'''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

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

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

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Optional[int]     				=					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   (       SCREAMING_SNAKE_CASE_							):
     print('Extracting'  ,       f.name							)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE_							) as bytestream:
          lowercase_					:       Optional[int]     				=					_readaa(SCREAMING_SNAKE_CASE_							)
          if magic != 2_051:
               raise ValueError(
                   'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)							)
          lowercase_					:       Optional[int]     				=					_readaa(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       int     				=					_readaa(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Dict     				=					_readaa(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       str     				=					bytestream.read(rows * cols * num_images							)
          lowercase_					:       List[Any]     				=					numpy.frombuffer(SCREAMING_SNAKE_CASE_  ,       dtype=numpy.uinta							)
          lowercase_					:       int     				=					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   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[str]     				=					labels_dense.shape[0]
     lowercase_					:       Dict     				=					numpy.arange(SCREAMING_SNAKE_CASE_							) * num_classes
     lowercase_					:       int     				=					numpy.zeros((num_labels, num_classes)							)
     lowercase_					:       Optional[Any]     				=					1
     return labels_one_hot

@deprecated(SCREAMING_SNAKE_CASE_  ,       'Please use tf.data to implement this functionality.'							)
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False  ,       SCREAMING_SNAKE_CASE_=10							):
     print('Extracting'  ,       f.name							)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE_							) as bytestream:
          lowercase_					:       Any     				=					_readaa(SCREAMING_SNAKE_CASE_							)
          if magic != 2_049:
               raise ValueError(
                   'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)							)
          lowercase_					:       Dict     				=					_readaa(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       List[str]     				=					bytestream.read(SCREAMING_SNAKE_CASE_							)
          lowercase_					:       Union[str, Any]     				=					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(
       _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       ,				)    ->     Tuple:
        lowercase_				,lowercase_					:       Dict     				=					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				)
        lowercase_					:       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:
             lowercase_					:       int     				=					10_000
             lowercase_					:       List[Any]     				=					one_hot
        else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
             lowercase_					:       Dict     				=					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
                  lowercase_					:       Dict     				=					images.reshape(
                      images.shape[0]       ,				images.shape[1] * images.shape[2]				)
             if dtype == dtypes.floataa:
                  # Convert from [0, 255] -> [0.0, 1.0].
                  lowercase_					:       Tuple     				=					images.astype(numpy.floataa				)
                  lowercase_					:       List[str]     				=					numpy.multiply(_a       ,				1.0 / 2_55.0				)
        lowercase_					:       Optional[Any]     				=					images
        lowercase_					:       int     				=					labels
        lowercase_					:       str     				=					0
        lowercase_					:       Optional[Any]     				=					0
   @property
   def 		_lowerCamelCase (self				)    ->     Dict:
        return self._images
   @property
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return self._labels
   @property
   def 		_lowerCamelCase (self				)    ->     Dict:
        return self._num_examples
   @property
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return self._epochs_completed





   def 		_lowerCamelCase (self       ,				_a       ,				_a=False       ,				_a=True				)    ->     Union[str, Any]:
        if fake_data:
             lowercase_					:       List[Any]     				=					[1] * 784
             lowercase_					:       List[Any]     				=					[1] + [0] * 9 if self.one_hot else 0
             return (
                 [fake_image for _ in range(_a				)],
                 [fake_label for _ in range(_a				)],
             )
        lowercase_					:       List[str]     				=					self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
             lowercase_					:       int     				=					numpy.arange(self._num_examples				)
             numpy.random.shuffle(_a				)
             lowercase_					:       Optional[Any]     				=					self.images[perma]
             lowercase_					:       List[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
             lowercase_					:       Union[str, Any]     				=					self._num_examples - start
             lowercase_					:       Tuple     				=					self._images[start : self._num_examples]
             lowercase_					:       Optional[Any]     				=					self._labels[start : self._num_examples]
             # Shuffle the data
             if shuffle:
                  lowercase_					:       List[str]     				=					numpy.arange(self._num_examples				)
                  numpy.random.shuffle(_a				)
                  lowercase_					:       Dict     				=					self.images[perm]
                  lowercase_					:       Union[str, Any]     				=					self.labels[perm]
             # Start next epoch
             lowercase_					:       Union[str, Any]     				=					0
             lowercase_					:       Tuple     				=					batch_size - rest_num_examples
             lowercase_					:       List[str]     				=					self._index_in_epoch
             lowercase_					:       Union[str, Any]     				=					self._images[start:end]
             lowercase_					:       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
             lowercase_					:       Any     				=					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   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     if not gfile.Exists(SCREAMING_SNAKE_CASE_							):
          gfile.MakeDirs(SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Dict     				=					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:
               lowercase_					:       Optional[Any]     				=					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   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=False  ,       SCREAMING_SNAKE_CASE_=False  ,       SCREAMING_SNAKE_CASE_=dtypes.floataa  ,       SCREAMING_SNAKE_CASE_=True  ,       SCREAMING_SNAKE_CASE_=5_000  ,       SCREAMING_SNAKE_CASE_=None  ,       SCREAMING_SNAKE_CASE_=DEFAULT_SOURCE_URL  ,       ):
     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_							)

          lowercase_					:       int     				=					fake()
          lowercase_					:       Optional[Any]     				=					fake()
          lowercase_					:       Optional[Any]     				=					fake()
          return _Datasets(train=SCREAMING_SNAKE_CASE_  ,       validation=SCREAMING_SNAKE_CASE_  ,       test=SCREAMING_SNAKE_CASE_							)

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

     lowercase_					:       Union[str, Any]     				=					'train-images-idx3-ubyte.gz'
     lowercase_					:       Union[str, Any]     				=					'train-labels-idx1-ubyte.gz'
     lowercase_					:       Dict     				=					't10k-images-idx3-ubyte.gz'
     lowercase_					:       str     				=					't10k-labels-idx1-ubyte.gz'

     lowercase_					:       Optional[int]     				=					_maybe_download(
         SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       source_url + train_images_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as f:
          lowercase_					:       str     				=					_extract_images(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       str     				=					_maybe_download(
         SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       source_url + train_labels_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as f:
          lowercase_					:       Any     				=					_extract_labels(SCREAMING_SNAKE_CASE_  ,       one_hot=SCREAMING_SNAKE_CASE_							)

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

     lowercase_					:       Union[str, Any]     				=					_maybe_download(
         SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       source_url + test_labels_file							)
     with gfile.Open(SCREAMING_SNAKE_CASE_  ,       'rb'							) as f:
          lowercase_					:       Union[str, Any]     				=					_extract_labels(SCREAMING_SNAKE_CASE_  ,       one_hot=SCREAMING_SNAKE_CASE_							)

     if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE_							):
          lowercase_					:       List[str]     				=					(
              'Validation size should be between 0 and '
              f'''{len(SCREAMING_SNAKE_CASE_							)}. Received: {validation_size}.'''
          )
          raise ValueError(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Tuple     				=					train_images[:validation_size]
     lowercase_					:       int     				=					train_labels[:validation_size]
     lowercase_					:       int     				=					train_images[validation_size:]
     lowercase_					:       Union[str, Any]     				=					train_labels[validation_size:]

     lowercase_					:       List[Any]     				=					{'dtype': dtype, 'reshape': reshape, 'seed': seed}

     lowercase_					:       Tuple     				=					_DataSet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)
     lowercase_					:       str     				=					_DataSet(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       **SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Union[str, Any]     				=					_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
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       str     =    TransfoXLTokenizer
   A							:       Optional[int]     =    False
   A							:       List[str]     =    False
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        super().setUp()

        lowercase_					:       Any     				=					[
            '<unk>',
            '[CLS]',
            '[SEP]',
            'want',
            'unwanted',
            'wa',
            'un',
            'running',
            ',',
            'low',
            'l',
        ]
        lowercase_					:       Any     				=					os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['vocab_file']				)
        with open(self.vocab_file       ,				'w'       ,				encoding='utf-8'				) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)
   def 		_lowerCamelCase (self       ,				**_a				)    ->     Union[str, Any]:
        lowercase_					:       str     				=					True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,				**_a				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       int     				=					'<unk> UNwanted , running'
        lowercase_					:       int     				=					'<unk> unwanted, running'
        return input_text, output_text
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					TransfoXLTokenizer(vocab_file=self.vocab_file       ,				lower_case=_a				)

        lowercase_					:       Optional[Any]     				=					tokenizer.tokenize('<unk> UNwanted , running'				)
        self.assertListEqual(_a       ,				['<unk>', 'unwanted', ',', 'running']				)

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)       ,				[0, 4, 8, 7]				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        lowercase_					:       List[str]     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['hello', '!', 'how', 'are', 'you', '?']				)
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)

        self.assertListEqual(
            tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '				)       ,				['HeLLo', '!', 'how', 'Are', 'yoU', '?']				)
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					TransfoXLTokenizer(lower_case=_a				)
        lowercase_					:       int     				=					'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
        lowercase_					:       List[str]     				=					[
            'Hello',
            '(',
            'bracket',
            ')',
            'and',
            'side',
            '@-@',
            'scrolled',
            '[',
            'and',
            ']',
            'Henry',
            '\'s',
            '$',
            '5',
            '@,@',
            '000',
            'with',
            '3',
            '@.@',
            '34',
            'm',
            '.',
            'What',
            '\'s',
            'up',
            '!',
            '?',
        ]

        self.assertListEqual(tokenizer.tokenize(_a				)       ,				_a				)

        self.assertEqual(tokenizer.convert_tokens_to_string(_a				)       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     Any:
        lowercase_					:       Optional[int]     				=					self.get_tokenizer()
        lowercase_					:       str     				=					len(_a				)

        tokenizer.add_tokens(['new1', 'new2']				)
        tokenizer.move_added_token('new1'       ,				1				)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(_a				)       ,				original_len + 2				)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode('new1'				)       ,				[1]				)
        self.assertEqual(tokenizer.decode([1]				)       ,				'new1'				)




