






"""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

_UpperCamelCase    :   Any   			=						collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def       snake_case  (A_				:Optional[int]				):







  '''simple docstring'''

  a					:      List[Any]    =       numpy.dtype(numpy.uintaa				).newbyteorder('>'				)
  return numpy.frombuffer(bytestream.read(4				)     ,      dtype=A_				)[0]




@deprecated(A_     ,      'Please use tf.data to implement this functionality.'				)
def       snake_case  (A_				:Any				):







  '''simple docstring'''

  print('Extracting'     ,      f.name				)
  with gzip.GzipFile(fileobj=A_				) as bytestream:
    a					:      int    =       _readaa(A_				)
    if magic != 2_0_5_1:
      raise ValueError(
          'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
    a					:      List[str]    =       _readaa(A_				)
    a					:      Dict    =       _readaa(A_				)
    a					:      Optional[int]    =       _readaa(A_				)
    a					:      int    =       bytestream.read(rows * cols * num_images				)
    a					:      Any    =       numpy.frombuffer(A_     ,      dtype=numpy.uinta				)
    a					:      Dict    =       data.reshape(A_     ,      A_     ,      A_     ,      1				)
    return data




@deprecated(A_     ,      'Please use tf.one_hot on tensors.'				)
def       snake_case  (A_				:Tuple     ,      A_				:Tuple				):







  '''simple docstring'''

  a					:      Optional[Any]    =       labels_dense.shape[0]
  a					:      Dict    =       numpy.arange(A_				) * num_classes
  a					:      Tuple    =       numpy.zeros((num_labels, num_classes)				)
  a					:      Union[str, Any]    =       1
  return labels_one_hot




@deprecated(A_     ,      'Please use tf.data to implement this functionality.'				)
def       snake_case  (A_				:Optional[Any]     ,      A_				:List[Any]=False     ,      A_				:str=1_0				):







  '''simple docstring'''

  print('Extracting'     ,      f.name				)
  with gzip.GzipFile(fileobj=A_				) as bytestream:
    a					:      Tuple    =       _readaa(A_				)
    if magic != 2_0_4_9:
      raise ValueError(
          'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
    a					:      List[Any]    =       _readaa(A_				)
    a					:      Union[str, Any]    =       bytestream.read(A_				)
    a					:      int    =       numpy.frombuffer(A_     ,      dtype=numpy.uinta				)
    if one_hot:
      return _dense_to_one_hot(A_     ,      A_				)
    return labels






class 						snake_case  :





    @deprecated(
        A    ,		'Please use alternatives such as official/mnist/_DataSet.py'
        ' from tensorflow/models.'    ,		)
    def __init__(							self     : List[Any]    ,		A     : Union[str, Any]    ,		A     : Any    ,		A     : Optional[Any]=False    ,		A     : str=False    ,		A     : Dict=dtypes.floataa    ,		A     : List[str]=True    ,		A     : Optional[int]=None    ,		):


      '''simple docstring'''




      a,					a					:      Dict    =       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      )
      a					:      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:
        a					:      str    =       1_0_0_0_0
        a					:      Dict    =       one_hot
      else:
        assert (
            images.shape[0] == labels.shape[0]
        ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
        a					:      Dict    =       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
          a					:      Dict    =       images.reshape(
              images.shape[0]    ,		images.shape[1] * images.shape[2]      )
        if dtype == dtypes.floataa:
          # Convert from [0, 255] -> [0.0, 1.0].
          a					:      Optional[Any]    =       images.astype(numpy.floataa      )
          a					:      Any    =       numpy.multiply(A    ,		1.0 / 2_55.0      )
      a					:      Optional[int]    =       images
      a					:      List[Any]    =       labels
      a					:      Optional[Any]    =       0
      a					:      Optional[int]    =       0





    @property
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      return self._images





    @property
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      return self._labels





    @property
    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      return self._num_examples





    @property
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      return self._epochs_completed





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : List[Any]    ,		A     : int=False    ,		A     : Dict=True      ):


      '''simple docstring'''




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




@deprecated(A_     ,      'Please write your own downloading logic.'				)
def       snake_case  (A_				:Any     ,      A_				:Any     ,      A_				:Any				):







  '''simple docstring'''

  if not gfile.Exists(A_				):
    gfile.MakeDirs(A_				)
  a					:      int    =       os.path.join(A_     ,      A_				)
  if not gfile.Exists(A_				):
    urllib.request.urlretrieve(A_     ,      A_				)  # noqa: S310
    with gfile.GFile(A_				) as f:
      a					:      Tuple    =       f.size()
    print('Successfully downloaded'     ,      A_     ,      A_     ,      'bytes.'				)
  return filepath






@deprecated(
    A_     ,      'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'				)
def       snake_case  (A_				:Any     ,      A_				:int=False     ,      A_				:List[str]=False     ,      A_				:List[str]=dtypes.floataa     ,      A_				:Union[str, Any]=True     ,      A_				:str=5_0_0_0     ,      A_				:List[Any]=None     ,      A_				:Union[str, Any]=DEFAULT_SOURCE_URL     ,      ):







  '''simple docstring'''

  if fake_data:

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

    a					:      Optional[Any]    =       fake()
    a					:      Union[str, Any]    =       fake()
    a					:      str    =       fake()
    return _Datasets(train=A_     ,      validation=A_     ,      test=A_				)

  if not source_url:  # empty string check
    a					:      Tuple    =       DEFAULT_SOURCE_URL

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

  a					:      List[Any]    =       _maybe_download(
      A_     ,      A_     ,      source_url + train_images_file				)
  with gfile.Open(A_     ,      'rb'				) as f:
    a					:      Any    =       _extract_images(A_				)

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

  a					:      int    =       _maybe_download(
      A_     ,      A_     ,      source_url + test_images_file				)
  with gfile.Open(A_     ,      'rb'				) as f:
    a					:      Union[str, Any]    =       _extract_images(A_				)

  a					:      Tuple    =       _maybe_download(
      A_     ,      A_     ,      source_url + test_labels_file				)
  with gfile.Open(A_     ,      'rb'				) as f:
    a					:      Tuple    =       _extract_labels(A_     ,      one_hot=A_				)

  if not 0 <= validation_size <= len(A_				):
    a					:      Optional[int]    =       (
        'Validation size should be between 0 and '
        f'''{len(A_				)}. Received: {validation_size}.'''
    )
    raise ValueError(A_				)

  a					:      int    =       train_images[:validation_size]
  a					:      Tuple    =       train_labels[:validation_size]
  a					:      List[Any]    =       train_images[validation_size:]
  a					:      Dict    =       train_labels[validation_size:]

  a					:      int    =       {'dtype': dtype, 'reshape': reshape, 'seed': seed}

  a					:      Dict    =       _DataSet(A_     ,      A_     ,      **A_				)
  a					:      Dict    =       _DataSet(A_     ,      A_     ,      **A_				)
  a					:      Tuple    =       _DataSet(A_     ,      A_     ,      **A_				)

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












"""simple docstring"""


import argparse
import os
import re

import packaging.version


_UpperCamelCase    :   Optional[Any]   			=						'examples/'
_UpperCamelCase    :   Any   			=						{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
_UpperCamelCase    :   List[str]   			=						{
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
_UpperCamelCase    :   List[str]   			=						'README.md'




def       snake_case  (A_				:str     ,      A_				:Optional[Any]     ,      A_				:Any				):







  '''simple docstring'''

  with open(A_     ,      'r'     ,      encoding='utf-8'     ,      newline='\n'				) as f:
    a					:      Tuple    =       f.read()
  a,					a					:      Any    =       REPLACE_PATTERNS[pattern]
  a					:      Dict    =       replace.replace('VERSION'     ,      A_				)
  a					:      Union[str, Any]    =       re_pattern.sub(A_     ,      A_				)
  with open(A_     ,      'w'     ,      encoding='utf-8'     ,      newline='\n'				) as f:
    f.write(A_				)




def       snake_case  (A_				:List[Any]				):







  '''simple docstring'''

  for folder, directories, fnames in os.walk(A_				):
    # Removing some of the folders with non-actively maintained examples from the walk
    if "research_projects" in directories:
      directories.remove('research_projects'				)
    if "legacy" in directories:
      directories.remove('legacy'				)
    for fname in fnames:
      if fname.endswith('.py'				):
        update_version_in_file(os.path.join(A_     ,      A_				)     ,      A_     ,      pattern='examples'				)




def       snake_case  (A_				:Tuple     ,      A_				:Optional[Any]=False				):







  '''simple docstring'''

  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(A_     ,      A_     ,      A_				)
  if not patch:
    update_version_in_examples(A_				)




def       snake_case  ():







  '''simple docstring'''

  a					:      str    =       '🤗 Transformers currently provides the following architectures'
  a					:      Dict    =       '1. Want to contribute a new model?'
  with open(A_     ,      'r'     ,      encoding='utf-8'     ,      newline='\n'				) as f:
    a					:      Optional[Any]    =       f.readlines()

  # Find the start of the list.
  a					:      List[str]    =       0
  while not lines[start_index].startswith(_start_prompt				):
    start_index += 1
  start_index += 1

  a					:      Optional[Any]    =       start_index
  # Update the lines in the model list.
  while not lines[index].startswith(_end_prompt				):
    if lines[index].startswith('1.'				):
      a					:      int    =       lines[index].replace(
          'https://huggingface.co/docs/diffusers/main/model_doc'     ,      'https://huggingface.co/docs/diffusers/model_doc'     ,      )
    index += 1

  with open(A_     ,      'w'     ,      encoding='utf-8'     ,      newline='\n'				) as f:
    f.writelines(A_				)




def       snake_case  ():







  '''simple docstring'''

  with open(REPLACE_FILES['init']     ,      'r'				) as f:
    a					:      List[str]    =       f.read()
  a					:      str    =       REPLACE_PATTERNS['init'][0].search(A_				).groups()[0]
  return packaging.version.parse(A_				)




def       snake_case  (A_				:Optional[Any]=False				):







  '''simple docstring'''

  a					:      Optional[int]    =       get_version()
  if patch and default_version.is_devrelease:
    raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'				)
  if default_version.is_devrelease:
    a					:      Tuple    =       default_version.base_version
  elif patch:
    a					:      Union[str, Any]    =       f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
  else:
    a					:      Optional[Any]    =       f'''{default_version.major}.{default_version.minor + 1}.0'''

  # Now let's ask nicely if that's the right one.
  a					:      Union[str, Any]    =       input(f'''Which version are you releasing? [{default_version}]'''				)
  if len(A_				) == 0:
    a					:      int    =       default_version

  print(f'''Updating version to {version}.'''				)
  global_version_update(A_     ,      patch=A_				)




def       snake_case  ():







  '''simple docstring'''

  a					:      str    =       get_version()
  a					:      Optional[int]    =       f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
  a					:      Optional[int]    =       current_version.base_version

  # Check with the user we got that right.
  a					:      str    =       input(f'''Which version are we developing now? [{dev_version}]'''				)
  if len(A_				) == 0:
    a					:      Union[str, Any]    =       dev_version

  print(f'''Updating version to {version}.'''				)
  global_version_update(A_				)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
   _UpperCamelCase    :   Dict   			=						argparse.ArgumentParser()
   parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
   parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
   _UpperCamelCase    :   Optional[Any]   			=						parser.parse_args()
   if not args.post_release:
      pre_release_work(patch=args.patch)
   elif args.patch:
      print('Nothing to do after a patch :-)')
   else:
      post_release_work()




