


'''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

_UpperCAmelCase				: Optional[int]							    =				collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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

def 							__magic_name__(						lowerCamelCase):
						__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 							__magic_name__(						lowerCamelCase):
						print('''Extracting'''						,				f.name)
						with gzip.GzipFile(fileobj=lowerCamelCase) as bytestream:
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												if magic != 2_0_5_1:
																		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 							__magic_name__(						lowerCamelCase						,				lowerCamelCase):
						__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 							__magic_name__(						lowerCamelCase						,				lowerCamelCase=False						,				lowerCamelCase=1_0):
						print('''Extracting'''						,				f.name)
						with gzip.GzipFile(fileobj=lowerCamelCase) as bytestream:
												__lowerCAmelCase							       =				_readaa(lowerCamelCase)
												if magic != 2_0_4_9:
																		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__				:







				"""simple docstring"""



				@deprecated(
				    __lowercase       ,			'''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       ,			):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				random_seed.get_seed(__lowercase		)
										# 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(__lowercase		).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(__lowercase       ,			1.0 / 2_55.0		)
										__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       ,			__lowercase       ,			__lowercase=False       ,			__lowercase=True		):
										if fake_data:
																__lowerCAmelCase							       =				[1] * 7_84
																__lowerCAmelCase							       =				[1] + [0] * 9 if self.one_hot else 0
																return (
																    [fake_image for _ in range(__lowercase		)],
																    [fake_label for _ in range(__lowercase		)],
																)
										__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(__lowercase		)
																__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(__lowercase		)
																						__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 							__magic_name__(						lowerCamelCase						,				lowerCamelCase						,				lowerCamelCase):
						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 							__magic_name__(						lowerCamelCase						,				lowerCamelCase=False						,				lowerCamelCase=False						,				lowerCamelCase=dtypes.floataa						,				lowerCamelCase=True						,				lowerCamelCase=5_0_0_0						,				lowerCamelCase=None						,				lowerCamelCase=DEFAULT_SOURCE_URL						,				):
						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'''

from manim import *


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				Rectangle(height=0.5       ,			width=0.5		)
										__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0		)

										__lowerCAmelCase							       =				[mem.copy() for i in range(6		)]
										__lowerCAmelCase							       =				[mem.copy() for i in range(6		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				VGroup(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				Text('''CPU'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										cpu.move_to([-2.5, -0.5, 0]		)
										self.add(__lowercase		)

										__lowerCAmelCase							       =				[mem.copy() for i in range(1		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				Text('''GPU'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										gpu.align_to(__lowercase       ,			__lowercase		)
										gpu.set_x(gpu.get_x() - 1		)

										self.add(__lowercase		)

										__lowerCAmelCase							       =				[mem.copy() for i in range(6		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)

										__lowerCAmelCase							       =				Text('''Model'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										model.move_to([3, -1.0, 0]		)

										self.play(
										    Create(__lowercase       ,			run_time=1		)       ,			Create(__lowercase       ,			run_time=1		)       ,			Create(__lowercase       ,			run_time=1		)       ,			)

										__lowerCAmelCase							       =				MarkupText(
										    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""       ,			font_size=24       ,			)

										__lowerCAmelCase							       =				Square(side_length=2.2		)
										key.move_to([-5, 2, 0]		)

										__lowerCAmelCase							       =				MarkupText(
										    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""       ,			font_size=18       ,			)

										key_text.move_to([-5, 2.4, 0]		)

										step_a.move_to([2, 2, 0]		)
										self.play(Write(__lowercase       ,			run_time=2.5		)       ,			Write(__lowercase		)       ,			Write(__lowercase		)		)

										self.add(__lowercase		)

										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										for i, rect in enumerate(__lowercase		):
																__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0.0		).set_fill(__lowercase       ,			opacity=0.7		)
																cpu_target.move_to(__lowercase		)
																cpu_target.generate_target()
																__lowerCAmelCase							       =				0.4_6 / 4
																__lowerCAmelCase							       =				0.4_6 / 3

																if i == 0:
																						cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)       ,			buff=0.0_2       ,			direction=__lowercase		)
																						cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
																elif i == 3:
																						cpu_target.target.next_to(cpu_targs[0].target       ,			direction=__lowercase       ,			buff=0.0		)
																else:
																						cpu_target.target.next_to(cpu_targs[i - 1].target       ,			direction=__lowercase       ,			buff=0.0		)
																cpu_targs.append(__lowercase		)

																first_animations.append(rect.animate(run_time=0.5		).set_stroke(__lowercase		)		)
																second_animations.append(MoveToTarget(__lowercase       ,			run_time=1.5		)		)

										self.play(*__lowercase		)
										self.play(*__lowercase		)

										self.wait()

