
'''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

__lowerCAmelCase         =    collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__lowerCAmelCase         =    """https://storage.googleapis.com/cvdf-datasets/mnist/"""





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   numpy.dtype(numpy.uintaa ).newbyteorder('>' )
   return numpy.frombuffer(bytestream.read(4 ) ,	dtype=__SCREAMING_SNAKE_CASE )[0]





@deprecated(__SCREAMING_SNAKE_CASE ,	'Please use tf.data to implement this functionality.' )
def 					UpperCAmelCase_		(__a      :    Dict ):






   """simple docstring"""

   print('Extracting' ,	f.name )
   with gzip.GzipFile(fileobj=__SCREAMING_SNAKE_CASE ) as bytestream:
      _a				:	str			=   _readaa(__SCREAMING_SNAKE_CASE )
      if magic != 2_0_5_1:
         raise ValueError(
             'Invalid magic number %d in MNIST image file: %s' % (magic, f.name) )
      _a				:	List[str]			=   _readaa(__SCREAMING_SNAKE_CASE )
      _a				:	int			=   _readaa(__SCREAMING_SNAKE_CASE )
      _a				:	Tuple			=   _readaa(__SCREAMING_SNAKE_CASE )
      _a				:	Tuple			=   bytestream.read(rows * cols * num_images )
      _a				:	List[str]			=   numpy.frombuffer(__SCREAMING_SNAKE_CASE ,	dtype=numpy.uinta )
      _a				:	List[str]			=   data.reshape(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	1 )
      return data





@deprecated(__SCREAMING_SNAKE_CASE ,	'Please use tf.one_hot on tensors.' )
def 					UpperCAmelCase_		(__a      :    Union[str, Any] ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   labels_dense.shape[0]
   _a				:	Optional[int]			=   numpy.arange(__SCREAMING_SNAKE_CASE ) * num_classes
   _a				:	Optional[int]			=   numpy.zeros((num_labels, num_classes) )
   _a				:	Union[str, Any]			=   1
   return labels_one_hot





@deprecated(__SCREAMING_SNAKE_CASE ,	'Please use tf.data to implement this functionality.' )
def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Tuple=False ,	__a      :    Any=1_0 ):






   """simple docstring"""

   print('Extracting' ,	f.name )
   with gzip.GzipFile(fileobj=__SCREAMING_SNAKE_CASE ) as bytestream:
      _a				:	List[Any]			=   _readaa(__SCREAMING_SNAKE_CASE )
      if magic != 2_0_4_9:
         raise ValueError(
             'Invalid magic number %d in MNIST label file: %s' % (magic, f.name) )
      _a				:	str			=   _readaa(__SCREAMING_SNAKE_CASE )
      _a				:	Optional[Any]			=   bytestream.read(__SCREAMING_SNAKE_CASE )
      _a				:	int			=   numpy.frombuffer(__SCREAMING_SNAKE_CASE ,	dtype=numpy.uinta )
      if one_hot:
         return _dense_to_one_hot(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE )
      return labels







class UpperCAmelCase__	:





  """simple docstring"""

  @deprecated(
      UpperCamelCase__							,'Please use alternatives such as official/mnist/_DataSet.py'
      ' from tensorflow/models.'							,)
  def __init__(		self      :     Optional[int]							,_a      :     Union[str, Any]							,_a      :     Dict							,_a      :     List[str]=False							,_a      :     List[str]=False							,_a      :     int=dtypes.floataa							,_a      :     int=True							,_a      :     int=None							,):


     '''simple docstring'''






     _a				:	str			=   random_seed.get_seed(UpperCamelCase__       )
     # If op level seed is not set, use whatever graph level seed is returned
     numpy.random.seed(seeda if seed is None else seeda       )
     _a				:	int			=   dtypes.as_dtype(UpperCamelCase__       ).base_dtype
     if dtype not in (dtypes.uinta, dtypes.floataa):
        raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype       )
     if fake_data:
        _a				:	Optional[int]			=   1_0000
        _a				:	List[str]			=   one_hot
     else:
        assert (
            images.shape[0] == labels.shape[0]
        ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
        _a				:	Optional[int]			=   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
           _a				:	Optional[int]			=   images.reshape(
               images.shape[0]							,images.shape[1] * images.shape[2]       )
        if dtype == dtypes.floataa:
           # Convert from [0, 255] -> [0.0, 1.0].
           _a				:	List[str]			=   images.astype(numpy.floataa       )
           _a				:	Optional[int]			=   numpy.multiply(UpperCamelCase__							,1.0 / 255.0       )
     _a				:	str			=   images
     _a				:	str			=   labels
     _a				:	List[Any]			=   0
     _a				:	Union[str, Any]			=   0


  @property
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     return self._images


  @property
  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self._labels


  @property
  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self._num_examples


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return self._epochs_completed


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     Dict							,_a      :     List[Any]=False							,_a      :     List[str]=True       ):


     '''simple docstring'''






     if fake_data:
        _a				:	int			=   [1] * 784
        _a				:	Optional[Any]			=   [1] + [0] * 9 if self.one_hot else 0
        return (
            [fake_image for _ in range(UpperCamelCase__       )],
            [fake_label for _ in range(UpperCamelCase__       )],
        )
     _a				:	List[str]			=   self._index_in_epoch
     # Shuffle for the first epoch
     if self._epochs_completed == 0 and start == 0 and shuffle:
        _a				:	Union[str, Any]			=   numpy.arange(self._num_examples       )
        numpy.random.shuffle(UpperCamelCase__       )
        _a				:	Tuple			=   self.images[perma]
        _a				:	Union[str, Any]			=   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
        _a				:	Optional[Any]			=   self._num_examples - start
        _a				:	Any			=   self._images[start : self._num_examples]
        _a				:	Optional[Any]			=   self._labels[start : self._num_examples]
        # Shuffle the data
        if shuffle:
           _a				:	Dict			=   numpy.arange(self._num_examples       )
           numpy.random.shuffle(UpperCamelCase__       )
           _a				:	Union[str, Any]			=   self.images[perm]
           _a				:	str			=   self.labels[perm]
        # Start next epoch
        _a				:	Union[str, Any]			=   0
        _a				:	Tuple			=   batch_size - rest_num_examples
        _a				:	str			=   self._index_in_epoch
        _a				:	str			=   self._images[start:end]
        _a				:	str			=   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
        _a				:	Optional[int]			=   self._index_in_epoch
        return self._images[start:end], self._labels[start:end]





@deprecated(__SCREAMING_SNAKE_CASE ,	'Please write your own downloading logic.' )
def 					UpperCAmelCase_		(__a      :    Optional[int] ,	__a      :    str ,	__a      :    Optional[int] ):






   """simple docstring"""

   if not gfile.Exists(__SCREAMING_SNAKE_CASE ):
      gfile.MakeDirs(__SCREAMING_SNAKE_CASE )
   _a				:	List[str]			=   os.path.join(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE )
   if not gfile.Exists(__SCREAMING_SNAKE_CASE ):
      urllib.request.urlretrieve(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE )  # noqa: S310
      with gfile.GFile(__SCREAMING_SNAKE_CASE ) as f:
         _a				:	List[str]			=   f.size()
      print('Successfully downloaded' ,	__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	'bytes.' )
   return filepath





@deprecated(
    __SCREAMING_SNAKE_CASE ,	'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')' )
def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    List[Any]=False ,	__a      :    Optional[int]=False ,	__a      :    Optional[int]=dtypes.floataa ,	__a      :    int=True ,	__a      :    int=5_0_0_0 ,	__a      :    Any=None ,	__a      :    Any=DEFAULT_SOURCE_URL ,	):






   """simple docstring"""

   if fake_data:

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

      _a				:	Tuple			=   fake()
      _a				:	Optional[int]			=   fake()
      _a				:	Any			=   fake()
      return _Datasets(train=__SCREAMING_SNAKE_CASE ,	validation=__SCREAMING_SNAKE_CASE ,	test=__SCREAMING_SNAKE_CASE )

   if not source_url:  # empty string check
      _a				:	str			=   DEFAULT_SOURCE_URL

   _a				:	List[Any]			=   "train-images-idx3-ubyte.gz"
   _a				:	List[Any]			=   "train-labels-idx1-ubyte.gz"
   _a				:	Optional[int]			=   "t10k-images-idx3-ubyte.gz"
   _a				:	List[Any]			=   "t10k-labels-idx1-ubyte.gz"

   _a				:	Union[str, Any]			=   _maybe_download(
       __SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	source_url + train_images_file )
   with gfile.Open(__SCREAMING_SNAKE_CASE ,	'rb' ) as f:
      _a				:	Any			=   _extract_images(__SCREAMING_SNAKE_CASE )

   _a				:	Optional[Any]			=   _maybe_download(
       __SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	source_url + train_labels_file )
   with gfile.Open(__SCREAMING_SNAKE_CASE ,	'rb' ) as f:
      _a				:	Optional[int]			=   _extract_labels(__SCREAMING_SNAKE_CASE ,	one_hot=__SCREAMING_SNAKE_CASE )

   _a				:	str			=   _maybe_download(
       __SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	source_url + test_images_file )
   with gfile.Open(__SCREAMING_SNAKE_CASE ,	'rb' ) as f:
      _a				:	Optional[int]			=   _extract_images(__SCREAMING_SNAKE_CASE )

   _a				:	Tuple			=   _maybe_download(
       __SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	source_url + test_labels_file )
   with gfile.Open(__SCREAMING_SNAKE_CASE ,	'rb' ) as f:
      _a				:	Optional[Any]			=   _extract_labels(__SCREAMING_SNAKE_CASE ,	one_hot=__SCREAMING_SNAKE_CASE )

   if not 0 <= validation_size <= len(__SCREAMING_SNAKE_CASE ):
      _a				:	List[Any]			=   (
          "Validation size should be between 0 and "
          f"""{len(__SCREAMING_SNAKE_CASE )}. Received: {validation_size}."""
      )
      raise ValueError(__SCREAMING_SNAKE_CASE )

   _a				:	Optional[Any]			=   train_images[:validation_size]
   _a				:	List[str]			=   train_labels[:validation_size]
   _a				:	str			=   train_images[validation_size:]
   _a				:	List[str]			=   train_labels[validation_size:]

   _a				:	List[str]			=   {"dtype": dtype, "reshape": reshape, "seed": seed}

   _a				:	Optional[int]			=   _DataSet(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE )
   _a				:	Tuple			=   _DataSet(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE )
   _a				:	Dict			=   _DataSet(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE )

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






"""simple docstring"""


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			__A						   =			None

__A						   =			logging.get_logger(__name__)
__A						   =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__A						   =			{
    "google/rembert": 256,
}

__A						   =			"▁"





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Any         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Tuple         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Dict         =  RemBertTokenizer

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Dict=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Optional[int]=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				int="[CLS]"     ,  UpperCamelCase__							:				Optional[Any]="[SEP]"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="[SEP]"     ,  UpperCamelCase__							:				int="<pad>"     ,  UpperCamelCase__							:				Any="[CLS]"     ,  UpperCamelCase__							:				str="[MASK]"     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else mask_token

						super().__init__(
						    UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Optional[int]          =			do_lower_case
						__lowerCAmelCase:       int          =			remove_space
						__lowerCAmelCase:       int          =			keep_accents
						__lowerCAmelCase:       str          =			vocab_file
						__lowerCAmelCase:       Tuple          =			False if not self.vocab_file else True

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[self.cls_token_id]
						if token_ids_a is None:
								return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None     ,  UpperCamelCase__							:				bool = False)->   List[int]:



						'''simple docstring'''


						if already_has_special_tokens:
								if token_ids_a is not None:
										raise ValueError(
										    "You should not supply a second sequence if the provided sequence of "
										    "ids is already formatted with special tokens for the model.")
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
								return [1] + ([0] * len(UpperCamelCase__)) + [1] + ([0] * len(UpperCamelCase__)) + [1]
						return [1] + ([0] * len(UpperCamelCase__)) + [1]

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Dict          =			[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) * [0] + len(token_ids_a + sep) * [1]




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						if not os.path.isdir(UpperCamelCase__):
								logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__))
								return
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(
						    UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

						if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__):
								copyfile(self.vocab_file     ,  UpperCamelCase__)

						return (out_vocab_file,)


