





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class   A							(						__lowercase    ):
      _snake_case				  ='''Wav2Vec2FeatureExtractor'''
      _snake_case				  ='''AutoTokenizer'''



      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    Optional[Any]		,	_lowerCAmelCase:    Any							)    ->     List[str]:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase		,	_lowerCAmelCase							)
         UpperCAmelCase_           =self.feature_extractor
         UpperCAmelCase_           =False



      @classmethod
      def 						lowerCAmelCase__     (						cls:    List[Any]		,	_lowerCAmelCase:    int		,	**_lowerCAmelCase:    int							)    ->     int:
         '''simple docstring'''

         try:
            return super().from_pretrained(_lowerCAmelCase		,	**_lowerCAmelCase							)
         except OSError:
            warnings.warn(
                F'Loading a tokenizer inside {cls.__name__} from a config that does not'
                " include a `tokenizer_class` attribute is deprecated and will be "
                "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
                " attribute to either your `config.json` or `tokenizer_config.json` "
                "file to suppress this warning: "		,	_lowerCAmelCase		,	)

            UpperCAmelCase_           =WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase		,	**_lowerCAmelCase							)
            UpperCAmelCase_           =WavaVecaCTCTokenizer.from_pretrained(_lowerCAmelCase		,	**_lowerCAmelCase							)

            return cls(feature_extractor=_lowerCAmelCase		,	tokenizer=_lowerCAmelCase							)



      def __call__(						self:    Dict		,	*_lowerCAmelCase:    List[str]		,	**_lowerCAmelCase:    Union[str, Any]							)    ->     List[Any]:
         '''simple docstring'''

         if self._in_target_context_manager:
            return self.current_processor(*_lowerCAmelCase		,	**_lowerCAmelCase							)

         if "raw_speech" in kwargs:
            warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."							)
            UpperCAmelCase_           =kwargs.pop("raw_speech"							)
         else:
            UpperCAmelCase_           =kwargs.pop("audio"		,	_lowerCAmelCase							)
         UpperCAmelCase_           =kwargs.pop("sampling_rate"		,	_lowerCAmelCase							)
         UpperCAmelCase_           =kwargs.pop("text"		,	_lowerCAmelCase							)
         if len(_lowerCAmelCase							) > 0:
            UpperCAmelCase_           =args[0]
            UpperCAmelCase_           =args[1:]

         if audio is None and text is None:
            raise ValueError("You need to specify either an `audio` or `text` input to process."							)

         if audio is not None:
            UpperCAmelCase_           =self.feature_extractor(_lowerCAmelCase		,	*_lowerCAmelCase		,	sampling_rate=_lowerCAmelCase		,	**_lowerCAmelCase							)
         if text is not None:
            UpperCAmelCase_           =self.tokenizer(_lowerCAmelCase		,	**_lowerCAmelCase							)

         if text is None:
            return inputs
         elif audio is None:
            return encodings
         else:
            UpperCAmelCase_           =encodings["input_ids"]
            return inputs



      def 						lowerCAmelCase__     (						self:    str		,	*_lowerCAmelCase:    str		,	**_lowerCAmelCase:    Optional[int]							)    ->     Union[str, Any]:
         '''simple docstring'''

         if self._in_target_context_manager:
            return self.current_processor.pad(*_lowerCAmelCase		,	**_lowerCAmelCase							)

         UpperCAmelCase_           =kwargs.pop("input_features"		,	_lowerCAmelCase							)
         UpperCAmelCase_           =kwargs.pop("labels"		,	_lowerCAmelCase							)
         if len(_lowerCAmelCase							) > 0:
            UpperCAmelCase_           =args[0]
            UpperCAmelCase_           =args[1:]

         if input_features is not None:
            UpperCAmelCase_           =self.feature_extractor.pad(_lowerCAmelCase		,	*_lowerCAmelCase		,	**_lowerCAmelCase							)
         if labels is not None:
            UpperCAmelCase_           =self.tokenizer.pad(_lowerCAmelCase		,	**_lowerCAmelCase							)

         if labels is None:
            return input_features
         elif input_features is None:
            return labels
         else:
            UpperCAmelCase_           =labels["input_ids"]
            return input_features



      def 						lowerCAmelCase__     (						self:    List[Any]		,	*_lowerCAmelCase:    Optional[int]		,	**_lowerCAmelCase:    Tuple							)    ->     Optional[int]:
         '''simple docstring'''

         return self.tokenizer.batch_decode(*_lowerCAmelCase		,	**_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    Dict		,	*_lowerCAmelCase:    List[Any]		,	**_lowerCAmelCase:    str							)    ->     str:
         '''simple docstring'''

         return self.tokenizer.decode(*_lowerCAmelCase		,	**_lowerCAmelCase							)






      @contextmanager
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     int:
         '''simple docstring'''

         warnings.warn(
             "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
             "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
             "your audio inputs, or in a separate call."							)
         UpperCAmelCase_           =True
         UpperCAmelCase_           =self.tokenizer
         yield
         UpperCAmelCase_           =self.feature_extractor
         UpperCAmelCase_           =False








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__				:	str					       =					logging.get_logger(__name__)





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''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=a__						,       metadata={"help": "Overwrite the cached training and evaluation sets"}						)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->Union[str, Any]:
          lowerCamelCase_						:   Optional[Any]		= self.task_name.lower()







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							"train"
       _a   							=							"dev"
       _a   							=							"test"







class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							42
       _a   							=							42
       _a   							=							42



       def __init__(						self				:				Any		,				__a				:				GlueDataTrainingArguments		,				__a				:				PreTrainedTokenizerBase		,				__a				:				Optional[int] = None		,				__a				:				Union[str, Split] = Split.train		,				__a				:				Optional[str] = None		,				)					->Optional[int]:
          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"""		,				__a		,				)
          lowerCamelCase_						:   Optional[int]		= args
          lowerCamelCase_						:   Tuple		= glue_processors[args.task_name]()
          lowerCamelCase_						:   Optional[Any]		= glue_output_modes[args.task_name]
          if isinstance(__a		,				__a							):
             try:
                lowerCamelCase_						:   List[Any]		= Split[mode]
             except KeyError:
                raise KeyError("""mode is not a valid split name"""							)
        # Load data features from cache or dataset file
          lowerCamelCase_						:   List[Any]		= 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}'''		,				)
          lowerCamelCase_						:   Any		= 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)
             lowerCamelCase_,	lowerCamelCase_						:   int		= label_list[2], label_list[1]
          lowerCamelCase_						:   Any		= label_list

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowerCamelCase_						:   List[Any]		= cached_features_file + """.lock"""
          with FileLock(__a							):
             if os.path.exists(__a							) and not args.overwrite_cache:
                lowerCamelCase_						:   str		= time.time()
                lowerCamelCase_						:   int		= torch.load(__a							)
                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:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_dev_examples(args.data_dir							)
                elif mode == Split.test:
                   lowerCamelCase_						:   Tuple		= self.processor.get_test_examples(args.data_dir							)
                else:
                   lowerCamelCase_						:   List[Any]		= self.processor.get_train_examples(args.data_dir							)
                if limit_length is not None:
                   lowerCamelCase_						:   Dict		= examples[:limit_length]
                lowerCamelCase_						:   Union[str, Any]		= glue_convert_examples_to_features(
                    __a		,				__a		,				max_length=args.max_seq_length		,				label_list=__a		,				output_mode=self.output_mode		,				)
                lowerCamelCase_						:   Optional[Any]		= time.time()
                torch.save(self.features		,				__a							)
                # ^ 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				:				Any							)					->Any:
          return len(self.features							)



       def __getitem__(						self				:				List[Any]		,				__a				:				Optional[int]							)					->InputFeatures:
          return self.features[i]




       def 				_lowerCAmelCase							(						self				:				int							)					->Optional[int]:
          return self.label_list





