



"""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

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

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



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       List[str]:
				_lowerCamelCase        =				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        lowerCAmelCase_(   lowercase_     :							Optional[int]							)	->       List[str]:
				print('''Extracting'''						,   f.name							)
				with gzip.GzipFile(fileobj=lowercase_							) as bytestream:
								_lowerCamelCase        =				_readaa(lowercase_							)
								if magic != 20_51:
												raise ValueError(
												    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)							)
								_lowerCamelCase        =				_readaa(lowercase_							)
								_lowerCamelCase        =				_readaa(lowercase_							)
								_lowerCamelCase        =				_readaa(lowercase_							)
								_lowerCamelCase        =				bytestream.read(rows * cols * num_images							)
								_lowerCamelCase        =				numpy.frombuffer(lowercase_						,   dtype=numpy.uinta							)
								_lowerCamelCase        =				data.reshape(lowercase_						,   lowercase_						,   lowercase_						,   1							)
								return data



@deprecated(lowercase_						,   '''Please use tf.one_hot on tensors.'''							)
def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							Union[str, Any]							)	->       str:
				_lowerCamelCase        =				labels_dense.shape[0]
				_lowerCamelCase        =				numpy.arange(lowercase_							) * num_classes
				_lowerCamelCase        =				numpy.zeros((num_labels, num_classes)							)
				_lowerCamelCase        =				1
				return labels_one_hot



@deprecated(lowercase_						,   '''Please use tf.data to implement this functionality.'''							)
def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Dict=False						,   lowercase_     :							Union[str, Any]=10							)	->       Optional[Any]:
				print('''Extracting'''						,   f.name							)
				with gzip.GzipFile(fileobj=lowercase_							) as bytestream:
								_lowerCamelCase        =				_readaa(lowercase_							)
								if magic != 20_49:
												raise ValueError(
												    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)							)
								_lowerCamelCase        =				_readaa(lowercase_							)
								_lowerCamelCase        =				bytestream.read(lowercase_							)
								_lowerCamelCase        =				numpy.frombuffer(lowercase_						,   dtype=numpy.uinta							)
								if one_hot:
												return _dense_to_one_hot(lowercase_						,   lowercase_							)
								return labels


class 							lowerCamelCase_:





					'''simple docstring'''



					@deprecated(
					    lowerCamelCase__				,       '''Please use alternatives such as official/mnist/_DataSet.py'''
					    ''' from tensorflow/models.'''				,       )
					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=dtypes.floataa				,       lowerCamelCase__=True				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase  ,     _lowerCamelCase        =				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   )
									_lowerCamelCase        =				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:
													_lowerCamelCase        =				1_0_0_0_0
													_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(lowerCamelCase__				,       1.0 / 2_5_5.0   )
									_lowerCamelCase        =				images
									_lowerCamelCase        =				labels
									_lowerCamelCase        =				0
									_lowerCamelCase        =				0

					@property
					def 			snake_case__      (    self   ):
									return self._images

					@property
					def 			snake_case__      (    self   ):
									return self._labels

					@property
					def 			snake_case__      (    self   ):
									return self._num_examples

					@property
					def 			snake_case__      (    self   ):
									return self._epochs_completed





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=False				,       lowerCamelCase__=True   ):
									if fake_data:
													_lowerCamelCase        =				[1] * 7_8_4
													_lowerCamelCase        =				[1] + [0] * 9 if self.one_hot else 0
													return (
													    [fake_image for _ in range(lowerCamelCase__   )],
													    [fake_label for _ in range(lowerCamelCase__   )],
													)
									_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(lowerCamelCase__   )
													_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(lowerCamelCase__   )
																	_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(lowercase_						,   '''Please write your own downloading logic.'''							)
def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							List[Any]							)	->       Any:
				if not gfile.Exists(lowercase_							):
								gfile.MakeDirs(lowercase_							)
				_lowerCamelCase        =				os.path.join(lowercase_						,   lowercase_							)
				if not gfile.Exists(lowercase_							):
								urllib.request.urlretrieve(lowercase_						,   lowercase_							)  # noqa: S310
								with gfile.GFile(lowercase_							) as f:
												_lowerCamelCase        =				f.size()
								print('''Successfully downloaded'''						,   lowercase_						,   lowercase_						,   '''bytes.'''							)
				return filepath




@deprecated(
    lowercase_						,   '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''							)
def        lowerCAmelCase_(   lowercase_     :							List[str]						,   lowercase_     :							Union[str, Any]=False						,   lowercase_     :							Dict=False						,   lowercase_     :							Dict=dtypes.floataa						,   lowercase_     :							List[str]=True						,   lowercase_     :							Union[str, Any]=50_00						,   lowercase_     :							List[Any]=None						,   lowercase_     :							Optional[Any]=DEFAULT_SOURCE_URL						,   )	->       List[Any]:
				if fake_data:

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

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

				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(
				    lowercase_						,   lowercase_						,   source_url + train_images_file							)
				with gfile.Open(lowercase_						,   '''rb'''							) as f:
								_lowerCamelCase        =				_extract_images(lowercase_							)

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

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

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

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

				_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(lowercase_						,   lowercase_						,   **lowercase_							)
				_lowerCamelCase        =				_DataSet(lowercase_						,   lowercase_						,   **lowercase_							)
				_lowerCamelCase        =				_DataSet(lowercase_						,   lowercase_						,   **lowercase_							)

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





"""simple docstring"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
