



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 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    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 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					print("""Extracting"""	,						f.name			)
					with gzip.GzipFile(fileobj=snake_case			) as bytestream:
										_lowerCAmelCase						=    _readaa(snake_case			)
										if magic != 20_51:
															raise ValueError(
															    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)			)
										_lowerCAmelCase						=    _readaa(snake_case			)
										_lowerCAmelCase						=    _readaa(snake_case			)
										_lowerCAmelCase						=    _readaa(snake_case			)
										_lowerCAmelCase						=    bytestream.read(rows * cols * num_images			)
										_lowerCAmelCase						=    numpy.frombuffer(snake_case	,						dtype=numpy.uinta			)
										_lowerCAmelCase						=    data.reshape(snake_case	,						snake_case	,						snake_case	,						1			)
										return data


@deprecated(snake_case	,						"""Please use tf.one_hot on tensors."""			)
def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

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


@deprecated(snake_case	,						"""Please use tf.data to implement this functionality."""			)
def 		_UpperCAmelCase				(      snake_case	,						snake_case=False	,						snake_case=10			):





					"""simple docstring"""

					print("""Extracting"""	,						f.name			)
					with gzip.GzipFile(fileobj=snake_case			) as bytestream:
										_lowerCAmelCase						=    _readaa(snake_case			)
										if magic != 20_49:
															raise ValueError(
															    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)			)
										_lowerCAmelCase						=    _readaa(snake_case			)
										_lowerCAmelCase						=    bytestream.read(snake_case			)
										_lowerCAmelCase						=    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						,				_snake_case						,				_snake_case						,				_snake_case=False						,				_snake_case=False						,				_snake_case=dtypes.floataa						,				_snake_case=True						,				_snake_case=None						,				):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    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		)
									_lowerCAmelCase						=    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:
														_lowerCAmelCase						=    10000
														_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(_snake_case						,				1.0 / 255.0		)
									_lowerCAmelCase						=    images
									_lowerCAmelCase						=    labels
									_lowerCAmelCase						=    0
									_lowerCAmelCase						=    0




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self._images




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self._labels




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self._num_examples




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self._epochs_completed




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=False						,				_snake_case=True		):



									"""simple docstring"""





									if fake_data:
														_lowerCAmelCase						=    [1] * 784
														_lowerCAmelCase						=    [1] + [0] * 9 if self.one_hot else 0
														return (
														    [fake_image for _ in range(_snake_case		)],
														    [fake_label for _ in range(_snake_case		)],
														)
									_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(_snake_case		)
														_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(_snake_case		)
																			_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(snake_case	,						"""Please write your own downloading logic."""			)
def 		_UpperCAmelCase				(      snake_case	,						snake_case	,						snake_case			):





					"""simple docstring"""

					if not gfile.Exists(snake_case			):
										gfile.MakeDirs(snake_case			)
					_lowerCAmelCase						=    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:
															_lowerCAmelCase						=    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 		_UpperCAmelCase				(      snake_case	,						snake_case=False	,						snake_case=False	,						snake_case=dtypes.floataa	,						snake_case=True	,						snake_case=50_00	,						snake_case=None	,						snake_case=DEFAULT_SOURCE_URL	,						):





					"""simple docstring"""

					if fake_data:

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

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

					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(
					    snake_case	,						snake_case	,						source_url + train_images_file			)
					with gfile.Open(snake_case	,						"""rb"""			) as f:
										_lowerCAmelCase						=    _extract_images(snake_case			)

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

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

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

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

					_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(snake_case	,						snake_case	,						**snake_case			)
					_lowerCAmelCase						=    _DataSet(snake_case	,						snake_case	,						**snake_case			)
					_lowerCAmelCase						=    _DataSet(snake_case	,						snake_case	,						**snake_case			)

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)