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_			(					__A							)					->    Optional[int]:







      '''simple docstring'''


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




@deprecated(__A,  "Please use tf.data to implement this functionality."							)
def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      print("Extracting",  f.name							)
      with gzip.GzipFile(fileobj=__A							) as bytestream:
            UpperCAmelCase__								=  _readaa(__A							)
            if magic != 2_051:
                  raise ValueError(
                      "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)							)
            UpperCAmelCase__								=  _readaa(__A							)
            UpperCAmelCase__								=  _readaa(__A							)
            UpperCAmelCase__								=  _readaa(__A							)
            UpperCAmelCase__								=  bytestream.read(rows * cols * num_images							)
            UpperCAmelCase__								=  numpy.frombuffer(__A,  dtype=numpy.uinta							)
            UpperCAmelCase__								=  data.reshape(__A,  __A,  __A,  1							)
            return data




@deprecated(__A,  "Please use tf.one_hot on tensors."							)
def        lowerCAmelCase_			(					__A,  __A							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  labels_dense.shape[0]
      UpperCAmelCase__								=  numpy.arange(__A							) * num_classes
      UpperCAmelCase__								=  numpy.zeros((num_labels, num_classes)							)
      UpperCAmelCase__								=  1
      return labels_one_hot




@deprecated(__A,  "Please use tf.data to implement this functionality."							)
def        lowerCAmelCase_			(					__A,  __A=False,  __A=10							)					->    List[str]:







      '''simple docstring'''


      print("Extracting",  f.name							)
      with gzip.GzipFile(fileobj=__A							) as bytestream:
            UpperCAmelCase__								=  _readaa(__A							)
            if magic != 2_049:
                  raise ValueError(
                      "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)							)
            UpperCAmelCase__								=  _readaa(__A							)
            UpperCAmelCase__								=  bytestream.read(__A							)
            UpperCAmelCase__								=  numpy.frombuffer(__A,  dtype=numpy.uinta							)
            if one_hot:
                  return _dense_to_one_hot(__A,  __A							)
            return labels





class 					A							:


   @deprecated(
       __UpperCAmelCase	,  "Please use alternatives such as official/mnist/_DataSet.py"
       " from tensorflow/models."	,  )
   def __init__(self					:						List[Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						str=False	,  __UpperCAmelCase					:						List[Any]=False	,  __UpperCAmelCase					:						Optional[int]=dtypes.floataa	,  __UpperCAmelCase					:						Optional[int]=True	,  __UpperCAmelCase					:						Tuple=None	,  )						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  random_seed.get_seed(__UpperCAmelCase		)
         # 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__								=  dtypes.as_dtype(__UpperCAmelCase		).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__								=  1_0_0_0_0
               UpperCAmelCase__								=  one_hot
         else:
               assert (
                   images.shape[0] == labels.shape[0]
               ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
               UpperCAmelCase__								=  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__								=  images.reshape(
                         images.shape[0]	,  images.shape[1] * images.shape[2]		)
               if dtype == dtypes.floataa:
                     # Convert from [0, 255] -> [0.0, 1.0].
                     UpperCAmelCase__								=  images.astype(numpy.floataa		)
                     UpperCAmelCase__								=  numpy.multiply(__UpperCAmelCase	,  1.0 / 255.0		)
         UpperCAmelCase__								=  images
         UpperCAmelCase__								=  labels
         UpperCAmelCase__								=  0
         UpperCAmelCase__								=  0


   @property
   def       lowercase_				(self					:						Tuple		)						->    str:
         """simple docstring"""



         return self._images


   @property
   def       lowercase_				(self					:						int		)						->    Tuple:
         """simple docstring"""



         return self._labels


   @property
   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         return self._num_examples


   @property
   def       lowercase_				(self					:						Any		)						->    str:
         """simple docstring"""



         return self._epochs_completed





   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Optional[int]=False	,  __UpperCAmelCase					:						Union[str, Any]=True		)						->    Optional[int]:
         """simple docstring"""



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




@deprecated(__A,  "Please write your own downloading logic."							)
def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Tuple:







      '''simple docstring'''


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







@deprecated(
    __A,  "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"							)
def        lowerCAmelCase_			(					__A,  __A=False,  __A=False,  __A=dtypes.floataa,  __A=True,  __A=5_000,  __A=None,  __A=DEFAULT_SOURCE_URL,  )					->    Dict:







      '''simple docstring'''


      if fake_data:

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

            UpperCAmelCase__								=  fake()
            UpperCAmelCase__								=  fake()
            UpperCAmelCase__								=  fake()
            return _Datasets(train=__A,  validation=__A,  test=__A							)

      if not source_url:  # empty string check
            UpperCAmelCase__								=  DEFAULT_SOURCE_URL

      UpperCAmelCase__								=  "train-images-idx3-ubyte.gz"
      UpperCAmelCase__								=  "train-labels-idx1-ubyte.gz"
      UpperCAmelCase__								=  "t10k-images-idx3-ubyte.gz"
      UpperCAmelCase__								=  "t10k-labels-idx1-ubyte.gz"

      UpperCAmelCase__								=  _maybe_download(
          __A,  __A,  source_url + train_images_file							)
      with gfile.Open(__A,  "rb"							) as f:
            UpperCAmelCase__								=  _extract_images(__A							)

      UpperCAmelCase__								=  _maybe_download(
          __A,  __A,  source_url + train_labels_file							)
      with gfile.Open(__A,  "rb"							) as f:
            UpperCAmelCase__								=  _extract_labels(__A,  one_hot=__A							)

      UpperCAmelCase__								=  _maybe_download(
          __A,  __A,  source_url + test_images_file							)
      with gfile.Open(__A,  "rb"							) as f:
            UpperCAmelCase__								=  _extract_images(__A							)

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

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

      UpperCAmelCase__								=  train_images[:validation_size]
      UpperCAmelCase__								=  train_labels[:validation_size]
      UpperCAmelCase__								=  train_images[validation_size:]
      UpperCAmelCase__								=  train_labels[validation_size:]

      UpperCAmelCase__								=  {"dtype": dtype, "reshape": reshape, "seed": seed}

      UpperCAmelCase__								=  _DataSet(__A,  __A,  **__A							)
      UpperCAmelCase__								=  _DataSet(__A,  __A,  **__A							)
      UpperCAmelCase__								=  _DataSet(__A,  __A,  **__A							)

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




# Lint as: python3
import itertools
import os
import re


UpperCamelCase__    							= re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__    							= re.compile(R'([a-z\d])([A-Z])')

UpperCamelCase__    							= re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__    							= re.compile(R'(_{2,})')

UpperCamelCase__    							= R'^\w+(\.\w+)*$'

UpperCamelCase__    							= R'<>:/\|?*'




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  _uppercase_uppercase_re.sub(r"\1_\2",  __A							)
      UpperCAmelCase__								=  _lowercase_uppercase_re.sub(r"\1_\2",  __A							)
      return name.lower()




def        lowerCAmelCase_			(					__A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  _single_underscore_re.split(__A							)
      UpperCAmelCase__								=  [_multiple_underscores_re.split(__A							) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(__A							) if n != ""							)




def        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      return camelcase_to_snakecase(__A							)




def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[int]:







      '''simple docstring'''


      if os.path.basename(__A							) != name:
            raise ValueError(f"""Should be a dataset name, not a path: {name}"""							)
      if not re.match(_split_re,  __A							):
            raise ValueError(f"""Split name should match '{_split_re}'' but got '{split}'."""							)
      return f"""{filename_prefix_for_name(__A							)}-{split}"""




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None							)					->    str:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      if filetype_suffix:
            prefix += f""".{filetype_suffix}"""
      UpperCAmelCase__								=  os.path.join(__A,  __A							)
      return f"""{filepath}*"""







def        lowerCAmelCase_			(					__A,  __A,  __A,  __A=None,  __A=None							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  filename_prefix_for_split(__A,  __A							)
      UpperCAmelCase__								=  os.path.join(__A,  __A							)

      if shard_lengths:
            UpperCAmelCase__								=  len(__A							)
            UpperCAmelCase__								=  [f"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(__A							)]
            if filetype_suffix:
                  UpperCAmelCase__								=  [filename + f""".{filetype_suffix}""" for filename in filenames]
            return filenames
      else:
            UpperCAmelCase__								=  prefix
            if filetype_suffix:
                  filename += f""".{filetype_suffix}"""
            return [filename]



