




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCAmelCase__    :							Union[str, Any]       		=     logging.getLogger(__name__)


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





  _lowercase:			Dict      =     argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'				)
  parser.add_argument(
      '--dataset_name'       ,  type=_A       ,  default='wikitext'       ,  help='Name of the training. Explore datasets at: hf.co/datasets.'       ,  )
  parser.add_argument(
      '--dataset_config'       ,  type=_A       ,  default='wikitext-103-raw-v1'       ,  help='Configuration name of the dataset.'				)
  parser.add_argument(
      '--batch_size'       ,  type=_A       ,  default=1_000       ,  help='Batch size during training.'       ,  )
  parser.add_argument(
      '--vocab_size'       ,  type=_A       ,  default=10_048       ,  help='Size of the desired vocabulary.'       ,  )
  parser.add_argument(
      '--limit'       ,  default=_A       ,  type=_A       ,  help='Limit the number of shards (used for debugging).'       ,  )
  parser.add_argument(
      '--export_to_hub'       ,  action='store_true'       ,  )

  _lowercase:			Optional[int]      =     parser.parse_args()
  return args


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[Any]:





  """simple docstring"""





  _lowercase:			Optional[Any]      =     datasets.load_dataset(args.dataset_name       ,  args.dataset_config       ,  split='train'				)

  if args.limit is not None:
    _lowercase:			str      =     min(len(_A				)       ,  args.limit				)
    _lowercase:			List[str]      =     dataset.select(range(_A				)				)
    logger.info(F'Limiting the dataset to {args.limit} entries.'				)

  def batch_iterator():
    for i in range(0       ,  len(_A				)       ,  args.batch_size				):
      yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
  _lowercase:			Tuple      =     Tokenizer(Unigram()				)
  _lowercase:			Tuple      =     normalizers.Sequence([normalizers.Replace('``'       ,  '"'				), normalizers.Replace('\'\''       ,  '"'				)]				)
  _lowercase:			Optional[int]      =     pre_tokenizers.Metaspace()

  # Prepare the trainer.
  _lowercase:			Union[str, Any]      =     UnigramTrainer(
      unk_token='<unk>'       ,  special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']       ,  vocab_size=args.vocab_size       ,  )

  logger.info('Training the tokenizer.'				)
  tokenizer.train_from_iterator(batch_iterator()       ,  trainer=_A				)
  logger.info('Tokenizer training complete!'				)

  _lowercase:			Dict      =     tokenizer.token_to_id('[CLS]'				)
  _lowercase:			Optional[Any]      =     tokenizer.token_to_id('[SEP]'				)
  _lowercase:			List[str]      =     processors.TemplateProcessing(
      single='[CLS]:0 $A:0 [SEP]:0'       ,  pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'       ,  special_tokens=[
          ('[CLS]', cls_token_id),
          ('[SEP]', sep_token_id),
      ]       ,  )
  _lowercase:			str      =     decoders.Metaspace()

  if args.export_to_hub:
    logger.info('Exporting the trained tokenzier to Hub.'				)
    _lowercase:			Optional[int]      =     AlbertTokenizerFast(tokenizer_object=_A				)
    new_tokenizer.push_to_hub('unigram-tokenizer-dataset'				)


if __name__ == "__main__":
   UpperCAmelCase__    :							List[str]       		=     parse_args()
   main(args)






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__       				=	logging.get_logger(__name__)
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						["input_values", "attention_mask"]



     def __init__(  self   :							Union[str, Any]      ,		lowercase__   :							str=1      ,		lowercase__   :							int=1_6_0_0_0      ,		lowercase__   :							Optional[int]=0.0      ,		lowercase__   :							List[Any]=False      ,		lowercase__   :							Dict=True      ,		**lowercase__   :							Any      ,		):
       super().__init__(feature_size=lowercase__      ,		sampling_rate=lowercase__      ,		padding_value=lowercase__      ,		**lowercase__       )
       a_		    =  return_attention_mask
       a_		    =  do_normalize



     @staticmethod
     def 				__magic_name__		(  lowercase__   :							List[np.ndarray]      ,		lowercase__   :							List[np.ndarray]      ,		lowercase__   :							float = 0.0       ):
       if attention_mask is not None:
         a_		    =  np.array(lowercase__      ,		np.intaa       )
         a_		    =  []

         for vector, length in zip(lowercase__      ,		attention_mask.sum(-1       )       ):
           a_		    =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7       )
           if length < normed_slice.shape[0]:
             a_		    =  padding_value

           normed_input_values.append(lowercase__       )
       else:
         a_		    =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7       ) for x in input_values]

       return normed_input_values



     def __call__(  self   :							Any      ,		lowercase__   :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,		lowercase__   :							Union[bool, str, PaddingStrategy] = False      ,		lowercase__   :							Optional[int] = None      ,		lowercase__   :							bool = False      ,		lowercase__   :							Optional[int] = None      ,		lowercase__   :							Optional[bool] = None      ,		lowercase__   :							Optional[Union[str, TensorType]] = None      ,		lowercase__   :							Optional[int] = None      ,		**lowercase__   :							List[str]      ,		):

       if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
           raise ValueError(
               f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
               f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
               f" {self.sampling_rate} and not {sampling_rate}."       )
       else:
         logger.warning(
             '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
             '''Failing to do so can result in silent errors that might be hard to debug.'''       )

       a_		    =  isinstance(lowercase__      ,		np.ndarray       ) and len(raw_speech.shape       ) > 1
       if is_batched_numpy and len(raw_speech.shape       ) > 2:
         raise ValueError(f"Only mono-channel audio is supported for input to {self}"       )
       a_		    =  is_batched_numpy or (
           isinstance(lowercase__      ,		(list, tuple)       ) and (isinstance(raw_speech[0]      ,		(np.ndarray, tuple, list)       ))
       )

       # always return batch
       if not is_batched:
         a_		    =  [raw_speech]

       # convert into correct format for padding
       a_		    =  BatchFeature({'''input_values''': raw_speech}       )

       a_		    =  self.pad(
           lowercase__      ,		padding=lowercase__      ,		max_length=lowercase__      ,		truncation=lowercase__      ,		pad_to_multiple_of=lowercase__      ,		return_attention_mask=lowercase__      ,		)

       # convert input values to correct format
       a_		    =  padded_inputs['''input_values''']
       if not isinstance(input_values[0]      ,		np.ndarray       ):
         a_		    =  [np.asarray(lowercase__      ,		dtype=np.floataa       ) for array in input_values]
       elif (
           not isinstance(lowercase__      ,		np.ndarray       )
           and isinstance(input_values[0]      ,		np.ndarray       )
           and input_values[0].dtype is np.dtype(np.floataa       )
       ):
         a_		    =  [array.astype(np.floataa       ) for array in input_values]
       elif isinstance(lowercase__      ,		np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
         a_		    =  input_values.astype(np.floataa       )

       # convert attention_mask to correct format
       a_		    =  padded_inputs.get('''attention_mask'''       )
       if attention_mask is not None:
         a_		    =  [np.asarray(lowercase__      ,		dtype=np.intaa       ) for array in attention_mask]

       # zero-mean and unit-variance normalization
       if self.do_normalize:
         a_		    =  (
             attention_mask
             if self._get_padding_strategies(lowercase__      ,		max_length=lowercase__       ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a_		    =  self.zero_mean_unit_var_norm(
             padded_inputs['''input_values''']      ,		attention_mask=lowercase__      ,		padding_value=self.padding_value       )

       if return_tensors is not None:
         a_		    =  padded_inputs.convert_to_tensors(lowercase__       )

       return padded_inputs
