



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


lowerCAmelCase__		:					Any							=logging.get_logger(__name__)

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()	)}	)
						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}	)
						UpperCamelCase__      :			int      =  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.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			bool      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.task_name.lower()







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Dict      =  '''train'''
						UpperCamelCase__      :			Optional[Any]      =  '''dev'''
						UpperCamelCase__      :			Any      =  '''test'''







class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			GlueDataTrainingArguments
						UpperCamelCase__      :			str
						UpperCamelCase__      :			List[InputFeatures]






						def __init__(   self				,							_A				,							_A				,							_A = None				,							_A = Split.train				,							_A = None				,							):







													'''simple docstring'''

													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				,							)
													__SCREAMING_SNAKE_CASE									= args
													__SCREAMING_SNAKE_CASE									= glue_processors[args.task_name]()
													__SCREAMING_SNAKE_CASE									= glue_output_modes[args.task_name]
													if isinstance(_A				,							_A		):
																				try:
																											__SCREAMING_SNAKE_CASE									= Split[mode]
																				except KeyError:
																											raise KeyError('mode is not a valid split name'		)
        # Load data features from cache or dataset file
													__SCREAMING_SNAKE_CASE									= 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}"""				,							)
													__SCREAMING_SNAKE_CASE									= 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)
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= label_list[2], label_list[1]
													__SCREAMING_SNAKE_CASE									= label_list

													# Make sure only the first process in distributed training processes the dataset,
													# and the others will use the cache.
													__SCREAMING_SNAKE_CASE									= cached_features_file + '.lock'
													with FileLock(_A		):
																				if os.path.exists(_A		) and not args.overwrite_cache:
																											__SCREAMING_SNAKE_CASE									= time.time()
																											__SCREAMING_SNAKE_CASE									= 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:
																																		__SCREAMING_SNAKE_CASE									= self.processor.get_dev_examples(args.data_dir		)
																											elif mode == Split.test:
																																		__SCREAMING_SNAKE_CASE									= self.processor.get_test_examples(args.data_dir		)
																											else:
																																		__SCREAMING_SNAKE_CASE									= self.processor.get_train_examples(args.data_dir		)
																											if limit_length is not None:
																																		__SCREAMING_SNAKE_CASE									= examples[:limit_length]
																											__SCREAMING_SNAKE_CASE									= glue_convert_examples_to_features(
																											    _A				,							_A				,							max_length=args.max_seq_length				,							label_list=_A				,							output_mode=self.output_mode				,							)
																											__SCREAMING_SNAKE_CASE									= 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		):







													'''simple docstring'''

													return len(self.features		)






						def __getitem__(   self				,							_A		):







													'''simple docstring'''

													return self.features[i]






						def 	_A     (   self		):







													'''simple docstring'''

													return self.label_list











import warnings
from contextlib import contextmanager

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

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Union[str, Any]      =  '''Wav2Vec2FeatureExtractor'''
						UpperCamelCase__      :			Union[str, Any]      =  '''AutoTokenizer'''






						def __init__(   self				,							_A				,							_A		):







													'''simple docstring'''

													super().__init__(_A				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.feature_extractor
													__SCREAMING_SNAKE_CASE									= False






						@classmethod
						def 	_A     (   cls				,							_A				,							**_A		):







													'''simple docstring'''

													try:
																				return super().from_pretrained(_A				,							**_A		)
													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: '				,							_A				,							)

																				__SCREAMING_SNAKE_CASE									= WavaVecaFeatureExtractor.from_pretrained(_A				,							**_A		)
																				__SCREAMING_SNAKE_CASE									= WavaVecaCTCTokenizer.from_pretrained(_A				,							**_A		)

																				return cls(feature_extractor=_A				,							tokenizer=_A		)






						def __call__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

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

													if "raw_speech" in kwargs:
																				warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'		)
																				__SCREAMING_SNAKE_CASE									= kwargs.pop('raw_speech'		)
													else:
																				__SCREAMING_SNAKE_CASE									= kwargs.pop('audio'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('sampling_rate'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('text'				,							_A		)
													if len(_A		) > 0:
																				__SCREAMING_SNAKE_CASE									= args[0]
																				__SCREAMING_SNAKE_CASE									= 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:
																				__SCREAMING_SNAKE_CASE									= self.feature_extractor(_A				,							*_A				,							sampling_rate=_A				,							**_A		)
													if text is not None:
																				__SCREAMING_SNAKE_CASE									= self.tokenizer(_A				,							**_A		)

													if text is None:
																				return inputs
													elif audio is None:
																				return encodings
													else:
																				__SCREAMING_SNAKE_CASE									= encodings['input_ids']
																				return inputs






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= kwargs.pop('input_features'				,							_A		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('labels'				,							_A		)
													if len(_A		) > 0:
																				__SCREAMING_SNAKE_CASE									= args[0]
																				__SCREAMING_SNAKE_CASE									= args[1:]

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

													if labels is None:
																				return input_features
													elif input_features is None:
																				return labels
													else:
																				__SCREAMING_SNAKE_CASE									= labels['input_ids']
																				return input_features






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							*_A				,							**_A		):







													'''simple docstring'''

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






						@contextmanager
						def 	_A     (   self		):







													'''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.'		)
													__SCREAMING_SNAKE_CASE									= True
													__SCREAMING_SNAKE_CASE									= self.tokenizer
													yield
													__SCREAMING_SNAKE_CASE									= self.feature_extractor
													__SCREAMING_SNAKE_CASE									= False






