





'''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

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

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

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   print("""Extracting"""							,	f.name      )
   with gzip.GzipFile(fileobj=__UpperCAmelCase      ) as bytestream:
      __SCREAMING_SNAKE_CASE      							=       _readaa(__UpperCAmelCase      )
      if magic != 2051:
         raise ValueError(
             """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)      )
      __SCREAMING_SNAKE_CASE      							=       _readaa(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       _readaa(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       _readaa(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       bytestream.read(rows * cols * num_images      )
      __SCREAMING_SNAKE_CASE      							=       numpy.frombuffer(__UpperCAmelCase							,	dtype=numpy.uinta      )
      __SCREAMING_SNAKE_CASE      							=       data.reshape(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	1      )
      return data

@deprecated(__UpperCAmelCase							,	"""Please use tf.one_hot on tensors."""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Any:

   '''simple docstring'''







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

@deprecated(__UpperCAmelCase							,	"""Please use tf.data to implement this functionality."""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=False							,	__UpperCAmelCase=10      )						->					Optional[Any]:

   '''simple docstring'''







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







class __a :





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       random_seed.get_seed(lowerCamelCase    )
      # If op level seed is not set, use whatever graph level seed is returned
      numpy.random.seed(seeda if seed is None else seeda    )
      __SCREAMING_SNAKE_CASE      							=       dtypes.as_dtype(lowerCamelCase    ).base_dtype
      if dtype not in (dtypes.uinta, dtypes.floataa):
         raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype    )
      if fake_data:
         __SCREAMING_SNAKE_CASE      							=       1_0000
         __SCREAMING_SNAKE_CASE      							=       one_hot
      else:
         assert (
             images.shape[0] == labels.shape[0]
         ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
         __SCREAMING_SNAKE_CASE      							=       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
            __SCREAMING_SNAKE_CASE      							=       images.reshape(
                images.shape[0]				,images.shape[1] * images.shape[2]    )
         if dtype == dtypes.floataa:
            # Convert from [0, 255] -> [0.0, 1.0].
            __SCREAMING_SNAKE_CASE      							=       images.astype(numpy.floataa    )
            __SCREAMING_SNAKE_CASE      							=       numpy.multiply(lowerCamelCase				,1.0 / 255.0    )
      __SCREAMING_SNAKE_CASE      							=       images
      __SCREAMING_SNAKE_CASE      							=       labels
      __SCREAMING_SNAKE_CASE      							=       0
      __SCREAMING_SNAKE_CASE      							=       0





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

      '''simple docstring'''

      return self._images





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self._labels





   @property
   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      return self._num_examples





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

      '''simple docstring'''

      return self._epochs_completed





   def      UpperCAmelCase__     (					self				:			int				,lowerCamelCase				:			List[str]				,lowerCamelCase				:			Any=False				,lowerCamelCase				:			Dict=True    ):

      '''simple docstring'''

      if fake_data:
         __SCREAMING_SNAKE_CASE      							=       [1] * 784
         __SCREAMING_SNAKE_CASE      							=       [1] + [0] * 9 if self.one_hot else 0
         return (
             [fake_image for _ in range(lowerCamelCase    )],
             [fake_label for _ in range(lowerCamelCase    )],
         )
      __SCREAMING_SNAKE_CASE      							=       self._index_in_epoch
      # Shuffle for the first epoch
      if self._epochs_completed == 0 and start == 0 and shuffle:
         __SCREAMING_SNAKE_CASE      							=       numpy.arange(self._num_examples    )
         numpy.random.shuffle(lowerCamelCase    )
         __SCREAMING_SNAKE_CASE      							=       self.images[perma]
         __SCREAMING_SNAKE_CASE      							=       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
         __SCREAMING_SNAKE_CASE      							=       self._num_examples - start
         __SCREAMING_SNAKE_CASE      							=       self._images[start : self._num_examples]
         __SCREAMING_SNAKE_CASE      							=       self._labels[start : self._num_examples]
         # Shuffle the data
         if shuffle:
            __SCREAMING_SNAKE_CASE      							=       numpy.arange(self._num_examples    )
            numpy.random.shuffle(lowerCamelCase    )
            __SCREAMING_SNAKE_CASE      							=       self.images[perm]
            __SCREAMING_SNAKE_CASE      							=       self.labels[perm]
         # Start next epoch
         __SCREAMING_SNAKE_CASE      							=       0
         __SCREAMING_SNAKE_CASE      							=       batch_size - rest_num_examples
         __SCREAMING_SNAKE_CASE      							=       self._index_in_epoch
         __SCREAMING_SNAKE_CASE      							=       self._images[start:end]
         __SCREAMING_SNAKE_CASE      							=       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
         __SCREAMING_SNAKE_CASE      							=       self._index_in_epoch
         return self._images[start:end], self._labels[start:end]





@deprecated(__UpperCAmelCase							,	"""Please write your own downloading logic."""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







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







@deprecated(
    __UpperCAmelCase							,	"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=False							,	__UpperCAmelCase=False							,	__UpperCAmelCase=dtypes.floataa							,	__UpperCAmelCase=True							,	__UpperCAmelCase=5000							,	__UpperCAmelCase=None							,	__UpperCAmelCase=DEFAULT_SOURCE_URL							,	)						->					int:

   '''simple docstring'''







   if fake_data:

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

      __SCREAMING_SNAKE_CASE      							=       fake()
      __SCREAMING_SNAKE_CASE      							=       fake()
      __SCREAMING_SNAKE_CASE      							=       fake()
      return _Datasets(train=__UpperCAmelCase							,	validation=__UpperCAmelCase							,	test=__UpperCAmelCase      )

   if not source_url:  # empty string check
      __SCREAMING_SNAKE_CASE      							=       DEFAULT_SOURCE_URL

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

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

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

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

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

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

   __SCREAMING_SNAKE_CASE      							=       train_images[:validation_size]
   __SCREAMING_SNAKE_CASE      							=       train_labels[:validation_size]
   __SCREAMING_SNAKE_CASE      							=       train_images[validation_size:]
   __SCREAMING_SNAKE_CASE      							=       train_labels[validation_size:]

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

   __SCREAMING_SNAKE_CASE      							=       _DataSet(__UpperCAmelCase							,	__UpperCAmelCase							,	**__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       _DataSet(__UpperCAmelCase							,	__UpperCAmelCase							,	**__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       _DataSet(__UpperCAmelCase							,	__UpperCAmelCase							,	**__UpperCAmelCase      )

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












'''simple docstring'''

import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a        =						"__DUMMY_TRANSFORMERS_USER__"
a        =						"Dummy User"
a        =						"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"

a        =						"https://hub-ci.huggingface.co"
a        =						CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
a        =						CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
a        =						Path("~/.huggingface/hub_ci_token").expanduser()

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   monkeypatch.setattr(
       """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""							,	__UpperCAmelCase      )
   monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   HfFolder.save_token(__UpperCAmelCase      )
   yield
   HfFolder.delete_token()

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							)						->					Optional[Any]:

   '''simple docstring'''







   return HfApi(endpoint=__UpperCAmelCase      )

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       HfFolder.get_token()
   HfFolder.save_token(__UpperCAmelCase      )
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   def _cleanup_repo(__UpperCAmelCase      ):
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )

   return _cleanup_repo

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   @contextmanager
   def _temporary_repo(__UpperCAmelCase      ):
      try:
         yield repo_id
      finally:
         cleanup_repo(__UpperCAmelCase      )

   return _temporary_repo

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data/text_data.txt"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass

@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   return hf_private_dataset_repo_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data.zip"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass

@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_img_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data.zip"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass







@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_img_data_





