




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

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
   snake_case				:    Any           =	numpy.dtype(numpy.uintaa					).newbyteorder('''>'''					)
   return numpy.frombuffer(bytestream.read(4					)						,dtype=_lowercase					)[0]




@deprecated(_lowercase						,'''Please use tf.data to implement this functionality.'''					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Union[str, Any]:
   print('''Extracting'''						,f.name					)
   with gzip.GzipFile(fileobj=_lowercase					) as bytestream:
      snake_case				:    Dict           =	_readaa(_lowercase					)
      if magic != 2051:
         raise ValueError(
             '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)					)
      snake_case				:    Tuple           =	_readaa(_lowercase					)
      snake_case				:    Union[str, Any]           =	_readaa(_lowercase					)
      snake_case				:    Any           =	_readaa(_lowercase					)
      snake_case				:    int           =	bytestream.read(rows * cols * num_images					)
      snake_case				:    str           =	numpy.frombuffer(_lowercase						,dtype=numpy.uinta					)
      snake_case				:    Dict           =	data.reshape(_lowercase						,_lowercase						,_lowercase						,1					)
      return data




@deprecated(_lowercase						,'''Please use tf.one_hot on tensors.'''					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Any:
   snake_case				:    Optional[Any]           =	labels_dense.shape[0]
   snake_case				:    Any           =	numpy.arange(_lowercase					) * num_classes
   snake_case				:    List[Any]           =	numpy.zeros((num_labels, num_classes)					)
   snake_case				:    str           =	1
   return labels_one_hot




@deprecated(_lowercase						,'''Please use tf.data to implement this functionality.'''					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False						,lowercase=10					)  ->       Dict:
   print('''Extracting'''						,f.name					)
   with gzip.GzipFile(fileobj=_lowercase					) as bytestream:
      snake_case				:    Any           =	_readaa(_lowercase					)
      if magic != 2049:
         raise ValueError(
             '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)					)
      snake_case				:    Union[str, Any]           =	_readaa(_lowercase					)
      snake_case				:    Any           =	bytestream.read(_lowercase					)
      snake_case				:    Optional[int]           =	numpy.frombuffer(_lowercase						,dtype=numpy.uinta					)
      if one_hot:
         return _dense_to_one_hot(_lowercase						,_lowercase					)
      return labels




class 						__lowercase		:




      """simple docstring"""





      @deprecated(
          lowerCAmelCase__    ,				'''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:
         snake_case				:    str           =	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     )
         snake_case				:    Tuple           =	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:
            snake_case				:    Dict           =	1_0_0_0_0
            snake_case				:    int           =	one_hot
         else:
            assert (
                images.shape[0] == labels.shape[0]
            ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
            snake_case				:    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
               snake_case				:    List[Any]           =	images.reshape(
                   images.shape[0]    ,				images.shape[1] * images.shape[2]     )
            if dtype == dtypes.floataa:
               # Convert from [0, 255] -> [0.0, 1.0].
               snake_case				:    str           =	images.astype(numpy.floataa     )
               snake_case				:    Optional[Any]           =	numpy.multiply(lowerCAmelCase__    ,				1.0 / 2_55.0     )
         snake_case				:    Any           =	images
         snake_case				:    Optional[Any]           =	labels
         snake_case				:    int           =	0
         snake_case				:    List[str]           =	0

      @property
      def      UpperCAmelCase				(				self     )		->    Dict:
         return self._images

      @property
      def      UpperCAmelCase				(				self     )		->    Dict:
         return self._labels

      @property
      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         return self._num_examples

      @property
      def      UpperCAmelCase				(				self     )		->    List[str]:
         return self._epochs_completed

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




@deprecated(_lowercase						,'''Please write your own downloading logic.'''					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Any:






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




@deprecated(
    _lowercase						,'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''					)
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False						,lowercase=False						,lowercase=dtypes.floataa						,lowercase=True						,lowercase=5000						,lowercase=None						,lowercase=DEFAULT_SOURCE_URL						,)  ->       Union[str, Any]:






   if fake_data:

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

      snake_case				:    Union[str, Any]           =	fake()
      snake_case				:    Union[str, Any]           =	fake()
      snake_case				:    List[str]           =	fake()
      return _Datasets(train=_lowercase						,validation=_lowercase						,test=_lowercase					)

   if not source_url:  # empty string check
      snake_case				:    Any           =	DEFAULT_SOURCE_URL

   snake_case				:    Tuple           =	"train-images-idx3-ubyte.gz"
   snake_case				:    List[Any]           =	"train-labels-idx1-ubyte.gz"
   snake_case				:    Union[str, Any]           =	"t10k-images-idx3-ubyte.gz"
   snake_case				:    List[str]           =	"t10k-labels-idx1-ubyte.gz"

   snake_case				:    Dict           =	_maybe_download(
       _lowercase						,_lowercase						,source_url + train_images_file					)
   with gfile.Open(_lowercase						,'''rb'''					) as f:
      snake_case				:    Tuple           =	_extract_images(_lowercase					)

   snake_case				:    List[Any]           =	_maybe_download(
       _lowercase						,_lowercase						,source_url + train_labels_file					)
   with gfile.Open(_lowercase						,'''rb'''					) as f:
      snake_case				:    Tuple           =	_extract_labels(_lowercase						,one_hot=_lowercase					)

   snake_case				:    List[Any]           =	_maybe_download(
       _lowercase						,_lowercase						,source_url + test_images_file					)
   with gfile.Open(_lowercase						,'''rb'''					) as f:
      snake_case				:    Any           =	_extract_images(_lowercase					)

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

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

   snake_case				:    str           =	train_images[:validation_size]
   snake_case				:    Optional[int]           =	train_labels[:validation_size]
   snake_case				:    List[Any]           =	train_images[validation_size:]
   snake_case				:    Optional[int]           =	train_labels[validation_size:]

   snake_case				:    Optional[Any]           =	{"dtype": dtype, "reshape": reshape, "seed": seed}

   snake_case				:    Union[str, Any]           =	_DataSet(_lowercase						,_lowercase						,**_lowercase					)
   snake_case				:    Dict           =	_DataSet(_lowercase						,_lowercase						,**_lowercase					)
   snake_case				:    int           =	_DataSet(_lowercase						,_lowercase						,**_lowercase					)

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











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





