



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

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





def       __magic_name__    (   _lowerCamelCase				: str					):
							__a		:		Tuple									=       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       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
							print("""Extracting"""  ,       f.name					)
							with gzip.GzipFile(fileobj=lowerCamelCase_					) as bytestream:
														__a		:		str									=       _readaa(lowerCamelCase_					)
														if magic != 2_0_5_1:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)					)
														__a		:		List[str]									=       _readaa(lowerCamelCase_					)
														__a		:		List[Any]									=       _readaa(lowerCamelCase_					)
														__a		:		Dict									=       _readaa(lowerCamelCase_					)
														__a		:		int									=       bytestream.read(rows * cols * num_images					)
														__a		:		int									=       numpy.frombuffer(lowerCamelCase_  ,       dtype=numpy.uinta					)
														__a		:		Optional[int]									=       data.reshape(lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_  ,       1					)
														return data





@deprecated(lowerCamelCase_  ,       """Please use tf.one_hot on tensors."""					)
def       __magic_name__    (   _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		List[Any]									=       labels_dense.shape[0]
							__a		:		int									=       numpy.arange(lowerCamelCase_					) * num_classes
							__a		:		List[str]									=       numpy.zeros((num_labels, num_classes)					)
							__a		:		Optional[int]									=       1
							return labels_one_hot





@deprecated(lowerCamelCase_  ,       """Please use tf.data to implement this functionality."""					)
def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: str=False  ,       _lowerCamelCase				: str=1_0					):
							print("""Extracting"""  ,       f.name					)
							with gzip.GzipFile(fileobj=lowerCamelCase_					) as bytestream:
														__a		:		Tuple									=       _readaa(lowerCamelCase_					)
														if magic != 2_0_4_9:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)					)
														__a		:		Any									=       _readaa(lowerCamelCase_					)
														__a		:		Tuple									=       bytestream.read(lowerCamelCase_					)
														__a		:		Any									=       numpy.frombuffer(lowerCamelCase_  ,       dtype=numpy.uinta					)
														if one_hot:
																					return _dense_to_one_hot(lowerCamelCase_  ,       lowerCamelCase_					)
														return labels




class SCREAMING_SNAKE_CASE__					:



						@deprecated(
						    __lowerCamelCase						,     """Please use alternatives such as official/mnist/_DataSet.py"""
						    """ from tensorflow/models."""						,     )
						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=False						,     _lowercase=dtypes.floataa						,     _lowercase=True						,     _lowercase=None						,     ):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       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							)
													__a		:		int									=       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:
																				__a		:		Any									=       10000
																				__a		:		Optional[int]									=       one_hot
													else:
																				assert (
																				    images.shape[0] == labels.shape[0]
																				), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																				__a		:		int									=       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
																											__a		:		Any									=       images.reshape(
																											    images.shape[0]						,     images.shape[1] * images.shape[2]							)
																				if dtype == dtypes.floataa:
																											# Convert from [0, 255] -> [0.0, 1.0].
																											__a		:		Union[str, Any]									=       images.astype(numpy.floataa							)
																											__a		:		Dict									=       numpy.multiply(__lowerCamelCase						,     1.0 / 255.0							)
													__a		:		Optional[int]									=       images
													__a		:		int									=       labels
													__a		:		Optional[Any]									=       0
													__a		:		Tuple									=       0



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._images



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._labels



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._num_examples



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self._epochs_completed





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=False						,     _lowercase=True							):





													'''simple docstring'''

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







@deprecated(lowerCamelCase_  ,       """Please write your own downloading logic."""					)
def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: int					):

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







@deprecated(
    lowerCamelCase_  ,       """Please use alternatives such as:""" """ tensorflow_datasets.load(\'mnist\')"""					)
def       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Any=False  ,       _lowerCamelCase				: str=False  ,       _lowerCamelCase				: Optional[int]=dtypes.floataa  ,       _lowerCamelCase				: List[str]=True  ,       _lowerCamelCase				: Any=5_0_0_0  ,       _lowerCamelCase				: Dict=None  ,       _lowerCamelCase				: List[Any]=DEFAULT_SOURCE_URL  ,       ):

							if fake_data:

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

														__a		:		List[str]									=       fake()
														__a		:		List[Any]									=       fake()
														__a		:		int									=       fake()
														return _Datasets(train=lowerCamelCase_  ,       validation=lowerCamelCase_  ,       test=lowerCamelCase_					)

							if not source_url:  # empty string check
														__a		:		List[Any]									=       DEFAULT_SOURCE_URL

							__a		:		Tuple									=       '''train-images-idx3-ubyte.gz'''
							__a		:		Optional[int]									=       '''train-labels-idx1-ubyte.gz'''
							__a		:		Optional[int]									=       '''t10k-images-idx3-ubyte.gz'''
							__a		:		List[Any]									=       '''t10k-labels-idx1-ubyte.gz'''

							__a		:		Optional[Any]									=       _maybe_download(
							    lowerCamelCase_  ,       lowerCamelCase_  ,       source_url + train_images_file					)
							with gfile.Open(lowerCamelCase_  ,       """rb"""					) as f:
														__a		:		Optional[int]									=       _extract_images(lowerCamelCase_					)

							__a		:		Optional[int]									=       _maybe_download(
							    lowerCamelCase_  ,       lowerCamelCase_  ,       source_url + train_labels_file					)
							with gfile.Open(lowerCamelCase_  ,       """rb"""					) as f:
														__a		:		Dict									=       _extract_labels(lowerCamelCase_  ,       one_hot=lowerCamelCase_					)

							__a		:		List[str]									=       _maybe_download(
							    lowerCamelCase_  ,       lowerCamelCase_  ,       source_url + test_images_file					)
							with gfile.Open(lowerCamelCase_  ,       """rb"""					) as f:
														__a		:		int									=       _extract_images(lowerCamelCase_					)

							__a		:		Tuple									=       _maybe_download(
							    lowerCamelCase_  ,       lowerCamelCase_  ,       source_url + test_labels_file					)
							with gfile.Open(lowerCamelCase_  ,       """rb"""					) as f:
														__a		:		str									=       _extract_labels(lowerCamelCase_  ,       one_hot=lowerCamelCase_					)

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

							__a		:		Any									=       train_images[:validation_size]
							__a		:		str									=       train_labels[:validation_size]
							__a		:		Any									=       train_images[validation_size:]
							__a		:		Any									=       train_labels[validation_size:]

							__a		:		Optional[Any]									=       {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

							__a		:		List[str]									=       _DataSet(lowerCamelCase_  ,       lowerCamelCase_  ,       **lowerCamelCase_					)
							__a		:		List[Any]									=       _DataSet(lowerCamelCase_  ,       lowerCamelCase_  ,       **lowerCamelCase_					)
							__a		:		List[Any]									=       _DataSet(lowerCamelCase_  ,       lowerCamelCase_  ,       **lowerCamelCase_					)

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








"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float(moles / volume					) * nfactor					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (volume)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

