
'''simple docstring'''


import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

snake_case_ :  List[Any]     =					collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
snake_case_ :  Optional[int]     =					'https://storage.googleapis.com/cvdf-datasets/mnist/'



def  __snake_case     (    _UpperCAmelCase					:       Dict):
   UpperCamelCase       =  numpy.dtype(numpy.uintaa).newbyteorder('''>''')
   return numpy.frombuffer(bytestream.read(4),      dtype=_UpperCAmelCase)[0]



@deprecated(_UpperCAmelCase,      '''Please use tf.data to implement this functionality.''')
def  __snake_case     (    _UpperCAmelCase					:       Optional[int]):
   print('''Extracting''',      f.name)
   with gzip.GzipFile(fileobj=_UpperCAmelCase) as bytestream:
      UpperCamelCase       =  _readaa(_UpperCAmelCase)
      if magic != 2051:
         raise ValueError(
             '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name))
      UpperCamelCase       =  _readaa(_UpperCAmelCase)
      UpperCamelCase       =  _readaa(_UpperCAmelCase)
      UpperCamelCase       =  _readaa(_UpperCAmelCase)
      UpperCamelCase       =  bytestream.read(rows * cols * num_images)
      UpperCamelCase       =  numpy.frombuffer(_UpperCAmelCase,      dtype=numpy.uinta)
      UpperCamelCase       =  data.reshape(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      1)
      return data



@deprecated(_UpperCAmelCase,      '''Please use tf.one_hot on tensors.''')
def  __snake_case     (    _UpperCAmelCase					:       Any,      _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  labels_dense.shape[0]
   UpperCamelCase       =  numpy.arange(_UpperCAmelCase) * num_classes
   UpperCamelCase       =  numpy.zeros((num_labels, num_classes))
   UpperCamelCase       =  1
   return labels_one_hot



@deprecated(_UpperCAmelCase,      '''Please use tf.data to implement this functionality.''')
def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Union[str, Any]=False,      _UpperCAmelCase					:       Optional[int]=10):
   print('''Extracting''',      f.name)
   with gzip.GzipFile(fileobj=_UpperCAmelCase) as bytestream:
      UpperCamelCase       =  _readaa(_UpperCAmelCase)
      if magic != 2049:
         raise ValueError(
             '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name))
      UpperCamelCase       =  _readaa(_UpperCAmelCase)
      UpperCamelCase       =  bytestream.read(_UpperCAmelCase)
      UpperCamelCase       =  numpy.frombuffer(_UpperCAmelCase,      dtype=numpy.uinta)
      if one_hot:
         return _dense_to_one_hot(_UpperCAmelCase,      _UpperCAmelCase)
      return labels


class 						lowercase__	:






   '''simple docstring'''







   @deprecated(
       lowerCamelCase__ ,   '''Please use alternatives such as official/mnist/_DataSet.py'''
       ''' from tensorflow/models.''' ,   )
   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   lowerCamelCase__=dtypes.floataa ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   ):

      '''simple docstring'''







      UpperCamelCase     , UpperCamelCase       =  random_seed.get_seed(lowerCamelCase__   )
      # If op level seed is not set, use whatever graph level seed is returned
      numpy.random.seed(seeda if seed is None else seeda   )
      UpperCamelCase       =  dtypes.as_dtype(lowerCamelCase__   ).base_dtype
      if dtype not in (dtypes.uinta, dtypes.floataa):
         raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype   )
      if fake_data:
         UpperCamelCase       =  1_0_0_0_0
         UpperCamelCase       =  one_hot
      else:
         assert (
             images.shape[0] == labels.shape[0]
         ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
         UpperCamelCase       =  images.shape[0]

         # Convert shape from [num examples, rows, columns, depth]
         # to [num examples, rows*columns] (assuming depth == 1)
         if reshape:
            assert images.shape[3] == 1
            UpperCamelCase       =  images.reshape(
                images.shape[0] ,   images.shape[1] * images.shape[2]   )
         if dtype == dtypes.floataa:
            # Convert from [0, 255] -> [0.0, 1.0].
            UpperCamelCase       =  images.astype(numpy.floataa   )
            UpperCamelCase       =  numpy.multiply(lowerCamelCase__ ,   1.0 / 255.0   )
      UpperCamelCase       =  images
      UpperCamelCase       =  labels
      UpperCamelCase       =  0
      UpperCamelCase       =  0







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self._images







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self._labels







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self._num_examples







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self._epochs_completed







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=False ,   lowerCamelCase__=True   ):

      '''simple docstring'''







      if fake_data:
         UpperCamelCase       =  [1] * 7_8_4
         UpperCamelCase       =  [1] + [0] * 9 if self.one_hot else 0
         return (
             [fake_image for _ in range(lowerCamelCase__   )],
             [fake_label for _ in range(lowerCamelCase__   )],
         )
      UpperCamelCase       =  self._index_in_epoch
      # Shuffle for the first epoch
      if self._epochs_completed == 0 and start == 0 and shuffle:
         UpperCamelCase       =  numpy.arange(self._num_examples   )
         numpy.random.shuffle(lowerCamelCase__   )
         UpperCamelCase       =  self.images[perma]
         UpperCamelCase       =  self.labels[perma]
      # Go to the next epoch
      if start + batch_size > self._num_examples:
         # Finished epoch
         self._epochs_completed += 1
         # Get the rest examples in this epoch
         UpperCamelCase       =  self._num_examples - start
         UpperCamelCase       =  self._images[start : self._num_examples]
         UpperCamelCase       =  self._labels[start : self._num_examples]
         # Shuffle the data
         if shuffle:
            UpperCamelCase       =  numpy.arange(self._num_examples   )
            numpy.random.shuffle(lowerCamelCase__   )
            UpperCamelCase       =  self.images[perm]
            UpperCamelCase       =  self.labels[perm]
         # Start next epoch
         UpperCamelCase       =  0
         UpperCamelCase       =  batch_size - rest_num_examples
         UpperCamelCase       =  self._index_in_epoch
         UpperCamelCase       =  self._images[start:end]
         UpperCamelCase       =  self._labels[start:end]
         return (
             numpy.concatenate((images_rest_part, images_new_part) ,   axis=0   ),
             numpy.concatenate((labels_rest_part, labels_new_part) ,   axis=0   ),
         )
      else:
         self._index_in_epoch += batch_size
         UpperCamelCase       =  self._index_in_epoch
         return self._images[start:end], self._labels[start:end]




@deprecated(_UpperCAmelCase,      '''Please write your own downloading logic.''')
def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Union[str, Any]):
   if not gfile.Exists(_UpperCAmelCase):
      gfile.MakeDirs(_UpperCAmelCase)
   UpperCamelCase       =  os.path.join(_UpperCAmelCase,      _UpperCAmelCase)
   if not gfile.Exists(_UpperCAmelCase):
      urllib.request.urlretrieve(_UpperCAmelCase,      _UpperCAmelCase)  # noqa: S310
      with gfile.GFile(_UpperCAmelCase) as f:
         UpperCamelCase       =  f.size()
      print('''Successfully downloaded''',      _UpperCAmelCase,      _UpperCAmelCase,      '''bytes.''')
   return filepath



@deprecated(
    _UpperCAmelCase,      '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''')
def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Optional[int]=False,      _UpperCAmelCase					:       Tuple=False,      _UpperCAmelCase					:       List[Any]=dtypes.floataa,      _UpperCAmelCase					:       Optional[int]=True,      _UpperCAmelCase					:       int=5000,      _UpperCAmelCase					:       Optional[int]=None,      _UpperCAmelCase					:       int=DEFAULT_SOURCE_URL,      ):
   if fake_data:

      def fake():
         return _DataSet(
             [],      [],      fake_data=_UpperCAmelCase,      one_hot=_UpperCAmelCase,      dtype=_UpperCAmelCase,      seed=_UpperCAmelCase)

      UpperCamelCase       =  fake()
      UpperCamelCase       =  fake()
      UpperCamelCase       =  fake()
      return _Datasets(train=_UpperCAmelCase,      validation=_UpperCAmelCase,      test=_UpperCAmelCase)

   if not source_url:  # empty string check
      UpperCamelCase       =  DEFAULT_SOURCE_URL

   UpperCamelCase       =  '''train-images-idx3-ubyte.gz'''
   UpperCamelCase       =  '''train-labels-idx1-ubyte.gz'''
   UpperCamelCase       =  '''t10k-images-idx3-ubyte.gz'''
   UpperCamelCase       =  '''t10k-labels-idx1-ubyte.gz'''

   UpperCamelCase       =  _maybe_download(
       _UpperCAmelCase,      _UpperCAmelCase,      source_url + train_images_file)
   with gfile.Open(_UpperCAmelCase,      '''rb''') as f:
      UpperCamelCase       =  _extract_images(_UpperCAmelCase)

   UpperCamelCase       =  _maybe_download(
       _UpperCAmelCase,      _UpperCAmelCase,      source_url + train_labels_file)
   with gfile.Open(_UpperCAmelCase,      '''rb''') as f:
      UpperCamelCase       =  _extract_labels(_UpperCAmelCase,      one_hot=_UpperCAmelCase)

   UpperCamelCase       =  _maybe_download(
       _UpperCAmelCase,      _UpperCAmelCase,      source_url + test_images_file)
   with gfile.Open(_UpperCAmelCase,      '''rb''') as f:
      UpperCamelCase       =  _extract_images(_UpperCAmelCase)

   UpperCamelCase       =  _maybe_download(
       _UpperCAmelCase,      _UpperCAmelCase,      source_url + test_labels_file)
   with gfile.Open(_UpperCAmelCase,      '''rb''') as f:
      UpperCamelCase       =  _extract_labels(_UpperCAmelCase,      one_hot=_UpperCAmelCase)

   if not 0 <= validation_size <= len(_UpperCAmelCase):
      UpperCamelCase       =  (
          '''Validation size should be between 0 and '''
          f'{len(_UpperCAmelCase)}. Received: {validation_size}.'
      )
      raise ValueError(_UpperCAmelCase)

   UpperCamelCase       =  train_images[:validation_size]
   UpperCamelCase       =  train_labels[:validation_size]
   UpperCamelCase       =  train_images[validation_size:]
   UpperCamelCase       =  train_labels[validation_size:]

   UpperCamelCase       =  {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

   UpperCamelCase       =  _DataSet(_UpperCAmelCase,      _UpperCAmelCase,      **_UpperCAmelCase)
   UpperCamelCase       =  _DataSet(_UpperCAmelCase,      _UpperCAmelCase,      **_UpperCAmelCase)
   UpperCamelCase       =  _DataSet(_UpperCAmelCase,      _UpperCAmelCase,      **_UpperCAmelCase)

   return _Datasets(train=_UpperCAmelCase,      validation=_UpperCAmelCase,      test=_UpperCAmelCase)







'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

snake_case_ :  int     =					'▁'

snake_case_ :  str     =					{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

snake_case_ :  Optional[Any]     =					{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

snake_case_ :  Dict     =					{'vinai/bartpho-syllable': 1_024}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="<mask>" ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) else mask_token

      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase__ ,   eos_token=lowerCamelCase__ ,   unk_token=lowerCamelCase__ ,   sep_token=lowerCamelCase__ ,   cls_token=lowerCamelCase__ ,   pad_token=lowerCamelCase__ ,   mask_token=lowerCamelCase__ ,   sp_model_kwargs=self.sp_model_kwargs ,   **lowerCamelCase__ ,   )

      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  monolingual_vocab_file
      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(lowerCamelCase__   )   )

      # Load the reduced vocab

      # Keep order of special tokens for backward compatibility
      UpperCamelCase       =  {}
      UpperCamelCase       =  0
      for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
         if str(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
            UpperCamelCase       =  cnt
            cnt += 1
      with open(lowerCamelCase__ ,   '''r''' ,   encoding='''utf-8'''   ) as f:
         for line in f.readlines():
            UpperCamelCase       =  line.strip().split()[0]
            UpperCamelCase       =  len(self.fairseq_tokens_to_ids   )
      if str(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
         UpperCamelCase       =  len(self.fairseq_tokens_to_ids   )

      UpperCamelCase       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







   def __getstate__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      UpperCamelCase       =  self.sp_model.serialized_model_proto()
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''   ):
         UpperCamelCase       =  {}

      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]
      UpperCamelCase       =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=lowerCamelCase__ ,   token_ids_a=lowerCamelCase__ ,   already_has_special_tokens=lowerCamelCase__   )

      if token_ids_a is None:
         return [1] + ([0] * len(lowerCamelCase__   )) + [1]
      return [1] + ([0] * len(lowerCamelCase__   )) + [1, 1] + ([0] * len(lowerCamelCase__   )) + [1]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      UpperCamelCase       =  [self.sep_token_id]
      UpperCamelCase       =  [self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep   ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.fairseq_ids_to_tokens   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      else:
         return self.unk_token_id







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.fairseq_ids_to_tokens[index]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  ''''''.join(lowerCamelCase__   ).replace(lowerCamelCase__ ,   ''' '''   ).strip()
      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] ,   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(lowerCamelCase__ ,   '''wb'''   ) as fi:
            UpperCamelCase       =  self.sp_model.serialized_model_proto()
            fi.write(lowerCamelCase__   )

      if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
          lowerCamelCase__   ) and os.path.isfile(self.monolingual_vocab_file   ):
         copyfile(self.monolingual_vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.monolingual_vocab_file   ):
         with open(lowerCamelCase__ ,   '''w''' ,   encoding='''utf-8'''   ) as fp:
            for token in self.fairseq_tokens_to_ids:
               if token not in self.all_special_tokens:
                  fp.write(f'{str(lowerCamelCase__   )} \n'   )

      return out_vocab_file, out_monolingual_vocab_file





