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

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






def __lowerCAmelCase (							UpperCamelCase__				)	->    Any:
    __lowerCamelCase		=  numpy.dtype(numpy.uintaa				).newbyteorder('''>'''				)
    return numpy.frombuffer(bytestream.read(4				)				,  dtype=UpperCamelCase__				)[0]






@deprecated(UpperCamelCase__				,  '''Please use tf.data to implement this functionality.'''				)
def __lowerCAmelCase (							UpperCamelCase__				)	->    Optional[Any]:
    print('''Extracting'''				,  f.name				)
    with gzip.GzipFile(fileobj=UpperCamelCase__				) as bytestream:
        __lowerCamelCase		=  _readaa(UpperCamelCase__				)
        if magic != 20_51:
            raise ValueError(
                '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)				)
        __lowerCamelCase		=  _readaa(UpperCamelCase__				)
        __lowerCamelCase		=  _readaa(UpperCamelCase__				)
        __lowerCamelCase		=  _readaa(UpperCamelCase__				)
        __lowerCamelCase		=  bytestream.read(rows * cols * num_images				)
        __lowerCamelCase		=  numpy.frombuffer(UpperCamelCase__				,  dtype=numpy.uinta				)
        __lowerCamelCase		=  data.reshape(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  1				)
        return data






@deprecated(UpperCamelCase__				,  '''Please use tf.one_hot on tensors.'''				)
def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    __lowerCamelCase		=  labels_dense.shape[0]
    __lowerCamelCase		=  numpy.arange(UpperCamelCase__				) * num_classes
    __lowerCamelCase		=  numpy.zeros((num_labels, num_classes)				)
    __lowerCamelCase		=  1
    return labels_one_hot






@deprecated(UpperCamelCase__				,  '''Please use tf.data to implement this functionality.'''				)
def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__=False				,  UpperCamelCase__=10				)	->    Union[str, Any]:
    print('''Extracting'''				,  f.name				)
    with gzip.GzipFile(fileobj=UpperCamelCase__				) as bytestream:
        __lowerCamelCase		=  _readaa(UpperCamelCase__				)
        if magic != 20_49:
            raise ValueError(
                '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)				)
        __lowerCamelCase		=  _readaa(UpperCamelCase__				)
        __lowerCamelCase		=  bytestream.read(UpperCamelCase__				)
        __lowerCamelCase		=  numpy.frombuffer(UpperCamelCase__				,  dtype=numpy.uinta				)
        if one_hot:
            return _dense_to_one_hot(UpperCamelCase__				,  UpperCamelCase__				)
        return labels




class     a__      :






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





         """simple docstring"""



         __lowerCamelCase	,  __lowerCamelCase		=  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    )
         __lowerCamelCase		=  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:
             __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(a  ,				1.0 / 2_55.0    )
         __lowerCamelCase		=  images
         __lowerCamelCase		=  labels
         __lowerCamelCase		=  0
         __lowerCamelCase		=  0






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         return self._images






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         return self._labels






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         return self._num_examples






     @property
     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         return self._epochs_completed






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]  ,				a		:		Any  ,				a		:		List[Any]=False  ,				a		:		Union[str, Any]=True    ):





         """simple docstring"""



         if fake_data:
             __lowerCamelCase		=  [1] * 7_84
             __lowerCamelCase		=  [1] + [0] * 9 if self.one_hot else 0
             return (
                 [fake_image for _ in range(a    )],
                 [fake_label for _ in range(a    )],
             )
         __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(a    )
             __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(a    )
                 __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(UpperCamelCase__				,  '''Please write your own downloading logic.'''				)
def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    List[str]:
    if not gfile.Exists(UpperCamelCase__				):
        gfile.MakeDirs(UpperCamelCase__				)
    __lowerCamelCase		=  os.path.join(UpperCamelCase__				,  UpperCamelCase__				)
    if not gfile.Exists(UpperCamelCase__				):
        urllib.request.urlretrieve(UpperCamelCase__				,  UpperCamelCase__				)  # noqa: S310
        with gfile.GFile(UpperCamelCase__				) as f:
            __lowerCamelCase		=  f.size()
        print('''Successfully downloaded'''				,  UpperCamelCase__				,  UpperCamelCase__				,  '''bytes.'''				)
    return filepath






@deprecated(
    UpperCamelCase__				,  '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''				)
def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__=False				,  UpperCamelCase__=False				,  UpperCamelCase__=dtypes.floataa				,  UpperCamelCase__=True				,  UpperCamelCase__=50_00				,  UpperCamelCase__=None				,  UpperCamelCase__=DEFAULT_SOURCE_URL				,  )	->    Tuple:
    if fake_data:

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

        __lowerCamelCase		=  fake()
        __lowerCamelCase		=  fake()
        __lowerCamelCase		=  fake()
        return _Datasets(train=UpperCamelCase__				,  validation=UpperCamelCase__				,  test=UpperCamelCase__				)

    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(
        UpperCamelCase__				,  UpperCamelCase__				,  source_url + train_images_file				)
    with gfile.Open(UpperCamelCase__				,  '''rb'''				) as f:
        __lowerCamelCase		=  _extract_images(UpperCamelCase__				)

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

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

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

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

    __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(UpperCamelCase__				,  UpperCamelCase__				,  **UpperCamelCase__				)
    __lowerCamelCase		=  _DataSet(UpperCamelCase__				,  UpperCamelCase__				,  **UpperCamelCase__				)
    __lowerCamelCase		=  _DataSet(UpperCamelCase__				,  UpperCamelCase__				,  **UpperCamelCase__				)

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




from __future__ import annotations

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




      if index == len(_A						):
            print(_A						)
            return

      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.append(sequence[index]						)
      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.pop()


if __name__ == "__main__":
    A      :    list[Any]		=					[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


