






"""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

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

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int				)->		Any:






     '''simple docstring'''



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


@deprecated(snake_case			,					"Please use tf.data to implement this functionality."				)
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		List[Any]:






     '''simple docstring'''



     print("Extracting"			,					f.name				)
     with gzip.GzipFile(fileobj=snake_case				) as bytestream:
          UpperCAmelCase__						:    List[str]							=							_readaa(snake_case				)
          if magic != 2051:
               raise ValueError(
                   "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)				)
          UpperCAmelCase__						:    List[str]							=							_readaa(snake_case				)
          UpperCAmelCase__						:    Any							=							_readaa(snake_case				)
          UpperCAmelCase__						:    Dict							=							_readaa(snake_case				)
          UpperCAmelCase__						:    Tuple							=							bytestream.read(rows * cols * num_images				)
          UpperCAmelCase__						:    Optional[int]							=							numpy.frombuffer(snake_case			,					dtype=numpy.uinta				)
          UpperCAmelCase__						:    str							=							data.reshape(snake_case			,					snake_case			,					snake_case			,					1				)
          return data


@deprecated(snake_case			,					"Please use tf.one_hot on tensors."				)
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : Optional[int]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							labels_dense.shape[0]
     UpperCAmelCase__						:    Optional[Any]							=							numpy.arange(snake_case				) * num_classes
     UpperCAmelCase__						:    int							=							numpy.zeros((num_labels, num_classes)				)
     UpperCAmelCase__						:    List[str]							=							1
     return labels_one_hot


@deprecated(snake_case			,					"Please use tf.data to implement this functionality."				)
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : Tuple=False			,					snake_case       : Union[str, Any]=10				)->		Tuple:






     '''simple docstring'''



     print("Extracting"			,					f.name				)
     with gzip.GzipFile(fileobj=snake_case				) as bytestream:
          UpperCAmelCase__						:    Tuple							=							_readaa(snake_case				)
          if magic != 2049:
               raise ValueError(
                   "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)				)
          UpperCAmelCase__						:    Tuple							=							_readaa(snake_case				)
          UpperCAmelCase__						:    Optional[Any]							=							bytestream.read(snake_case				)
          UpperCAmelCase__						:    int							=							numpy.frombuffer(snake_case			,					dtype=numpy.uinta				)
          if one_hot:
               return _dense_to_one_hot(snake_case			,					snake_case				)
          return labels




class 			lowerCAmelCase__			:
   @deprecated(
       snake_case__							,			"Please use alternatives such as official/mnist/_DataSet.py"
       " from tensorflow/models."							,			)
   def __init__(		self      :	Any							,			snake_case__      :	Tuple							,			snake_case__      :	Optional[int]							,			snake_case__      :	Optional[Any]=False							,			snake_case__      :	Dict=False							,			snake_case__      :	Union[str, Any]=dtypes.floataa							,			snake_case__      :	Dict=True							,			snake_case__      :	List[Any]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							random_seed.get_seed(snake_case__							)
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seeda if seed is None else seeda							)
        UpperCAmelCase__						:    int							=							dtypes.as_dtype(snake_case__							).base_dtype
        if dtype not in (dtypes.uinta, dtypes.floataa):
             raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype							)
        if fake_data:
             UpperCAmelCase__						:    Union[str, Any]							=							1_0_0_0_0
             UpperCAmelCase__						:    Tuple							=							one_hot
        else:
             assert (
                 images.shape[0] == labels.shape[0]
             ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
             UpperCAmelCase__						:    str							=							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
                  UpperCAmelCase__						:    List[Any]							=							images.reshape(
                      images.shape[0]							,			images.shape[1] * images.shape[2]							)
             if dtype == dtypes.floataa:
                  # Convert from [0, 255] -> [0.0, 1.0].
                  UpperCAmelCase__						:    int							=							images.astype(numpy.floataa							)
                  UpperCAmelCase__						:    str							=							numpy.multiply(snake_case__							,			1.0 / 255.0							)
        UpperCAmelCase__						:    int							=							images
        UpperCAmelCase__						:    List[str]							=							labels
        UpperCAmelCase__						:    Any							=							0
        UpperCAmelCase__						:    str							=							0
   @property
   def       __a							(		self      :	int							):


        '''simple docstring'''
        return self._images
   @property
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return self._labels
   @property
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return self._num_examples
   @property
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return self._epochs_completed




   def       __a							(		self      :	int							,			snake_case__      :	List[Any]							,			snake_case__      :	Optional[int]=False							,			snake_case__      :	Tuple=True							):


        '''simple docstring'''
        if fake_data:
             UpperCAmelCase__						:    Any							=							[1] * 7_8_4
             UpperCAmelCase__						:    Tuple							=							[1] + [0] * 9 if self.one_hot else 0
             return (
                 [fake_image for _ in range(snake_case__							)],
                 [fake_label for _ in range(snake_case__							)],
             )
        UpperCAmelCase__						:    Tuple							=							self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
             UpperCAmelCase__						:    str							=							numpy.arange(self._num_examples							)
             numpy.random.shuffle(snake_case__							)
             UpperCAmelCase__						:    Optional[int]							=							self.images[perma]
             UpperCAmelCase__						:    Optional[Any]							=							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
             UpperCAmelCase__						:    List[Any]							=							self._num_examples - start
             UpperCAmelCase__						:    int							=							self._images[start : self._num_examples]
             UpperCAmelCase__						:    Union[str, Any]							=							self._labels[start : self._num_examples]
             # Shuffle the data
             if shuffle:
                  UpperCAmelCase__						:    str							=							numpy.arange(self._num_examples							)
                  numpy.random.shuffle(snake_case__							)
                  UpperCAmelCase__						:    Tuple							=							self.images[perm]
                  UpperCAmelCase__						:    List[Any]							=							self.labels[perm]
             # Start next epoch
             UpperCAmelCase__						:    List[str]							=							0
             UpperCAmelCase__						:    Union[str, Any]							=							batch_size - rest_num_examples
             UpperCAmelCase__						:    Union[str, Any]							=							self._index_in_epoch
             UpperCAmelCase__						:    List[Any]							=							self._images[start:end]
             UpperCAmelCase__						:    Tuple							=							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
             UpperCAmelCase__						:    Optional[Any]							=							self._index_in_epoch
             return self._images[start:end], self._labels[start:end]




@deprecated(snake_case			,					"Please write your own downloading logic."				)
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : Dict			,					snake_case       : List[Any]				)->		int:






     '''simple docstring'''



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



@deprecated(
    snake_case			,					"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"				)
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : Dict=False			,					snake_case       : List[str]=False			,					snake_case       : Optional[Any]=dtypes.floataa			,					snake_case       : int=True			,					snake_case       : str=5000			,					snake_case       : str=None			,					snake_case       : List[Any]=DEFAULT_SOURCE_URL			,					)->		Optional[int]:






     '''simple docstring'''



     if fake_data:

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

          UpperCAmelCase__						:    Optional[Any]							=							fake()
          UpperCAmelCase__						:    List[str]							=							fake()
          UpperCAmelCase__						:    Union[str, Any]							=							fake()
          return _Datasets(train=snake_case			,					validation=snake_case			,					test=snake_case				)

     if not source_url:  # empty string check
          UpperCAmelCase__						:    Union[str, Any]							=							DEFAULT_SOURCE_URL

     UpperCAmelCase__						:    Tuple							=							"train-images-idx3-ubyte.gz"
     UpperCAmelCase__						:    Dict							=							"train-labels-idx1-ubyte.gz"
     UpperCAmelCase__						:    str							=							"t10k-images-idx3-ubyte.gz"
     UpperCAmelCase__						:    Tuple							=							"t10k-labels-idx1-ubyte.gz"

     UpperCAmelCase__						:    Optional[Any]							=							_maybe_download(
         snake_case			,					snake_case			,					source_url + train_images_file				)
     with gfile.Open(snake_case			,					"rb"				) as f:
          UpperCAmelCase__						:    List[str]							=							_extract_images(snake_case				)

     UpperCAmelCase__						:    Optional[int]							=							_maybe_download(
         snake_case			,					snake_case			,					source_url + train_labels_file				)
     with gfile.Open(snake_case			,					"rb"				) as f:
          UpperCAmelCase__						:    Optional[Any]							=							_extract_labels(snake_case			,					one_hot=snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							_maybe_download(
         snake_case			,					snake_case			,					source_url + test_images_file				)
     with gfile.Open(snake_case			,					"rb"				) as f:
          UpperCAmelCase__						:    Optional[int]							=							_extract_images(snake_case				)

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

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

     UpperCAmelCase__						:    Optional[Any]							=							train_images[:validation_size]
     UpperCAmelCase__						:    Optional[Any]							=							train_labels[:validation_size]
     UpperCAmelCase__						:    Union[str, Any]							=							train_images[validation_size:]
     UpperCAmelCase__						:    Optional[Any]							=							train_labels[validation_size:]

     UpperCAmelCase__						:    Union[str, Any]							=							{"dtype": dtype, "reshape": reshape, "seed": seed}

     UpperCAmelCase__						:    Optional[int]							=							_DataSet(snake_case			,					snake_case			,					**snake_case				)
     UpperCAmelCase__						:    Tuple							=							_DataSet(snake_case			,					snake_case			,					**snake_case				)
     UpperCAmelCase__						:    Any							=							_DataSet(snake_case			,					snake_case			,					**snake_case				)

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










"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Any




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							data
        UpperCAmelCase__						:    Node | None							=							None




class 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							None
        UpperCAmelCase__						:    Optional[int]							=							None
   def __iter__(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.head
        while self.head:
             yield node.data
             UpperCAmelCase__						:    Optional[Any]							=							node.next
             if node == self.head:
                  break
   def __len__(		self      :	Tuple							):


        '''simple docstring'''
        return sum(1 for _ in self							)
   def __repr__(		self      :	str							):


        '''simple docstring'''
        return "->".join(str(snake_case__							) for item in iter(self							)							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(len(self							)							,			snake_case__							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(0							,			snake_case__							)
   def       __a							(		self      :	Any							,			snake_case__      :	int							,			snake_case__      :	Any							):


        '''simple docstring'''
        if index < 0 or index > len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Any							=							Node(snake_case__							)
        if self.head is None:
             UpperCAmelCase__						:    Union[str, Any]							=							new_node  # first node points itself
             UpperCAmelCase__						:    List[str]							=							new_node
        elif index == 0:  # insert at head
             UpperCAmelCase__						:    Optional[Any]							=							self.head
             UpperCAmelCase__						:    List[Any]							=							new_node
        else:
             UpperCAmelCase__						:    Union[str, Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    str							=							new_node
             if index == len(self							) - 1:  # insert at tail
                  UpperCAmelCase__						:    Union[str, Any]							=							new_node
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return self.delete_nth(0							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        return self.delete_nth(len(self							) - 1							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	int = 0							):


        '''simple docstring'''
        if not 0 <= index < len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Union[str, Any]							=							self.head
        if self.head == self.tail:  # just one node
             UpperCAmelCase__						:    str							=							None
        elif index == 0:  # delete head node
             UpperCAmelCase__						:    List[Any]							=							self.tail.next.next
             UpperCAmelCase__						:    Dict							=							self.head.next
        else:
             UpperCAmelCase__						:    List[Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next.next
             if index == len(self							) - 1:  # delete at tail
                  UpperCAmelCase__						:    int							=							temp
        return delete_node.data




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return len(self							) == 0




def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							CircularLinkedList()
     assert len(snake_case				) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case				) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1				)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0				)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5				):
          assert len(snake_case				) == i
          circular_linked_list.insert_nth(snake_case			,					i + 1				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     circular_linked_list.insert_tail(6				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					7				)				)
     circular_linked_list.insert_head(0				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(0			,					7				)				)

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)
     assert circular_linked_list.delete_nth(2				) == 3

     circular_linked_list.insert_nth(2			,					3				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()


