




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

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


def 					UpperCamelCase_(     snake_case							:	Optional[Any]					):

							'''simple docstring'''






							snake_case_      =				numpy.dtype(numpy.uintaa					).newbyteorder(">"					)
							return numpy.frombuffer(bytestream.read(4					)							,    dtype=UpperCAmelCase__					)[0]


@deprecated(UpperCAmelCase__							,    "Please use tf.data to implement this functionality."					)
def 					UpperCamelCase_(     snake_case							:	Optional[int]					):

							'''simple docstring'''






							print("Extracting"							,    f.name					)
							with gzip.GzipFile(fileobj=UpperCAmelCase__					) as bytestream:
														snake_case_      =				_readaa(UpperCAmelCase__					)
														if magic != 2_0_5_1:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)					)
														snake_case_      =				_readaa(UpperCAmelCase__					)
														snake_case_      =				_readaa(UpperCAmelCase__					)
														snake_case_      =				_readaa(UpperCAmelCase__					)
														snake_case_      =				bytestream.read(rows * cols * num_images					)
														snake_case_      =				numpy.frombuffer(UpperCAmelCase__							,    dtype=numpy.uinta					)
														snake_case_      =				data.reshape(UpperCAmelCase__							,    UpperCAmelCase__							,    UpperCAmelCase__							,    1					)
														return data


@deprecated(UpperCAmelCase__							,    "Please use tf.one_hot on tensors."					)
def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	str					):

							'''simple docstring'''






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


@deprecated(UpperCAmelCase__							,    "Please use tf.data to implement this functionality."					)
def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Optional[Any]=False							,    snake_case							:	Optional[Any]=1_0					):

							'''simple docstring'''






							print("Extracting"							,    f.name					)
							with gzip.GzipFile(fileobj=UpperCAmelCase__					) as bytestream:
														snake_case_      =				_readaa(UpperCAmelCase__					)
														if magic != 2_0_4_9:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)					)
														snake_case_      =				_readaa(UpperCAmelCase__					)
														snake_case_      =				bytestream.read(UpperCAmelCase__					)
														snake_case_      =				numpy.frombuffer(UpperCAmelCase__							,    dtype=numpy.uinta					)
														if one_hot:
																					return _dense_to_one_hot(UpperCAmelCase__							,    UpperCAmelCase__					)
														return labels




class 			_snake_case		:




			@deprecated(
			    UpperCamelCase__    ,							"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    ,							)    ->    List[str]:

										'''simple docstring'''




										snake_case_ ,				snake_case_      =				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							)
										snake_case_      =				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:
																	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(UpperCamelCase__    ,							1.0 / 2_5_5.0							)
										snake_case_      =				images
										snake_case_      =				labels
										snake_case_      =				0
										snake_case_      =				0




			@property
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										return self._images




			@property
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										return self._labels




			@property
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										return self._num_examples




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

										'''simple docstring'''




										return self._epochs_completed




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__=False    ,							a__=True							)    ->    Union[str, Any]:

										'''simple docstring'''




										if fake_data:
																	snake_case_      =				[1] * 784
																	snake_case_      =				[1] + [0] * 9 if self.one_hot else 0
																	return (
																	    [fake_image for _ in range(UpperCamelCase__							)],
																	    [fake_label for _ in range(UpperCamelCase__							)],
																	)
										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(UpperCamelCase__							)
																	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(UpperCamelCase__							)
																								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(UpperCAmelCase__							,    "Please write your own downloading logic."					)
def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	List[str]							,    snake_case							:	Union[str, Any]					):

							'''simple docstring'''






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




@deprecated(
    UpperCAmelCase__							,    "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"					)
def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	str=False							,    snake_case							:	List[str]=False							,    snake_case							:	Optional[int]=dtypes.floataa							,    snake_case							:	str=True							,    snake_case							:	Optional[Any]=5_0_0_0							,    snake_case							:	Optional[Any]=None							,    snake_case							:	Tuple=DEFAULT_SOURCE_URL							,    ):

							'''simple docstring'''






							if fake_data:

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

														snake_case_      =				fake()
														snake_case_      =				fake()
														snake_case_      =				fake()
														return _Datasets(train=UpperCAmelCase__							,    validation=UpperCAmelCase__							,    test=UpperCAmelCase__					)

							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(
							    UpperCAmelCase__							,    UpperCAmelCase__							,    source_url + train_images_file					)
							with gfile.Open(UpperCAmelCase__							,    "rb"					) as f:
														snake_case_      =				_extract_images(UpperCAmelCase__					)

							snake_case_      =				_maybe_download(
							    UpperCAmelCase__							,    UpperCAmelCase__							,    source_url + train_labels_file					)
							with gfile.Open(UpperCAmelCase__							,    "rb"					) as f:
														snake_case_      =				_extract_labels(UpperCAmelCase__							,    one_hot=UpperCAmelCase__					)

							snake_case_      =				_maybe_download(
							    UpperCAmelCase__							,    UpperCAmelCase__							,    source_url + test_images_file					)
							with gfile.Open(UpperCAmelCase__							,    "rb"					) as f:
														snake_case_      =				_extract_images(UpperCAmelCase__					)

							snake_case_      =				_maybe_download(
							    UpperCAmelCase__							,    UpperCAmelCase__							,    source_url + test_labels_file					)
							with gfile.Open(UpperCAmelCase__							,    "rb"					) as f:
														snake_case_      =				_extract_labels(UpperCAmelCase__							,    one_hot=UpperCAmelCase__					)

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

							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(UpperCAmelCase__							,    UpperCAmelCase__							,    **UpperCAmelCase__					)
							snake_case_      =				_DataSet(UpperCAmelCase__							,    UpperCAmelCase__							,    **UpperCAmelCase__					)
							snake_case_      =				_DataSet(UpperCAmelCase__							,    UpperCAmelCase__							,    **UpperCAmelCase__					)

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










'''simple docstring'''





_SCREAMING_SNAKE_CASE							:       Optional[int]						=			"Alexander Joslin"

import operator as op

from .stack import Stack


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				{"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

							snake_case_      =				Stack()
							snake_case_      =				Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(snake_case					)					)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(snake_case					)
														elif i == ")":
																					# RULE 4
																					snake_case_      =				operator_stack.peek()
																					operator_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()
																					snake_case_      =				operand_stack.peek()
																					operand_stack.pop()

																					snake_case_      =				operators[opr](snake_case							,    snake_case					)
																					operand_stack.push(snake_case					)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[Any]						=			"(5 + ((4 * 2) * (2 + 3)))"
		# answer = 45
		print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")




