





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A       :   List[str]					= logging.getLogger(__name__)







def   __lowerCamelCase   (				)						->							List[Any]:






		"""simple docstring"""

		A__     =  argparse.ArgumentParser(
		    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""       )
		parser.add_argument("""--file_path"""       ,	type=__a       ,	default="""data/dump.txt"""       ,	help="""The path to the data."""       )
		parser.add_argument("""--tokenizer_type"""       ,	type=__a       ,	default="""bert"""       ,	choices=["""bert""", """roberta""", """gpt2"""]       )
		parser.add_argument("""--tokenizer_name"""       ,	type=__a       ,	default="""bert-base-uncased"""       ,	help="""The tokenizer to use."""       )
		parser.add_argument("""--dump_file"""       ,	type=__a       ,	default="""data/dump"""       ,	help="""The dump file prefix."""       )
		A__     =  parser.parse_args()

		logger.info(F'Loading Tokenizer ({args.tokenizer_name})'       )
		if args.tokenizer_type == "bert":
				A__     =  BertTokenizer.from_pretrained(args.tokenizer_name       )
				A__     =  tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
				A__     =  tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
		elif args.tokenizer_type == "roberta":
				A__     =  RobertaTokenizer.from_pretrained(args.tokenizer_name       )
				A__     =  tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
				A__     =  tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
		elif args.tokenizer_type == "gpt2":
				A__     =  GPTaTokenizer.from_pretrained(args.tokenizer_name       )
				A__     =  tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
				A__     =  tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

		logger.info(F'Loading text from {args.file_path}'       )
		with open(args.file_path       ,	"""r"""       ,	encoding="""utf8"""       ) as fp:
				A__     =  fp.readlines()

		logger.info("""Start encoding"""       )
		logger.info(F'{len(__a       )} examples to process.'       )

		A__     =  []
		A__     =  0
		A__     =  1_0_0_0_0
		A__     =  time.time()
		for text in data:
				A__     =  F'{bos} {text.strip()} {sep}'
				A__     =  tokenizer.encode(__a       ,	add_special_tokens=__a       )
				rslt.append(__a       )

				iter += 1
				if iter % interval == 0:
						A__     =  time.time()
						logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'       )
						A__     =  time.time()
		logger.info("""Finished binarization"""       )
		logger.info(F'{len(__a       )} examples processed.'       )

		A__     =  F'{args.dump_file}.{args.tokenizer_name}.pickle'
		A__     =  tokenizer.vocab_size
		if vocab_size < (1 << 1_6):
				A__     =  [np.uintaa(__a       ) for d in rslt]
		else:
				A__     =  [np.intaa(__a       ) for d in rslt]
		random.shuffle(rslt_       )
		logger.info(F'Dump to {dp_file}'       )
		with open(__a       ,	"""wb"""       ) as handle:
				pickle.dump(rslt_       ,	__a       ,	protocol=pickle.HIGHEST_PROTOCOL       )


if __name__ == "__main__":
					main()










import warnings
from contextlib import contextmanager

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


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       '''Wav2Vec2FeatureExtractor'''
			__lowerCamelCase      :			str							     =       '''AutoTokenizer'''






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase				,      __lowerCAmelCase  )
					A__     =  self.feature_extractor
					A__     =  False






			@classmethod
			def   a_			(     cls  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      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				,      )

							A__     =  WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )
							A__     =  WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase				,      **__lowerCAmelCase  )

							return cls(feature_extractor=__lowerCAmelCase				,      tokenizer=__lowerCAmelCase  )






			def __call__(     self  :       Any				,      *__lowerCAmelCase  :       Union[str, Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[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."""  )
							A__     =  kwargs.pop("""raw_speech"""  )
					else:
							A__     =  kwargs.pop("""audio"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""sampling_rate"""				,      __lowerCAmelCase  )
					A__     =  kwargs.pop("""text"""				,      __lowerCAmelCase  )
					if len(__lowerCAmelCase  ) > 0:
							A__     =  args[0]
							A__     =  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:
							A__     =  self.feature_extractor(__lowerCAmelCase				,      *__lowerCAmelCase				,      sampling_rate=__lowerCAmelCase				,      **__lowerCAmelCase  )
					if text is not None:
							A__     =  self.tokenizer(__lowerCAmelCase				,      **__lowerCAmelCase  )

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






			def   a_			(     self  :       List[str]				,      *__lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Tuple  )      ->      List[str]:







					"""simple docstring"""






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

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

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

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






			def   a_			(     self  :       Optional[int]				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Dict:







					"""simple docstring"""






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






			def   a_			(     self  :       Any				,      *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






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







			@contextmanager
			def   a_			(     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."""  )
					A__     =  True
					A__     =  self.tokenizer
					yield
					A__     =  self.feature_extractor
					A__     =  False



