


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

__UpperCamelCase       :       List[str]						       =				collections.namedtuple("_Datasets", ["train", "validation", "test"])

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   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  (				SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



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

@deprecated(_lowerCamelCase    , '''Please use tf.one_hot on tensors.'''     )
def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   labels_dense.shape[0]
 UpperCamelCase__					:    str          =   numpy.arange(_lowerCamelCase     ) * num_classes
 UpperCamelCase__					:    str          =   numpy.zeros((num_labels, num_classes)     )
 UpperCamelCase__					:    Tuple          =   1
 return labels_one_hot

@deprecated(_lowerCamelCase    , '''Please use tf.data to implement this functionality.'''     )
def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Optional[int]=False    , SCREAMING_SNAKE_CASE	:				Optional[int]=10     ):







 """simple docstring"""



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

class       __magic_name__	:


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   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__					:    Optional[Any]          =   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__					:    str          =   10000
       UpperCamelCase__					:    Optional[Any]          =   one_hot
      else:
       assert (
           images.shape[0] == labels.shape[0]
       ), F"images.shape: {images.shape} labels.shape: {labels.shape}"
       UpperCamelCase__					:    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
        UpperCamelCase__					:    Union[str, Any]          =   images.reshape(
            images.shape[0]							,	images.shape[1] * images.shape[2]			)
       if dtype == dtypes.floataa:
        # Convert from [0, 255] -> [0.0, 1.0].
        UpperCamelCase__					:    List[Any]          =   images.astype(numpy.floataa			)
        UpperCamelCase__					:    Optional[Any]          =   numpy.multiply(lowerCamelCase__							,	1.0 / 255.0			)
      UpperCamelCase__					:    Optional[Any]          =   images
      UpperCamelCase__					:    List[Any]          =   labels
      UpperCamelCase__					:    str          =   0
      UpperCamelCase__					:    Union[str, Any]          =   0


     @property
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Any:





      '''simple docstring'''






      return self._images


     @property
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Dict:





      '''simple docstring'''






      return self._labels


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      str:





      '''simple docstring'''






      return self._num_examples


     @property
     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      return self._epochs_completed


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Optional[int]=False							,	lowerCamelCase__							:     int=True			)  ->      Optional[int]:





      '''simple docstring'''






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



@deprecated(_lowerCamelCase    , '''Please write your own downloading logic.'''     )
def 			_a  (				SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Union[str, Any]     ):







 """simple docstring"""



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






@deprecated(
    _lowerCamelCase    , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''     )
def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				List[Any]=False    , SCREAMING_SNAKE_CASE	:				str=False    , SCREAMING_SNAKE_CASE	:				List[str]=dtypes.floataa    , SCREAMING_SNAKE_CASE	:				Any=True    , SCREAMING_SNAKE_CASE	:				Union[str, Any]=5000    , SCREAMING_SNAKE_CASE	:				str=None    , SCREAMING_SNAKE_CASE	:				Optional[int]=DEFAULT_SOURCE_URL    , ):







 """simple docstring"""



 if fake_data:

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

  UpperCamelCase__					:    Optional[int]          =   fake()
  UpperCamelCase__					:    Tuple          =   fake()
  UpperCamelCase__					:    List[str]          =   fake()
  return _Datasets(train=_lowerCamelCase    , validation=_lowerCamelCase    , test=_lowerCamelCase     )

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

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

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

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

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

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

 if not 0 <= validation_size <= len(_lowerCamelCase     ):
  UpperCamelCase__					:    str          =   (
      "Validation size should be between 0 and "
      F"{len(_lowerCamelCase     )}. Received: {validation_size}."
  )
  raise ValueError(_lowerCamelCase     )

 UpperCamelCase__					:    Any          =   train_images[:validation_size]
 UpperCamelCase__					:    Optional[Any]          =   train_labels[:validation_size]
 UpperCamelCase__					:    Optional[int]          =   train_images[validation_size:]
 UpperCamelCase__					:    Tuple          =   train_labels[validation_size:]

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

 UpperCamelCase__					:    Union[str, Any]          =   _DataSet(_lowerCamelCase    , _lowerCamelCase    , **_lowerCamelCase     )
 UpperCamelCase__					:    str          =   _DataSet(_lowerCamelCase    , _lowerCamelCase    , **_lowerCamelCase     )
 UpperCamelCase__					:    Optional[Any]          =   _DataSet(_lowerCamelCase    , _lowerCamelCase    , **_lowerCamelCase     )

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






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (modulus_power * alphabet_size) % modulus

 for i in range(0    , t_len - p_len + 1     ):
  if text_hash == p_hash and text[i : i + p_len] == pattern:
   return True
  if i == t_len - p_len:
   continue
  # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
  UpperCamelCase__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


