"""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

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

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







def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							numpy.dtype(numpy.uintaa).newbyteorder('''>''')
     return numpy.frombuffer(bytestream.read(4),      dtype=snake_case)[0]







@deprecated(snake_case,      '''Please use tf.data to implement this functionality.''')
def    SCREAMING_SNAKE_CASE       (     snake_case):
     print('''Extracting''',      f.name)
     with gzip.GzipFile(fileobj=snake_case) as bytestream:
          __snake_case				=							_readaa(snake_case)
          if magic != 20_51:
               raise ValueError(
                   '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name))
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							bytestream.read(rows * cols * num_images)
          __snake_case				=							numpy.frombuffer(snake_case,      dtype=numpy.uinta)
          __snake_case				=							data.reshape(snake_case,      snake_case,      snake_case,      1)
          return data







@deprecated(snake_case,      '''Please use tf.one_hot on tensors.''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							labels_dense.shape[0]
     __snake_case				=							numpy.arange(snake_case) * num_classes
     __snake_case				=							numpy.zeros((num_labels, num_classes))
     __snake_case				=							1
     return labels_one_hot







@deprecated(snake_case,      '''Please use tf.data to implement this functionality.''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=False,      snake_case=10):
     print('''Extracting''',      f.name)
     with gzip.GzipFile(fileobj=snake_case) as bytestream:
          __snake_case				=							_readaa(snake_case)
          if magic != 20_49:
               raise ValueError(
                   '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name))
          __snake_case				=							_readaa(snake_case)
          __snake_case				=							bytestream.read(snake_case)
          __snake_case				=							numpy.frombuffer(snake_case,      dtype=numpy.uinta)
          if one_hot:
               return _dense_to_one_hot(snake_case,      snake_case)
          return labels



class      _A	:




    """simple docstring"""




    @deprecated(
        A_	,      '''Please use alternatives such as official/mnist/_DataSet.py'''
        ''' from tensorflow/models.'''	,      )
    def __init__(		self							:     List[str]	,      A_							:     int	,      A_							:     Tuple	,      A_							:     Optional[int]=False	,      A_							:     int=False	,      A_							:     Dict=dtypes.floataa	,      A_							:     Tuple=True	,      A_							:     Any=None	,      )    ->       Any:
         __snake_case		,	__snake_case				=							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    )
         __snake_case				=							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:
              __snake_case				=							10_000
              __snake_case				=							one_hot
         else:
              assert (
                  images.shape[0] == labels.shape[0]
              ), f"images.shape: {images.shape} labels.shape: {labels.shape}"
              __snake_case				=							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				=							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				=							images.astype(numpy.floataa    )
                   __snake_case				=							numpy.multiply(A_	,      1.0 / 255.0    )
         __snake_case				=							images
         __snake_case				=							labels
         __snake_case				=							0
         __snake_case				=							0
    @property
    def  lowercase					(		self							:     int    )    ->       Dict:
         return self._images
    @property
    def  lowercase					(		self							:     Dict    )    ->       Optional[int]:
         return self._labels
    @property
    def  lowercase					(		self							:     int    )    ->       Tuple:
         return self._num_examples
    @property
    def  lowercase					(		self							:     int    )    ->       Tuple:
         return self._epochs_completed







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







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







@deprecated(
    snake_case,      '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''')
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case=False,      snake_case=False,      snake_case=dtypes.floataa,      snake_case=True,      snake_case=50_00,      snake_case=None,      snake_case=DEFAULT_SOURCE_URL,      ):
     if fake_data:

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

          __snake_case				=							fake()
          __snake_case				=							fake()
          __snake_case				=							fake()
          return _Datasets(train=snake_case,      validation=snake_case,      test=snake_case)

     if not source_url:  # empty string check
          __snake_case				=							DEFAULT_SOURCE_URL

     __snake_case				=							'''train-images-idx3-ubyte.gz'''
     __snake_case				=							'''train-labels-idx1-ubyte.gz'''
     __snake_case				=							'''t10k-images-idx3-ubyte.gz'''
     __snake_case				=							'''t10k-labels-idx1-ubyte.gz'''

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

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + train_labels_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_labels(snake_case,      one_hot=snake_case)

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + test_images_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_images(snake_case)

     __snake_case				=							_maybe_download(
         snake_case,      snake_case,      source_url + test_labels_file)
     with gfile.Open(snake_case,      '''rb''') as f:
          __snake_case				=							_extract_labels(snake_case,      one_hot=snake_case)

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

     __snake_case				=							train_images[:validation_size]
     __snake_case				=							train_labels[:validation_size]
     __snake_case				=							train_images[validation_size:]
     __snake_case				=							train_labels[validation_size:]

     __snake_case				=							{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

     __snake_case				=							_DataSet(snake_case,      snake_case,      **snake_case)
     __snake_case				=							_DataSet(snake_case,      snake_case,      **snake_case)
     __snake_case				=							_DataSet(snake_case,      snake_case,      **snake_case)

     return _Datasets(train=snake_case,      validation=snake_case,      test=snake_case)
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))