


"""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

_snake_case          =							collections.namedtuple("_Datasets", ["train", "validation", "test"])

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



def   snake_case						(				_a:     Any							)->		str:
      '''simple docstring'''


      lowerCamelCase__           =		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   snake_case						(				_a:     Dict							)->		Optional[int]:
      '''simple docstring'''


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



@deprecated(lowerCamelCase_		,   'Please use tf.one_hot on tensors.'							)
def   snake_case						(				_a:     str		,   _a:     Dict							)->		str:
      '''simple docstring'''


      lowerCamelCase__           =		labels_dense.shape[0]
      lowerCamelCase__           =		numpy.arange(lowerCamelCase_							) * num_classes
      lowerCamelCase__           =		numpy.zeros((num_labels, num_classes)							)
      lowerCamelCase__           =		1
      return labels_one_hot



@deprecated(lowerCamelCase_		,   'Please use tf.data to implement this functionality.'							)
def   snake_case						(				_a:     Any		,   _a:     Dict=False		,   _a:     str=10							)->		Any:
      '''simple docstring'''


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







class   _a      :
    @deprecated(
        UpperCAmelCase_							,					'Please use alternatives such as official/mnist/_DataSet.py'
        ' from tensorflow/models.'							,					)
    def __init__( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]=False							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=False							,					SCREAMING_SNAKE_CASE__							:					Tuple=dtypes.floataa							,					SCREAMING_SNAKE_CASE__							:					int=True							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=None							,					):
          lowerCamelCase__           =		random_seed.get_seed(UpperCAmelCase_				)
          # If op level seed is not set, use whatever graph level seed is returned
          numpy.random.seed(seeda if seed is None else seeda				)
          lowerCamelCase__           =		dtypes.as_dtype(UpperCAmelCase_				).base_dtype
          if dtype not in (dtypes.uinta, dtypes.floataa):
                raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype				)
          if fake_data:
                lowerCamelCase__           =		1_00_00
                lowerCamelCase__           =		one_hot
          else:
                assert (
                    images.shape[0] == labels.shape[0]
                ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
                lowerCamelCase__           =		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
                      lowerCamelCase__           =		images.reshape(
                          images.shape[0]							,					images.shape[1] * images.shape[2]				)
                if dtype == dtypes.floataa:
                      # Convert from [0, 255] -> [0.0, 1.0].
                      lowerCamelCase__           =		images.astype(numpy.floataa				)
                      lowerCamelCase__           =		numpy.multiply(UpperCAmelCase_							,					1.0 / 2_55.0				)
          lowerCamelCase__           =		images
          lowerCamelCase__           =		labels
          lowerCamelCase__           =		0
          lowerCamelCase__           =		0
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          return self._images
    @property
    def      _UpperCamelCase       ( self							:					List[Any]				):
          return self._labels
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          return self._num_examples
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return self._epochs_completed


    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					List[Any]=False							,					SCREAMING_SNAKE_CASE__							:					Dict=True				):
          if fake_data:
                lowerCamelCase__           =		[1] * 7_84
                lowerCamelCase__           =		[1] + [0] * 9 if self.one_hot else 0
                return (
                    [fake_image for _ in range(UpperCAmelCase_				)],
                    [fake_label for _ in range(UpperCAmelCase_				)],
                )
          lowerCamelCase__           =		self._index_in_epoch
          # Shuffle for the first epoch
          if self._epochs_completed == 0 and start == 0 and shuffle:
                lowerCamelCase__           =		numpy.arange(self._num_examples				)
                numpy.random.shuffle(UpperCAmelCase_				)
                lowerCamelCase__           =		self.images[perma]
                lowerCamelCase__           =		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
                lowerCamelCase__           =		self._num_examples - start
                lowerCamelCase__           =		self._images[start : self._num_examples]
                lowerCamelCase__           =		self._labels[start : self._num_examples]
                # Shuffle the data
                if shuffle:
                      lowerCamelCase__           =		numpy.arange(self._num_examples				)
                      numpy.random.shuffle(UpperCAmelCase_				)
                      lowerCamelCase__           =		self.images[perm]
                      lowerCamelCase__           =		self.labels[perm]
                # Start next epoch
                lowerCamelCase__           =		0
                lowerCamelCase__           =		batch_size - rest_num_examples
                lowerCamelCase__           =		self._index_in_epoch
                lowerCamelCase__           =		self._images[start:end]
                lowerCamelCase__           =		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
                lowerCamelCase__           =		self._index_in_epoch
                return self._images[start:end], self._labels[start:end]





@deprecated(lowerCamelCase_		,   'Please write your own downloading logic.'							)
def   snake_case						(				_a:     List[Any]		,   _a:     Optional[Any]		,   _a:     Any							)->		Optional[Any]:
      '''simple docstring'''


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






@deprecated(
    lowerCamelCase_		,   'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'							)
def   snake_case						(				_a:     Tuple		,   _a:     Optional[Any]=False		,   _a:     str=False		,   _a:     Union[str, Any]=dtypes.floataa		,   _a:     int=True		,   _a:     Optional[int]=5000		,   _a:     Union[str, Any]=None		,   _a:     Dict=DEFAULT_SOURCE_URL		,   )->		List[str]:
      '''simple docstring'''


      if fake_data:

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

            lowerCamelCase__           =		fake()
            lowerCamelCase__           =		fake()
            lowerCamelCase__           =		fake()
            return _Datasets(train=lowerCamelCase_		,   validation=lowerCamelCase_		,   test=lowerCamelCase_							)

      if not source_url:  # empty string check
            lowerCamelCase__           =		DEFAULT_SOURCE_URL

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

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

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

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

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

      if not 0 <= validation_size <= len(lowerCamelCase_							):
            lowerCamelCase__           =		(
                'Validation size should be between 0 and '
                F'{len(lowerCamelCase_							)}. Received: {validation_size}.'
            )
            raise ValueError(lowerCamelCase_							)

      lowerCamelCase__           =		train_images[:validation_size]
      lowerCamelCase__           =		train_labels[:validation_size]
      lowerCamelCase__           =		train_images[validation_size:]
      lowerCamelCase__           =		train_labels[validation_size:]

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

      lowerCamelCase__           =		_DataSet(lowerCamelCase_		,   lowerCamelCase_		,   **lowerCamelCase_							)
      lowerCamelCase__           =		_DataSet(lowerCamelCase_		,   lowerCamelCase_		,   **lowerCamelCase_							)
      lowerCamelCase__           =		_DataSet(lowerCamelCase_		,   lowerCamelCase_		,   **lowerCamelCase_							)

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







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
   print(f"""{solution() = }""")



