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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowercase__	:int										=					"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(
							    _lowercase     ,'''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					=		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)
												lowercase					=		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:
																	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(_lowercase     ,1.0 / 2_5_5.0)
												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(_lowercase)],
																	    [fake_label for _ in range(_lowercase)],
																	)
												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(_lowercase)
																	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(_lowercase)
																						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

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = 10**-10	):
					'''simple docstring'''
					lowercase					=		a
					while True:
										lowercase					=		Decimal(lowerCAmelCase__	) - (
										    Decimal(eval(lowerCAmelCase__	)	) / Decimal(eval(str(diff(lowerCAmelCase__	)	)	)	)  # noqa: S307
										)
										# This number dictates the accuracy of the answer
										if abs(eval(lowerCAmelCase__	)	) < precision:  # noqa: S307
															return float(lowerCAmelCase__	)


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')




