

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

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict				)      ->			str:
     """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        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]				)      ->			List[Any]:
     """simple docstring"""




     print('Extracting'							, f.name				)
     with gzip.GzipFile(fileobj=_UpperCamelCase				) as bytestream:
          _SCREAMING_SNAKE_CASE							    =_readaa(_UpperCamelCase				)
          if magic != 20_51:
               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        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			Optional[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        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Tuple=False							, _UpperCamelCase						:     Tuple=10				)      ->			str:
     """simple docstring"""




     print('Extracting'							, f.name				)
     with gzip.GzipFile(fileobj=_UpperCamelCase				) as bytestream:
          _SCREAMING_SNAKE_CASE							    =_readaa(_UpperCamelCase				)
          if magic != 20_49:
               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(
      _a							,					'Please use alternatives such as official/mnist/_DataSet.py'
      ' from tensorflow/models.'							,					)
  def __init__(		self      :	Any							,					_a      :	Union[str, Any]							,					_a      :	Optional[int]							,					_a      :	Dict=False							,					_a      :	Tuple=False							,					_a      :	List[Any]=dtypes.floataa							,					_a      :	Optional[int]=True							,					_a      :	List[Any]=None							,					)	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =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 )
       _SCREAMING_SNAKE_CASE							    =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:
            _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(_a							,					1.0 / 2_55.0 )
       _SCREAMING_SNAKE_CASE							    =images
       _SCREAMING_SNAKE_CASE							    =labels
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =0





  @property
  def 						A  (		self      :	Union[str, Any] )	->    Dict:





       '''simple docstring'''






       return self._images





  @property
  def 						A  (		self      :	str )	->    List[str]:





       '''simple docstring'''






       return self._labels





  @property
  def 						A  (		self      :	List[Any] )	->    Union[str, Any]:





       '''simple docstring'''






       return self._num_examples





  @property
  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       return self._epochs_completed





  def 						A  (		self      :	Dict							,					_a      :	Dict							,					_a      :	Dict=False							,					_a      :	Tuple=True )	->    Optional[Any]:





       '''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(_a )],
                [fake_label for _ in range(_a )],
            )
       _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(_a )
            _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(_a )
                 _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        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     List[Any]				)      ->			int:
     """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        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     str=False							, _UpperCamelCase						:     str=False							, _UpperCamelCase						:     Any=dtypes.floataa							, _UpperCamelCase						:     Any=True							, _UpperCamelCase						:     Any=50_00							, _UpperCamelCase						:     Optional[int]=None							, _UpperCamelCase						:     List[Any]=DEFAULT_SOURCE_URL							, )      ->			Optional[Any]:
     """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'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      int					 =     {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[Any]					 =     ["MBartTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Dict					 =     ["MBartTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
         "MBartForConditionalGeneration",
         "MBartForQuestionAnswering",
         "MBartForSequenceClassification",
         "MBartModel",
         "MBartPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
         "FlaxMBartForSequenceClassification",
         "FlaxMBartModel",
         "FlaxMBartPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
