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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowercase__		:Optional[Any]   =    "https://storage.googleapis.com/cvdf-datasets/mnist/"



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



@deprecated(lowerCAmelCase__ ,			'''Please use tf.one_hot on tensors.'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						labels_dense.shape[0]
					lowercase							=						numpy.arange(lowerCAmelCase__		) * num_classes
					lowercase							=						numpy.zeros((num_labels, num_classes)		)
					lowercase							=						1
					return labels_one_hot



@deprecated(lowerCAmelCase__ ,			'''Please use tf.data to implement this functionality.'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False ,			lowerCAmelCase__=10		):
					'''simple docstring'''
					print('''Extracting''' ,			f.name		)
					with gzip.GzipFile(fileobj=lowerCAmelCase__		) as bytestream:
										lowercase							=						_readaa(lowerCAmelCase__		)
										if magic != 2049:
															raise ValueError(
															    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)		)
										lowercase							=						_readaa(lowerCAmelCase__		)
										lowercase							=						bytestream.read(lowerCAmelCase__		)
										lowercase							=						numpy.frombuffer(lowerCAmelCase__ ,			dtype=numpy.uinta		)
										if one_hot:
															return _dense_to_one_hot(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return labels







class 				lowercase		:




							@deprecated(
							    A__   ,'''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   ,):
												lowercase	,						lowercase							=						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)
												lowercase							=						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:
																	lowercase							=						1_0_0_0_0
																	lowercase							=						one_hot
												else:
																	assert (
																	    images.shape[0] == labels.shape[0]
																	), f'images.shape: {images.shape} labels.shape: {labels.shape}'
																	lowercase							=						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
																						lowercase							=						images.reshape(
																						    images.shape[0]   ,images.shape[1] * images.shape[2])
																	if dtype == dtypes.floataa:
																						# Convert from [0, 255] -> [0.0, 1.0].
																						lowercase							=						images.astype(numpy.floataa)
																						lowercase							=						numpy.multiply(A__   ,1.0 / 255.0)
												lowercase							=						images
												lowercase							=						labels
												lowercase							=						0
												lowercase							=						0




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




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




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




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





							def  A__      (				self   ,A__   ,A__=False   ,A__=True):
												if fake_data:
																	lowercase							=						[1] * 7_8_4
																	lowercase							=						[1] + [0] * 9 if self.one_hot else 0
																	return (
																	    [fake_image for _ in range(A__)],
																	    [fake_label for _ in range(A__)],
																	)
												lowercase							=						self._index_in_epoch
												# Shuffle for the first epoch
												if self._epochs_completed == 0 and start == 0 and shuffle:
																	lowercase							=						numpy.arange(self._num_examples)
																	numpy.random.shuffle(A__)
																	lowercase							=						self.images[perma]
																	lowercase							=						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
																	lowercase							=						self._num_examples - start
																	lowercase							=						self._images[start : self._num_examples]
																	lowercase							=						self._labels[start : self._num_examples]
																	# Shuffle the data
																	if shuffle:
																						lowercase							=						numpy.arange(self._num_examples)
																						numpy.random.shuffle(A__)
																						lowercase							=						self.images[perm]
																						lowercase							=						self.labels[perm]
																	# Start next epoch
																	lowercase							=						0
																	lowercase							=						batch_size - rest_num_examples
																	lowercase							=						self._index_in_epoch
																	lowercase							=						self._images[start:end]
																	lowercase							=						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
																	lowercase							=						self._index_in_epoch
																	return self._images[start:end], self._labels[start:end]



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



@deprecated(
    lowerCAmelCase__ ,			'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''		)
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False ,			lowerCAmelCase__=False ,			lowerCAmelCase__=dtypes.floataa ,			lowerCAmelCase__=True ,			lowerCAmelCase__=5000 ,			lowerCAmelCase__=None ,			lowerCAmelCase__=DEFAULT_SOURCE_URL ,			):
					'''simple docstring'''
					if fake_data:

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

										lowercase							=						fake()
										lowercase							=						fake()
										lowercase							=						fake()
										return _Datasets(train=lowerCAmelCase__ ,			validation=lowerCAmelCase__ ,			test=lowerCAmelCase__		)

					if not source_url:  # empty string check
										lowercase							=						DEFAULT_SOURCE_URL

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

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

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

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

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

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

					lowercase							=						train_images[:validation_size]
					lowercase							=						train_labels[:validation_size]
					lowercase							=						train_images[validation_size:]
					lowercase							=						train_labels[validation_size:]

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

					lowercase							=						_DataSet(lowerCAmelCase__ ,			lowerCAmelCase__ ,			**lowerCAmelCase__		)
					lowercase							=						_DataSet(lowerCAmelCase__ ,			lowerCAmelCase__ ,			**lowerCAmelCase__		)
					lowercase							=						_DataSet(lowerCAmelCase__ ,			lowerCAmelCase__ ,			**lowerCAmelCase__		)

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






from __future__ import annotations

lowercase__		:Any   =    1.60_21E-19  # units = C



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			):
					'''simple docstring'''
					if (conductivity, electron_conc, mobility).count(0		) != 1:
										raise ValueError('''You cannot supply more or less than 2 values'''		)
					elif conductivity < 0:
										raise ValueError('''Conductivity cannot be negative'''		)
					elif electron_conc < 0:
										raise ValueError('''Electron concentration cannot be negative'''		)
					elif mobility < 0:
										raise ValueError('''mobility cannot be negative'''		)
					elif conductivity == 0:
										return (
										    "conductivity",
										    mobility * electron_conc * ELECTRON_CHARGE,
										)
					elif electron_conc == 0:
										return (
										    "electron_conc",
										    conductivity / (mobility * ELECTRON_CHARGE),
										)
					else:
										return (
										    "mobility",
										    conductivity / (electron_conc * ELECTRON_CHARGE),
										)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




