modes[args.task_name]
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        try:
           UpperCamelCase_									=     Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
     UpperCamelCase_									=     os.path.join(
         cache_dir if cache_dir is not None else args.data_dir		,		f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""		,		)
     UpperCamelCase_									=     self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
     UpperCamelCase_									=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     UpperCamelCase_									=     cached_features_file + '.lock'
     with FileLock(_UpperCAmelCase ):
        if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
           UpperCamelCase_									=     time.time()
           UpperCamelCase_									=     torch.load(_UpperCAmelCase )
           logger.info(
               f"""Loading features from cached file {cached_features_file} [took %.3f s]"""		,		time.time() - start )
        else:
           logger.info(f"""Creating features from dataset file at {args.data_dir}""" )

           if mode == Split.dev:
              UpperCamelCase_									=     self.processor.get_dev_examples(args.data_dir )
           elif mode == Split.test:
              UpperCamelCase_									=     self.processor.get_test_examples(args.data_dir )
           else:
              UpperCamelCase_									=     self.processor.get_train_examples(args.data_dir )
           if limit_length is not None:
              UpperCamelCase_									=     examples[:limit_length]
           UpperCamelCase_									=     glue_convert_examples_to_features(
               _UpperCAmelCase		,		_UpperCAmelCase		,		max_length=args.max_seq_length		,		label_list=_UpperCAmelCase		,		output_mode=self.output_mode		,		)
           UpperCamelCase_									=     time.time()
           torch.save(self.features		,		_UpperCAmelCase )
           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
           logger.info(
               f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )




  def __len__(     self )			-> List[str]:
     return len(self.features )




  def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
     return self.features[i]




  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.label_list





