




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

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



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Optional[Any]:
  lowerCAmelCase__				:  int		       =     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      )     ->	List[str]:
  print('''Extracting'''	,      f.name      )
  with gzip.GzipFile(fileobj=UpperCamelCase      ) as bytestream:
    lowerCAmelCase__				:  Dict		       =     _readaa(UpperCamelCase      )
    if magic != 2051:
      raise ValueError(
          '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)      )
    lowerCAmelCase__				:  Optional[Any]		       =     _readaa(UpperCamelCase      )
    lowerCAmelCase__				:  Union[str, Any]		       =     _readaa(UpperCamelCase      )
    lowerCAmelCase__				:  List[Any]		       =     _readaa(UpperCamelCase      )
    lowerCAmelCase__				:  Dict		       =     bytestream.read(rows * cols * num_images      )
    lowerCAmelCase__				:  Any		       =     numpy.frombuffer(UpperCamelCase	,      dtype=numpy.uinta      )
    lowerCAmelCase__				:  List[str]		       =     data.reshape(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      1      )
    return data



@deprecated(UpperCamelCase	,      '''Please use tf.one_hot on tensors.'''      )
def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	Optional[int]:
  lowerCAmelCase__				:  Optional[int]		       =     labels_dense.shape[0]
  lowerCAmelCase__				:  Optional[int]		       =     numpy.arange(UpperCamelCase      ) * num_classes
  lowerCAmelCase__				:  str		       =     numpy.zeros((num_labels, num_classes)      )
  lowerCAmelCase__				:  Optional[Any]		       =     1
  return labels_one_hot



@deprecated(UpperCamelCase	,      '''Please use tf.data to implement this functionality.'''      )
def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase=False	,      UpperCamelCase=10      )     ->	Dict:
  print('''Extracting'''	,      f.name      )
  with gzip.GzipFile(fileobj=UpperCamelCase      ) as bytestream:
    lowerCAmelCase__				:  Optional[Any]		       =     _readaa(UpperCamelCase      )
    if magic != 2049:
      raise ValueError(
          '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)      )
    lowerCAmelCase__				:  Dict		       =     _readaa(UpperCamelCase      )
    lowerCAmelCase__				:  List[Any]		       =     bytestream.read(UpperCamelCase      )
    lowerCAmelCase__				:  Optional[Any]		       =     numpy.frombuffer(UpperCamelCase	,      dtype=numpy.uinta      )
    if one_hot:
      return _dense_to_one_hot(UpperCamelCase	,      UpperCamelCase      )
    return labels






class  _lowerCAmelCase     :

      @deprecated(
          __UpperCAmelCase      ,				'''Please use alternatives such as official/mnist/_DataSet.py'''
          ''' from tensorflow/models.'''      ,				)
      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=dtypes.floataa      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=None      ,				):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     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		)
        lowerCAmelCase__				:  Any		       =     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:
          lowerCAmelCase__				:  int		       =     1_0000
          lowerCAmelCase__				:  Any		       =     one_hot
        else:
          assert (
              images.shape[0] == labels.shape[0]
          ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
          lowerCAmelCase__				:  List[Any]		       =     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__				:  List[str]		       =     images.reshape(
                images.shape[0]      ,				images.shape[1] * images.shape[2]		)
          if dtype == dtypes.floataa:
            # Convert from [0, 255] -> [0.0, 1.0].
            lowerCAmelCase__				:  Optional[Any]		       =     images.astype(numpy.floataa		)
            lowerCAmelCase__				:  Optional[Any]		       =     numpy.multiply(__UpperCAmelCase      ,				1.0 / 255.0		)
        lowerCAmelCase__				:  int		       =     images
        lowerCAmelCase__				:  Optional[int]		       =     labels
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Optional[Any]		       =     0

      @property
      def 	__magic_name__(   self		):
        return self._images

      @property
      def 	__magic_name__(   self		):
        return self._labels

      @property
      def 	__magic_name__(   self		):
        return self._num_examples

      @property
      def 	__magic_name__(   self		):
        return self._epochs_completed







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=True		):
        if fake_data:
          lowerCAmelCase__				:  Dict		       =     [1] * 784
          lowerCAmelCase__				:  Optional[Any]		       =     [1] + [0] * 9 if self.one_hot else 0
          return (
              [fake_image for _ in range(__UpperCAmelCase		)],
              [fake_label for _ in range(__UpperCAmelCase		)],
          )
        lowerCAmelCase__				:  Tuple		       =     self._index_in_epoch
        # Shuffle for the first epoch
        if self._epochs_completed == 0 and start == 0 and shuffle:
          lowerCAmelCase__				:  Tuple		       =     numpy.arange(self._num_examples		)
          numpy.random.shuffle(__UpperCAmelCase		)
          lowerCAmelCase__				:  Optional[int]		       =     self.images[perma]
          lowerCAmelCase__				:  Union[str, Any]		       =     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__				:  Optional[Any]		       =     self._num_examples - start
          lowerCAmelCase__				:  Tuple		       =     self._images[start : self._num_examples]
          lowerCAmelCase__				:  Optional[int]		       =     self._labels[start : self._num_examples]
          # Shuffle the data
          if shuffle:
            lowerCAmelCase__				:  Optional[Any]		       =     numpy.arange(self._num_examples		)
            numpy.random.shuffle(__UpperCAmelCase		)
            lowerCAmelCase__				:  Union[str, Any]		       =     self.images[perm]
            lowerCAmelCase__				:  List[Any]		       =     self.labels[perm]
          # Start next epoch
          lowerCAmelCase__				:  Dict		       =     0
          lowerCAmelCase__				:  Any		       =     batch_size - rest_num_examples
          lowerCAmelCase__				:  Union[str, Any]		       =     self._index_in_epoch
          lowerCAmelCase__				:  str		       =     self._images[start:end]
          lowerCAmelCase__				:  List[str]		       =     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__				:  Tuple		       =     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      )     ->	Tuple:
  if not gfile.Exists(UpperCamelCase      ):
    gfile.MakeDirs(UpperCamelCase      )
  lowerCAmelCase__				:  List[str]		       =     os.path.join(UpperCamelCase	,      UpperCamelCase      )
  if not gfile.Exists(UpperCamelCase      ):
    urllib.request.urlretrieve(UpperCamelCase	,      UpperCamelCase      )  # noqa: S310
    with gfile.GFile(UpperCamelCase      ) as f:
      lowerCAmelCase__				:  str		       =     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=5000	,      UpperCamelCase=None	,      UpperCamelCase=DEFAULT_SOURCE_URL	,      )     ->	List[Any]:
  if fake_data:

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

    lowerCAmelCase__				:  int		       =     fake()
    lowerCAmelCase__				:  List[Any]		       =     fake()
    lowerCAmelCase__				:  List[str]		       =     fake()
    return _Datasets(train=UpperCamelCase	,      validation=UpperCamelCase	,      test=UpperCamelCase      )

  if not source_url:  # empty string check
    lowerCAmelCase__				:  Any		       =     DEFAULT_SOURCE_URL

  lowerCAmelCase__				:  List[str]		       =     '''train-images-idx3-ubyte.gz'''
  lowerCAmelCase__				:  Union[str, Any]		       =     '''train-labels-idx1-ubyte.gz'''
  lowerCAmelCase__				:  Dict		       =     '''t10k-images-idx3-ubyte.gz'''
  lowerCAmelCase__				:  Union[str, Any]		       =     '''t10k-labels-idx1-ubyte.gz'''

  lowerCAmelCase__				:  Optional[Any]		       =     _maybe_download(
      UpperCamelCase	,      UpperCamelCase	,      source_url + train_images_file      )
  with gfile.Open(UpperCamelCase	,      '''rb'''      ) as f:
    lowerCAmelCase__				:  Union[str, Any]		       =     _extract_images(UpperCamelCase      )

  lowerCAmelCase__				:  List[str]		       =     _maybe_download(
      UpperCamelCase	,      UpperCamelCase	,      source_url + train_labels_file      )
  with gfile.Open(UpperCamelCase	,      '''rb'''      ) as f:
    lowerCAmelCase__				:  int		       =     _extract_labels(UpperCamelCase	,      one_hot=UpperCamelCase      )

  lowerCAmelCase__				:  Optional[Any]		       =     _maybe_download(
      UpperCamelCase	,      UpperCamelCase	,      source_url + test_images_file      )
  with gfile.Open(UpperCamelCase	,      '''rb'''      ) as f:
    lowerCAmelCase__				:  int		       =     _extract_images(UpperCamelCase      )

  lowerCAmelCase__				:  Union[str, Any]		       =     _maybe_download(
      UpperCamelCase	,      UpperCamelCase	,      source_url + test_labels_file      )
  with gfile.Open(UpperCamelCase	,      '''rb'''      ) as f:
    lowerCAmelCase__				:  Optional[Any]		       =     _extract_labels(UpperCamelCase	,      one_hot=UpperCamelCase      )

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

  lowerCAmelCase__				:  Union[str, Any]		       =     train_images[:validation_size]
  lowerCAmelCase__				:  int		       =     train_labels[:validation_size]
  lowerCAmelCase__				:  List[str]		       =     train_images[validation_size:]
  lowerCAmelCase__				:  int		       =     train_labels[validation_size:]

  lowerCAmelCase__				:  Dict		       =     {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

  lowerCAmelCase__				:  Union[str, Any]		       =     _DataSet(UpperCamelCase	,      UpperCamelCase	,      **UpperCamelCase      )
  lowerCAmelCase__				:  Tuple		       =     _DataSet(UpperCamelCase	,      UpperCamelCase	,      **UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     _DataSet(UpperCamelCase	,      UpperCamelCase	,      **UpperCamelCase      )

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







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
          # modified it, it will not be accepted here again, since `auto` values would have been overridden
          lowerCAmelCase__				:  Union[str, Any]		       =     deepcopy(__UpperCAmelCase		)
        elif os.path.exists(__UpperCAmelCase		):
          with io.open(__UpperCAmelCase      ,				'''r'''      ,				encoding='''utf-8'''		) as f:
            lowerCAmelCase__				:  int		       =     json.load(__UpperCAmelCase		)
        else:
          try:
            lowerCAmelCase__				:  Union[str, Any]		       =     baseaa.urlsafe_baadecode(__UpperCAmelCase		).decode('''utf-8'''		)
            lowerCAmelCase__				:  Optional[int]		       =     json.loads(__UpperCAmelCase		)
          except (UnicodeDecodeError, AttributeError, ValueError):
            raise ValueError(
                f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""		)

        lowerCAmelCase__				:  Optional[Any]		       =     config

        self.set_stage_and_offload()

      def 	__magic_name__(   self		):
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value('''zero_optimization.stage'''      ,				-1		)

        # offload
        lowerCAmelCase__				:  Dict		       =     False
        if self.is_zeroa() or self.is_zeroa():
          lowerCAmelCase__				:  Dict		       =     set(['''cpu''', '''nvme''']		)
          lowerCAmelCase__				:  Optional[Any]		       =     set(
              [
                  self.get_value('''zero_optimization.offload_optimizer.device'''		),
                  self.get_value('''zero_optimization.offload_param.device'''		),
              ]		)
          if len(offload_devices & offload_devices_valid		) > 0:
            lowerCAmelCase__				:  List[Any]		       =     True

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  Tuple		       =     ds_key_long.split('''.'''		)
        lowerCAmelCase__				:  int		       =     nodes.pop()
        for node in nodes:
          lowerCAmelCase__				:  int		       =     config.get(__UpperCAmelCase		)
          if config is None:
            return None, ds_key

        return config, ds_key

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.find_config_node(__UpperCAmelCase		)
        if config is None:
          return default
        return config.get(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False		):
        lowerCAmelCase__				:  Dict		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  str		       =     ds_key_long.split('''.'''		)
        for node in nodes:
          lowerCAmelCase__				:  Dict		       =     config
          lowerCAmelCase__				:  List[str]		       =     config.get(__UpperCAmelCase		)
          if config is None:
            if must_exist:
              raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""		)
            else:
              return

        # if found remove it
        if parent_config is not None:
          parent_config.pop(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else bool(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else not bool(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        return self._stage == 2

      def 	__magic_name__(   self		):
        return self._stage == 3







      def 	__magic_name__(   self		):
        return self._offload






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     engine







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        # runs backpropagation and handles mixed precision
        self.engine.backward(__UpperCAmelCase      ,				**__UpperCAmelCase		)

        # Deepspeed's `engine.step` performs the following operations:
        # - gradient accumulation check
        # - gradient clipping
        # - optimizer step
        # - zero grad
        # - checking overflow
        # - lr_scheduler step (only if engine.lr_scheduler is not None)
        self.engine.step()
        # and this plugin overrides the above calls with no-ops when Accelerate runs under
        # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
        # training loop that works transparently under many training regimes.






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				device_placement=__UpperCAmelCase      ,				scaler=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     hasattr(self.optimizer      ,				'''overflow'''		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







      @property
      def 	__magic_name__(   self		):
        if self.__has_overflow__:
          return self.optimizer.overflow
        return False






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=0.001      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     params
        lowerCAmelCase__				:  str		       =     lr
        lowerCAmelCase__				:  List[str]		       =     weight_decay
        lowerCAmelCase__				:  Dict		       =     kwargs






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     optimizer
        lowerCAmelCase__				:  Optional[Any]		       =     total_num_steps
        lowerCAmelCase__				:  str		       =     warmup_num_steps
        lowerCAmelCase__				:  Dict		       =     kwargs

