





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

_A   :       Dict    					=    collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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



def 			_a		(       UpperCAmelCase	)							->							Dict:







   """simple docstring"""
   lowerCamelCase__  :		Tuple     =				numpy.dtype(numpy.uintaa	).newbyteorder('''>'''	)
   return numpy.frombuffer(bytestream.read(4	)     ,    dtype=_A	)[0]



@deprecated(_A     ,    '''Please use tf.data to implement this functionality.'''	)
def 			_a		(       UpperCAmelCase	)							->							List[Any]:







   """simple docstring"""
   print('''Extracting'''     ,    f.name	)
   with gzip.GzipFile(fileobj=_A	) as bytestream:
      lowerCamelCase__  :		Dict     =				_readaa(_A	)
      if magic != 2051:
         raise ValueError(
             '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)	)
      lowerCamelCase__  :		Tuple     =				_readaa(_A	)
      lowerCamelCase__  :		List[Any]     =				_readaa(_A	)
      lowerCamelCase__  :		Dict     =				_readaa(_A	)
      lowerCamelCase__  :		List[str]     =				bytestream.read(rows * cols * num_images	)
      lowerCamelCase__  :		List[str]     =				numpy.frombuffer(_A     ,    dtype=numpy.uinta	)
      lowerCamelCase__  :		Optional[int]     =				data.reshape(_A     ,    _A     ,    _A     ,    1	)
      return data



@deprecated(_A     ,    '''Please use tf.one_hot on tensors.'''	)
def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__  :		List[str]     =				labels_dense.shape[0]
   lowerCamelCase__  :		int     =				numpy.arange(_A	) * num_classes
   lowerCamelCase__  :		int     =				numpy.zeros((num_labels, num_classes)	)
   lowerCamelCase__  :		List[str]     =				1
   return labels_one_hot



@deprecated(_A     ,    '''Please use tf.data to implement this functionality.'''	)
def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase=False     ,    UpperCAmelCase=10	)							->							str:







   """simple docstring"""
   print('''Extracting'''     ,    f.name	)
   with gzip.GzipFile(fileobj=_A	) as bytestream:
      lowerCamelCase__  :		List[Any]     =				_readaa(_A	)
      if magic != 2049:
         raise ValueError(
             '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)	)
      lowerCamelCase__  :		int     =				_readaa(_A	)
      lowerCamelCase__  :		List[Any]     =				bytestream.read(_A	)
      lowerCamelCase__  :		Optional[int]     =				numpy.frombuffer(_A     ,    dtype=numpy.uinta	)
      if one_hot:
         return _dense_to_one_hot(_A     ,    _A	)
      return labels


class   __SCREAMING_SNAKE_CASE     :







       @deprecated(
           A						,   '''Please use alternatives such as official/mnist/_DataSet.py'''
           ''' from tensorflow/models.'''						,   )
       def __init__( self	:      List[str]						,   A	:      Any						,   A	:      List[str]						,   A	:      List[Any]=False						,   A	:      Dict=False						,   A	:      List[Any]=dtypes.floataa						,   A	:      Dict=True						,   A	:      Tuple=None						,   )  ->List[Any]:
          lowerCamelCase__  :		str     =				random_seed.get_seed(A     )
          # If op level seed is not set, use whatever graph level seed is returned
          numpy.random.seed(seeda if seed is None else seeda     )
          lowerCamelCase__  :		int     =				dtypes.as_dtype(A     ).base_dtype
          if dtype not in (dtypes.uinta, dtypes.floataa):
             raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype     )
          if fake_data:
             lowerCamelCase__  :		int     =				1_0_0_0_0
             lowerCamelCase__  :		Any     =				one_hot
          else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), F"images.shape: {images.shape} labels.shape: {labels.shape}"
             lowerCamelCase__  :		Union[str, Any]     =				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
                lowerCamelCase__  :		Tuple     =				images.reshape(
                    images.shape[0]						,   images.shape[1] * images.shape[2]     )
             if dtype == dtypes.floataa:
                # Convert from [0, 255] -> [0.0, 1.0].
                lowerCamelCase__  :		Tuple     =				images.astype(numpy.floataa     )
                lowerCamelCase__  :		Dict     =				numpy.multiply(A						,   1.0 / 2_55.0     )
          lowerCamelCase__  :		List[Any]     =				images
          lowerCamelCase__  :		Dict     =				labels
          lowerCamelCase__  :		Union[str, Any]     =				0
          lowerCamelCase__  :		str     =				0







       @property
       def 		__lowerCamelCase       ( self	:      Tuple     )  ->List[Any]:
          return self._images







       @property
       def 		__lowerCamelCase       ( self	:      Dict     )  ->Optional[int]:
          return self._labels







       @property
       def 		__lowerCamelCase       ( self	:      Any     )  ->Tuple:
          return self._num_examples







       @property
       def 		__lowerCamelCase       ( self	:      Tuple     )  ->Optional[Any]:
          return self._epochs_completed







       def 		__lowerCamelCase       ( self	:      Optional[Any]						,   A	:      Union[str, Any]						,   A	:      Union[str, Any]=False						,   A	:      Optional[int]=True     )  ->Dict:
          if fake_data:
             lowerCamelCase__  :		List[str]     =				[1] * 7_8_4
             lowerCamelCase__  :		Union[str, Any]     =				[1] + [0] * 9 if self.one_hot else 0
             return (
                 [fake_image for _ in range(A     )],
                 [fake_label for _ in range(A     )],
             )
          lowerCamelCase__  :		Any     =				self._index_in_epoch
          # Shuffle for the first epoch
          if self._epochs_completed == 0 and start == 0 and shuffle:
             lowerCamelCase__  :		Union[str, Any]     =				numpy.arange(self._num_examples     )
             numpy.random.shuffle(A     )
             lowerCamelCase__  :		Optional[int]     =				self.images[perma]
             lowerCamelCase__  :		Tuple     =				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
             lowerCamelCase__  :		List[Any]     =				self._num_examples - start
             lowerCamelCase__  :		List[Any]     =				self._images[start : self._num_examples]
             lowerCamelCase__  :		Any     =				self._labels[start : self._num_examples]
             # Shuffle the data
             if shuffle:
                lowerCamelCase__  :		int     =				numpy.arange(self._num_examples     )
                numpy.random.shuffle(A     )
                lowerCamelCase__  :		Tuple     =				self.images[perm]
                lowerCamelCase__  :		Optional[int]     =				self.labels[perm]
             # Start next epoch
             lowerCamelCase__  :		Optional[int]     =				0
             lowerCamelCase__  :		Any     =				batch_size - rest_num_examples
             lowerCamelCase__  :		int     =				self._index_in_epoch
             lowerCamelCase__  :		Dict     =				self._images[start:end]
             lowerCamelCase__  :		Dict     =				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
             lowerCamelCase__  :		str     =				self._index_in_epoch
             return self._images[start:end], self._labels[start:end]



@deprecated(_A     ,    '''Please write your own downloading logic.'''	)
def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[Any]:







   """simple docstring"""
   if not gfile.Exists(_A	):
      gfile.MakeDirs(_A	)
   lowerCamelCase__  :		int     =				os.path.join(_A     ,    _A	)
   if not gfile.Exists(_A	):
      urllib.request.urlretrieve(_A     ,    _A	)  # noqa: S310
      with gfile.GFile(_A	) as f:
         lowerCamelCase__  :		Union[str, Any]     =				f.size()
      print('''Successfully downloaded'''     ,    _A     ,    _A     ,    '''bytes.'''	)
   return filepath




@deprecated(
    _A     ,    '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''	)
def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase=False     ,    UpperCAmelCase=False     ,    UpperCAmelCase=dtypes.floataa     ,    UpperCAmelCase=True     ,    UpperCAmelCase=5000     ,    UpperCAmelCase=None     ,    UpperCAmelCase=DEFAULT_SOURCE_URL     ,    )							->							Tuple:







   """simple docstring"""
   if fake_data:

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

      lowerCamelCase__  :		Tuple     =				fake()
      lowerCamelCase__  :		int     =				fake()
      lowerCamelCase__  :		str     =				fake()
      return _Datasets(train=_A     ,    validation=_A     ,    test=_A	)

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

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

   lowerCamelCase__  :		Dict     =				_maybe_download(
       _A     ,    _A     ,    source_url + train_images_file	)
   with gfile.Open(_A     ,    '''rb'''	) as f:
      lowerCamelCase__  :		List[Any]     =				_extract_images(_A	)

   lowerCamelCase__  :		Dict     =				_maybe_download(
       _A     ,    _A     ,    source_url + train_labels_file	)
   with gfile.Open(_A     ,    '''rb'''	) as f:
      lowerCamelCase__  :		int     =				_extract_labels(_A     ,    one_hot=_A	)

   lowerCamelCase__  :		Tuple     =				_maybe_download(
       _A     ,    _A     ,    source_url + test_images_file	)
   with gfile.Open(_A     ,    '''rb'''	) as f:
      lowerCamelCase__  :		int     =				_extract_images(_A	)

   lowerCamelCase__  :		Dict     =				_maybe_download(
       _A     ,    _A     ,    source_url + test_labels_file	)
   with gfile.Open(_A     ,    '''rb'''	) as f:
      lowerCamelCase__  :		List[str]     =				_extract_labels(_A     ,    one_hot=_A	)

   if not 0 <= validation_size <= len(_A	):
      lowerCamelCase__  :		Union[str, Any]     =				(
          """Validation size should be between 0 and """
          f"{len(_A	)}. Received: {validation_size}."
      )
      raise ValueError(_A	)

   lowerCamelCase__  :		int     =				train_images[:validation_size]
   lowerCamelCase__  :		Dict     =				train_labels[:validation_size]
   lowerCamelCase__  :		List[str]     =				train_images[validation_size:]
   lowerCamelCase__  :		int     =				train_labels[validation_size:]

   lowerCamelCase__  :		str     =				{"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

   lowerCamelCase__  :		List[str]     =				_DataSet(_A     ,    _A     ,    **_A	)
   lowerCamelCase__  :		Optional[int]     =				_DataSet(_A     ,    _A     ,    **_A	)
   lowerCamelCase__  :		Dict     =				_DataSet(_A     ,    _A     ,    **_A	)

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











"""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_fnet import FNetTokenizer
else:
				_lowerCAmelCase            =	None

_lowerCAmelCase            =	logging.get_logger(__name__)
_lowerCAmelCase            =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase            =	{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

_lowerCAmelCase            =	{
    'google/fnet-base': 5_12,
    'google/fnet-large': 5_12,
}

_lowerCAmelCase            =	'▁'


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Dict												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		["""input_ids""", """token_type_ids"""]
						_SCREAMING_SNAKE_CASE  : Union[str, Any]												=		FNetTokenizer






						def __init__(		self  :Optional[Any]			, __magic_name__  :Union[str, Any]=None			, __magic_name__  :Any=None			, __magic_name__  :List[str]=False			, __magic_name__  :Any=True			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]="<unk>"			, __magic_name__  :List[Any]="[SEP]"			, __magic_name__  :Union[str, Any]="<pad>"			, __magic_name__  :Tuple="[CLS]"			, __magic_name__  :Optional[int]="[MASK]"			, **__magic_name__  :Optional[int]			, )      ->Tuple:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase    :      Union[str, Any]       = (
										    AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__			, normalized=__magic_name__				)
										    if isinstance(__magic_name__			, __magic_name__				)
										    else mask_token
										)

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

										lowercase    :      Tuple       = do_lower_case
										lowercase    :      Tuple       = remove_space
										lowercase    :      List[str]       = keep_accents
										lowercase    :      Union[str, Any]       = vocab_file
										lowercase    :      int       = False if not self.vocab_file else True






						def      __snake_case      (		self  :Any			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      List[str]       = [self.sep_token_id]
										lowercase    :      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      __snake_case      (		self  :int			, __magic_name__  :List[int]			, __magic_name__  :Optional[List[int]] = None				)      ->List[int]:
										lowercase    :      Tuple       = [self.sep_token_id]
										lowercase    :      Union[str, Any]       = [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      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return
										lowercase    :      str       = os.path.join(
										    __magic_name__			, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

										return (out_vocab_file,)
