



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 		a__			(		_UpperCamelCase			: Optional[Any]							):
   __lowerCamelCase													=    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			: str							):
   print('''Extracting''' ,f.name							)
   with gzip.GzipFile(fileobj=_UpperCamelCase							) as bytestream:
      __lowerCamelCase													=    _readaa(_UpperCamelCase							)
      if magic != 20_51:
         raise ValueError(
             '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)							)
      __lowerCamelCase													=    _readaa(_UpperCamelCase							)
      __lowerCamelCase													=    _readaa(_UpperCamelCase							)
      __lowerCamelCase													=    _readaa(_UpperCamelCase							)
      __lowerCamelCase													=    bytestream.read(rows * cols * num_images							)
      __lowerCamelCase													=    numpy.frombuffer(_UpperCamelCase ,dtype=numpy.uinta							)
      __lowerCamelCase													=    data.reshape(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,1							)
      return data





@deprecated(_UpperCamelCase ,'''Please use tf.one_hot on tensors.'''							)
def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    labels_dense.shape[0]
   __lowerCamelCase													=    numpy.arange(_UpperCamelCase							) * num_classes
   __lowerCamelCase													=    numpy.zeros((num_labels, num_classes)							)
   __lowerCamelCase													=    1
   return labels_one_hot





@deprecated(_UpperCamelCase ,'''Please use tf.data to implement this functionality.'''							)
def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Dict=False ,_UpperCamelCase			: Any=10							):
   print('''Extracting''' ,f.name							)
   with gzip.GzipFile(fileobj=_UpperCamelCase							) as bytestream:
      __lowerCamelCase													=    _readaa(_UpperCamelCase							)
      if magic != 20_49:
         raise ValueError(
             '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)							)
      __lowerCamelCase													=    _readaa(_UpperCamelCase							)
      __lowerCamelCase													=    bytestream.read(_UpperCamelCase							)
      __lowerCamelCase													=    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	,				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    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													=    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													=    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(__UpperCAmelCase	,				1.0 / 255.0				)
    __lowerCamelCase													=    images
    __lowerCamelCase													=    labels
    __lowerCamelCase													=    0
    __lowerCamelCase													=    0




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._images




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._labels




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._num_examples




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self._epochs_completed




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=False	,				__UpperCAmelCase=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(__UpperCAmelCase				)],
           [fake_label for _ in range(__UpperCAmelCase				)],
       )
    __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(__UpperCAmelCase				)
       __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(__UpperCAmelCase				)
          __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(_UpperCamelCase ,'''Please write your own downloading logic.'''							)
def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Optional[Any]							):
   if not gfile.Exists(_UpperCamelCase							):
      gfile.MakeDirs(_UpperCamelCase							)
   __lowerCamelCase													=    os.path.join(_UpperCamelCase ,_UpperCamelCase							)
   if not gfile.Exists(_UpperCamelCase							):
      urllib.request.urlretrieve(_UpperCamelCase ,_UpperCamelCase							)  # noqa: S310
      with gfile.GFile(_UpperCamelCase							) as f:
         __lowerCamelCase													=    f.size()
      print('''Successfully downloaded''' ,_UpperCamelCase ,_UpperCamelCase ,'''bytes.'''							)
   return filepath





@deprecated(
    _UpperCamelCase ,'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''							)
def 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: List[Any]=False ,_UpperCamelCase			: Tuple=False ,_UpperCamelCase			: List[str]=dtypes.floataa ,_UpperCamelCase			: Tuple=True ,_UpperCamelCase			: Tuple=50_00 ,_UpperCamelCase			: str=None ,_UpperCamelCase			: Optional[Any]=DEFAULT_SOURCE_URL ,):
   if fake_data:

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

      __lowerCamelCase													=    fake()
      __lowerCamelCase													=    fake()
      __lowerCamelCase													=    fake()
      return _Datasets(train=_UpperCamelCase ,validation=_UpperCamelCase ,test=_UpperCamelCase							)

   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(
       _UpperCamelCase ,_UpperCamelCase ,source_url + train_images_file							)
   with gfile.Open(_UpperCamelCase ,'''rb'''							) as f:
      __lowerCamelCase													=    _extract_images(_UpperCamelCase							)

   __lowerCamelCase													=    _maybe_download(
       _UpperCamelCase ,_UpperCamelCase ,source_url + train_labels_file							)
   with gfile.Open(_UpperCamelCase ,'''rb'''							) as f:
      __lowerCamelCase													=    _extract_labels(_UpperCamelCase ,one_hot=_UpperCamelCase							)

   __lowerCamelCase													=    _maybe_download(
       _UpperCamelCase ,_UpperCamelCase ,source_url + test_images_file							)
   with gfile.Open(_UpperCamelCase ,'''rb'''							) as f:
      __lowerCamelCase													=    _extract_images(_UpperCamelCase							)

   __lowerCamelCase													=    _maybe_download(
       _UpperCamelCase ,_UpperCamelCase ,source_url + test_labels_file							)
   with gfile.Open(_UpperCamelCase ,'''rb'''							) as f:
      __lowerCamelCase													=    _extract_labels(_UpperCamelCase ,one_hot=_UpperCamelCase							)

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

   __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(_UpperCamelCase ,_UpperCamelCase ,**_UpperCamelCase							)
   __lowerCamelCase													=    _DataSet(_UpperCamelCase ,_UpperCamelCase ,**_UpperCamelCase							)
   __lowerCamelCase													=    _DataSet(_UpperCamelCase ,_UpperCamelCase ,**_UpperCamelCase							)

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_			=					{
    """configuration_clipseg""": [
        """CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPSegConfig""",
        """CLIPSegTextConfig""",
        """CLIPSegVisionConfig""",
    ],
    """processing_clipseg""": ["""CLIPSegProcessor"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_			=					[
      """CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CLIPSegModel""",
      """CLIPSegPreTrainedModel""",
      """CLIPSegTextModel""",
      """CLIPSegVisionModel""",
      """CLIPSegForImageSegmentation""",
  ]

if TYPE_CHECKING:
  from .configuration_clipseg import (
      CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
      CLIPSegConfig,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
  )
  from .processing_clipseg import CLIPSegProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_clipseg import (
        CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
        CLIPSegForImageSegmentation,
        CLIPSegModel,
        CLIPSegPreTrainedModel,
        CLIPSegTextModel,
        CLIPSegVisionModel,
    )

else:
  import sys

  a_			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






