


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

SCREAMING_SNAKE_CASE							    =							collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def  a   (lowerCAmelCase__   ):
   __a					=  numpy.dtype(numpy.uintaa   ).newbyteorder(""">"""   )
   return numpy.frombuffer(bytestream.read(4   )		,				dtype=lowerCAmelCase__   )[0]




@deprecated(lowerCAmelCase__		,				"""Please use tf.data to implement this functionality."""   )
def  a   (lowerCAmelCase__   ):
   print("""Extracting"""		,				f.name   )
   with gzip.GzipFile(fileobj=lowerCAmelCase__   ) as bytestream:
      __a					=  _readaa(lowerCAmelCase__   )
      if magic != 2_051:
         raise ValueError(
             """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)   )
      __a					=  _readaa(lowerCAmelCase__   )
      __a					=  _readaa(lowerCAmelCase__   )
      __a					=  _readaa(lowerCAmelCase__   )
      __a					=  bytestream.read(rows * cols * num_images   )
      __a					=  numpy.frombuffer(lowerCAmelCase__		,				dtype=numpy.uinta   )
      __a					=  data.reshape(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				1   )
      return data




@deprecated(lowerCAmelCase__		,				"""Please use tf.one_hot on tensors."""   )
def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  labels_dense.shape[0]
   __a					=  numpy.arange(lowerCAmelCase__   ) * num_classes
   __a					=  numpy.zeros((num_labels, num_classes)   )
   __a					=  1
   return labels_one_hot




@deprecated(lowerCAmelCase__		,				"""Please use tf.data to implement this functionality."""   )
def  a   (lowerCAmelCase__		,				lowerCAmelCase__=False		,				lowerCAmelCase__=10   ):
   print("""Extracting"""		,				f.name   )
   with gzip.GzipFile(fileobj=lowerCAmelCase__   ) as bytestream:
      __a					=  _readaa(lowerCAmelCase__   )
      if magic != 2_049:
         raise ValueError(
             """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)   )
      __a					=  _readaa(lowerCAmelCase__   )
      __a					=  bytestream.read(lowerCAmelCase__   )
      __a					=  numpy.frombuffer(lowerCAmelCase__		,				dtype=numpy.uinta   )
      if one_hot:
         return _dense_to_one_hot(lowerCAmelCase__		,				lowerCAmelCase__   )
      return labels


class     __UpperCAmelCase							:


     """simple docstring"""


     @deprecated(
         __A  ,       """Please use alternatives such as official/mnist/_DataSet.py"""
         """ from tensorflow/models."""  ,       )
     def __init__(	self  ,       __A  ,       __A  ,       __A=False  ,       __A=False  ,       __A=dtypes.floataa  ,       __A=True  ,       __A=None  ,       ):
        __a	,    __a					=  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    )
        __a					=  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:
           __a					=  10000
           __a					=  one_hot
        else:
           assert (
               images.shape[0] == labels.shape[0]
           ), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
           __a					=  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					=  images.reshape(
                  images.shape[0]  ,       images.shape[1] * images.shape[2]    )
           if dtype == dtypes.floataa:
              # Convert from [0, 255] -> [0.0, 1.0].
              __a					=  images.astype(numpy.floataa    )
              __a					=  numpy.multiply(__A  ,       1.0 / 255.0    )
        __a					=  images
        __a					=  labels
        __a					=  0
        __a					=  0


     @property
     def 	snake_case_  (	self    ):
        return self._images


     @property
     def 	snake_case_  (	self    ):
        return self._labels


     @property
     def 	snake_case_  (	self    ):
        return self._num_examples


     @property
     def 	snake_case_  (	self    ):
        return self._epochs_completed


     def 	snake_case_  (	self  ,       __A  ,       __A=False  ,       __A=True    ):
        if fake_data:
           __a					=  [1] * 784
           __a					=  [1] + [0] * 9 if self.one_hot else 0
           return (
               [fake_image for _ in range(__A    )],
               [fake_label for _ in range(__A    )],
           )
        __a					=  self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
           __a					=  numpy.arange(self._num_examples    )
           numpy.random.shuffle(__A    )
           __a					=  self.images[perma]
           __a					=  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					=  self._num_examples - start
           __a					=  self._images[start : self._num_examples]
           __a					=  self._labels[start : self._num_examples]
           # Shuffle the data
           if shuffle:
              __a					=  numpy.arange(self._num_examples    )
              numpy.random.shuffle(__A    )
              __a					=  self.images[perm]
              __a					=  self.labels[perm]
           # Start next epoch
           __a					=  0
           __a					=  batch_size - rest_num_examples
           __a					=  self._index_in_epoch
           __a					=  self._images[start:end]
           __a					=  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					=  self._index_in_epoch
           return self._images[start:end], self._labels[start:end]







@deprecated(lowerCAmelCase__		,				"""Please write your own downloading logic."""   )
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   if not gfile.Exists(lowerCAmelCase__   ):
      gfile.MakeDirs(lowerCAmelCase__   )
   __a					=  os.path.join(lowerCAmelCase__		,				lowerCAmelCase__   )
   if not gfile.Exists(lowerCAmelCase__   ):
      urllib.request.urlretrieve(lowerCAmelCase__		,				lowerCAmelCase__   )  # noqa: S310
      with gfile.GFile(lowerCAmelCase__   ) as f:
         __a					=  f.size()
      print("""Successfully downloaded"""		,				lowerCAmelCase__		,				lowerCAmelCase__		,				"""bytes."""   )
   return filepath




@deprecated(
    lowerCAmelCase__		,				"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""   )
def  a   (lowerCAmelCase__		,				lowerCAmelCase__=False		,				lowerCAmelCase__=False		,				lowerCAmelCase__=dtypes.floataa		,				lowerCAmelCase__=True		,				lowerCAmelCase__=5_000		,				lowerCAmelCase__=None		,				lowerCAmelCase__=DEFAULT_SOURCE_URL		,				):
   if fake_data:

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

      __a					=  fake()
      __a					=  fake()
      __a					=  fake()
      return _Datasets(train=lowerCAmelCase__		,				validation=lowerCAmelCase__		,				test=lowerCAmelCase__   )

   if not source_url:  # empty string check
      __a					=  DEFAULT_SOURCE_URL

   __a					=  """train-images-idx3-ubyte.gz"""
   __a					=  """train-labels-idx1-ubyte.gz"""
   __a					=  """t10k-images-idx3-ubyte.gz"""
   __a					=  """t10k-labels-idx1-ubyte.gz"""

   __a					=  _maybe_download(
       lowerCAmelCase__		,				lowerCAmelCase__		,				source_url + train_images_file   )
   with gfile.Open(lowerCAmelCase__		,				"""rb"""   ) as f:
      __a					=  _extract_images(lowerCAmelCase__   )

   __a					=  _maybe_download(
       lowerCAmelCase__		,				lowerCAmelCase__		,				source_url + train_labels_file   )
   with gfile.Open(lowerCAmelCase__		,				"""rb"""   ) as f:
      __a					=  _extract_labels(lowerCAmelCase__		,				one_hot=lowerCAmelCase__   )

   __a					=  _maybe_download(
       lowerCAmelCase__		,				lowerCAmelCase__		,				source_url + test_images_file   )
   with gfile.Open(lowerCAmelCase__		,				"""rb"""   ) as f:
      __a					=  _extract_images(lowerCAmelCase__   )

   __a					=  _maybe_download(
       lowerCAmelCase__		,				lowerCAmelCase__		,				source_url + test_labels_file   )
   with gfile.Open(lowerCAmelCase__		,				"""rb"""   ) as f:
      __a					=  _extract_labels(lowerCAmelCase__		,				one_hot=lowerCAmelCase__   )

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

   __a					=  train_images[:validation_size]
   __a					=  train_labels[:validation_size]
   __a					=  train_images[validation_size:]
   __a					=  train_labels[validation_size:]

   __a					=  {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

   __a					=  _DataSet(lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__   )
   __a					=  _DataSet(lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__   )
   __a					=  _DataSet(lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__   )

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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
    'tokenizer_file': {
        'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
    },
}

SCREAMING_SNAKE_CASE							    =							{'mobilebert-uncased': 5_1_2}


SCREAMING_SNAKE_CASE							    =							{}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase   =      MobileBertTokenizer


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=True  ,       __A="[UNK]"  ,       __A="[SEP]"  ,       __A="[PAD]"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A=True  ,       __A=None  ,       **__A  ,       ):
        super().__init__(
            __A  ,       tokenizer_file=__A  ,       do_lower_case=__A  ,       unk_token=__A  ,       sep_token=__A  ,       pad_token=__A  ,       cls_token=__A  ,       mask_token=__A  ,       tokenize_chinese_chars=__A  ,       strip_accents=__A  ,       **__A  ,       )

        __a					=  json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
        if (
            normalizer_state.get("""lowercase"""  ,       __A    ) != do_lower_case
            or normalizer_state.get("""strip_accents"""  ,       __A    ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""  ,       __A    ) != tokenize_chinese_chars
        ):
           __a					=  getattr(__A  ,       normalizer_state.pop("""type"""    )    )
           __a					=  do_lower_case
           __a					=  strip_accents
           __a					=  tokenize_chinese_chars
           __a					=  normalizer_class(**__A    )

        __a					=  do_lower_case


     def 	snake_case_  (	self  ,       __A  ,       __A=None    ):
        __a					=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
           output += token_ids_a + [self.sep_token_id]

        return output


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [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  ,       __A  ,       __A = None    ):
        __a					=  self._tokenizer.model.save(__A  ,       name=__A    )
        return tuple(__A    )



