




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    :       List[Any]        =							collections.namedtuple("_Datasets", ["train", "validation", "test"])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCamelCase    :       List[Any]        =							"https://storage.googleapis.com/cvdf-datasets/mnist/"

def       a__					(		UpperCAmelCase							:      Optional[Any]      )  ->						Any:
  UpperCAmelCase	:							str						=			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       a__					(		UpperCAmelCase							:      int      )  ->						int:
  print('''Extracting'''    ,			f.name      )
  with gzip.GzipFile(fileobj=UpperCAmelCase      ) as bytestream:
    UpperCAmelCase	:							int						=			_readaa(UpperCAmelCase      )
    if magic != 2_051:
      raise ValueError(
          '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)      )
    UpperCAmelCase	:							Tuple						=			_readaa(UpperCAmelCase      )
    UpperCAmelCase	:							Optional[Any]						=			_readaa(UpperCAmelCase      )
    UpperCAmelCase	:							int						=			_readaa(UpperCAmelCase      )
    UpperCAmelCase	:							List[str]						=			bytestream.read(rows * cols * num_images      )
    UpperCAmelCase	:							Optional[int]						=			numpy.frombuffer(UpperCAmelCase    ,			dtype=numpy.uinta      )
    UpperCAmelCase	:							Optional[Any]						=			data.reshape(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			1      )
    return data

@deprecated(UpperCAmelCase    ,			'''Please use tf.one_hot on tensors.'''      )
def       a__					(		UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      Tuple      )  ->						List[str]:
  UpperCAmelCase	:							int						=			labels_dense.shape[0]
  UpperCAmelCase	:							Any						=			numpy.arange(UpperCAmelCase      ) * num_classes
  UpperCAmelCase	:							List[str]						=			numpy.zeros((num_labels, num_classes)      )
  UpperCAmelCase	:							Dict						=			1
  return labels_one_hot

@deprecated(UpperCAmelCase    ,			'''Please use tf.data to implement this functionality.'''      )
def       a__					(		UpperCAmelCase							:      Union[str, Any]    ,			UpperCAmelCase							:      List[Any]=False    ,			UpperCAmelCase							:      Dict=10      )  ->						Tuple:
  print('''Extracting'''    ,			f.name      )
  with gzip.GzipFile(fileobj=UpperCAmelCase      ) as bytestream:
    UpperCAmelCase	:							List[Any]						=			_readaa(UpperCAmelCase      )
    if magic != 2_049:
      raise ValueError(
          '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)      )
    UpperCAmelCase	:							List[str]						=			_readaa(UpperCAmelCase      )
    UpperCAmelCase	:							Optional[int]						=			bytestream.read(UpperCAmelCase      )
    UpperCAmelCase	:							Dict						=			numpy.frombuffer(UpperCAmelCase    ,			dtype=numpy.uinta      )
    if one_hot:
      return _dense_to_one_hot(UpperCAmelCase    ,			UpperCAmelCase      )
    return labels





class  __UpperCAmelCase					:



   @deprecated(
       __A,		'''Please use alternatives such as official/mnist/_DataSet.py'''
       ''' from tensorflow/models.''',		)
   def __init__(							self							:					Dict,		__A							:					List[Any],		__A							:					int,		__A							:					Optional[int]=False,		__A							:					Tuple=False,		__A							:					List[str]=dtypes.floataa,		__A							:					List[str]=True,		__A							:					Tuple=None,		):
     UpperCAmelCase	:							Optional[Any]						=			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						)
     UpperCAmelCase	:							Optional[Any]						=			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:
       UpperCAmelCase	:							Any						=			1_0_0_0_0
       UpperCAmelCase	:							str						=			one_hot
     else:
       assert (
           images.shape[0] == labels.shape[0]
       ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
       UpperCAmelCase	:							Tuple						=			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	:							Optional[Any]						=			images.reshape(
             images.shape[0],		images.shape[1] * images.shape[2]						)
       if dtype == dtypes.floataa:
         # Convert from [0, 255] -> [0.0, 1.0].
         UpperCAmelCase	:							Tuple						=			images.astype(numpy.floataa						)
         UpperCAmelCase	:							Tuple						=			numpy.multiply(__A,		1.0 / 2_5_5.0						)
     UpperCAmelCase	:							str						=			images
     UpperCAmelCase	:							Union[str, Any]						=			labels
     UpperCAmelCase	:							int						=			0
     UpperCAmelCase	:							Any						=			0



   @property
   def 	__magic_name__   (							self							:					List[str]						):
     return self._images



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



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



   @property
   def 	__magic_name__   (							self							:					List[Any]						):
     return self._epochs_completed





   def 	__magic_name__   (							self							:					List[Any],		__A							:					str,		__A							:					Dict=False,		__A							:					Union[str, Any]=True						):
     if fake_data:
       UpperCAmelCase	:							int						=			[1] * 7_8_4
       UpperCAmelCase	:							Optional[int]						=			[1] + [0] * 9 if self.one_hot else 0
       return (
           [fake_image for _ in range(__A						)],
           [fake_label for _ in range(__A						)],
       )
     UpperCAmelCase	:							Any						=			self._index_in_epoch
     # Shuffle for the first epoch
     if self._epochs_completed == 0 and start == 0 and shuffle:
       UpperCAmelCase	:							Dict						=			numpy.arange(self._num_examples						)
       numpy.random.shuffle(__A						)
       UpperCAmelCase	:							Optional[Any]						=			self.images[perma]
       UpperCAmelCase	:							str						=			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	:							List[str]						=			self._num_examples - start
       UpperCAmelCase	:							Tuple						=			self._images[start : self._num_examples]
       UpperCAmelCase	:							List[str]						=			self._labels[start : self._num_examples]
       # Shuffle the data
       if shuffle:
         UpperCAmelCase	:							Optional[Any]						=			numpy.arange(self._num_examples						)
         numpy.random.shuffle(__A						)
         UpperCAmelCase	:							Any						=			self.images[perm]
         UpperCAmelCase	:							Any						=			self.labels[perm]
       # Start next epoch
       UpperCAmelCase	:							Any						=			0
       UpperCAmelCase	:							List[Any]						=			batch_size - rest_num_examples
       UpperCAmelCase	:							str						=			self._index_in_epoch
       UpperCAmelCase	:							List[Any]						=			self._images[start:end]
       UpperCAmelCase	:							Dict						=			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	:							List[Any]						=			self._index_in_epoch
       return self._images[start:end], self._labels[start:end]






@deprecated(UpperCAmelCase    ,			'''Please write your own downloading logic.'''      )
def       a__					(		UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      List[str]      )  ->						Dict:
  if not gfile.Exists(UpperCAmelCase      ):
    gfile.MakeDirs(UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			os.path.join(UpperCAmelCase    ,			UpperCAmelCase      )
  if not gfile.Exists(UpperCAmelCase      ):
    urllib.request.urlretrieve(UpperCAmelCase    ,			UpperCAmelCase      )  # noqa: S310
    with gfile.GFile(UpperCAmelCase      ) as f:
      UpperCAmelCase	:							List[str]						=			f.size()
    print('''Successfully downloaded'''    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			'''bytes.'''      )
  return filepath






@deprecated(
    UpperCAmelCase    ,			'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''      )
def       a__					(		UpperCAmelCase							:      Union[str, Any]    ,			UpperCAmelCase							:      str=False    ,			UpperCAmelCase							:      Tuple=False    ,			UpperCAmelCase							:      Union[str, Any]=dtypes.floataa    ,			UpperCAmelCase							:      Union[str, Any]=True    ,			UpperCAmelCase							:      List[Any]=5_000    ,			UpperCAmelCase							:      Optional[Any]=None    ,			UpperCAmelCase							:      Optional[Any]=DEFAULT_SOURCE_URL    ,			)  ->						List[Any]:
  if fake_data:

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

    UpperCAmelCase	:							Tuple						=			fake()
    UpperCAmelCase	:							int						=			fake()
    UpperCAmelCase	:							Union[str, Any]						=			fake()
    return _Datasets(train=UpperCAmelCase    ,			validation=UpperCAmelCase    ,			test=UpperCAmelCase      )

  if not source_url:  # empty string check
    UpperCAmelCase	:							Union[str, Any]						=			DEFAULT_SOURCE_URL

  UpperCAmelCase	:							Optional[int]						=			'''train-images-idx3-ubyte.gz'''
  UpperCAmelCase	:							str						=			'''train-labels-idx1-ubyte.gz'''
  UpperCAmelCase	:							List[Any]						=			'''t10k-images-idx3-ubyte.gz'''
  UpperCAmelCase	:							Optional[int]						=			'''t10k-labels-idx1-ubyte.gz'''

  UpperCAmelCase	:							Any						=			_maybe_download(
      UpperCAmelCase    ,			UpperCAmelCase    ,			source_url + train_images_file      )
  with gfile.Open(UpperCAmelCase    ,			'''rb'''      ) as f:
    UpperCAmelCase	:							Any						=			_extract_images(UpperCAmelCase      )

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

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

  UpperCAmelCase	:							Dict						=			_maybe_download(
      UpperCAmelCase    ,			UpperCAmelCase    ,			source_url + test_labels_file      )
  with gfile.Open(UpperCAmelCase    ,			'''rb'''      ) as f:
    UpperCAmelCase	:							Optional[int]						=			_extract_labels(UpperCAmelCase    ,			one_hot=UpperCAmelCase      )

  if not 0 <= validation_size <= len(UpperCAmelCase      ):
    UpperCAmelCase	:							List[str]						=			(
        '''Validation size should be between 0 and '''
        f'''{len(UpperCAmelCase      )}. Received: {validation_size}.'''
    )
    raise ValueError(UpperCAmelCase      )

  UpperCAmelCase	:							Dict						=			train_images[:validation_size]
  UpperCAmelCase	:							Optional[Any]						=			train_labels[:validation_size]
  UpperCAmelCase	:							Union[str, Any]						=			train_images[validation_size:]
  UpperCAmelCase	:							Optional[int]						=			train_labels[validation_size:]

  UpperCAmelCase	:							Tuple						=			{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

  UpperCAmelCase	:							int						=			_DataSet(UpperCAmelCase    ,			UpperCAmelCase    ,			**UpperCAmelCase      )
  UpperCAmelCase	:							List[str]						=			_DataSet(UpperCAmelCase    ,			UpperCAmelCase    ,			**UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			_DataSet(UpperCAmelCase    ,			UpperCAmelCase    ,			**UpperCAmelCase      )

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








import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )





