





import argparse
import os
import re


_a 						=      "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a 						=      re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
_a 						=      re.compile(r"\s*\(\s*\"(\S[^\"]+)\"")





def lowerCAmelCase__(__snake_case       ,__snake_case = False							)				->      int:







	'''simple docstring'''







	with open(SCREAMING_SNAKE_CASE__       ,'''r'''       ,encoding='''utf-8'''							) as f:
		lowerCamelCase__								=		f.read()

	lowerCamelCase__								=		content.split('''\n'''							)
	lowerCamelCase__								=		[]
	lowerCamelCase__								=		0
	while line_idx < len(SCREAMING_SNAKE_CASE__							):
		if _re_intro_mapping.search(lines[line_idx]							) is not None:
			lowerCamelCase__								=		len(re.search(R'''^(\s*)\S'''       ,lines[line_idx]							).groups()[0]							) + 8
			# Start of a new mapping!
			while not lines[line_idx].startswith(''' ''' * indent + '''('''							):
				new_lines.append(lines[line_idx]							)
				line_idx += 1

			lowerCamelCase__								=		[]
			while lines[line_idx].strip() != "]":
				# Blocks either fit in one line or not
				if lines[line_idx].strip() == "(":
					lowerCamelCase__								=		line_idx
					while not lines[line_idx].startswith(''' ''' * indent + ''')'''							):
						line_idx += 1
					blocks.append('''\n'''.join(lines[start_idx : line_idx + 1]							)							)
				else:
					blocks.append(lines[line_idx]							)
				line_idx += 1

			# Sort blocks by their identifiers
			lowerCamelCase__								=		sorted(SCREAMING_SNAKE_CASE__       ,key=lambda __snake_case							: _re_identifier.search(SCREAMING_SNAKE_CASE__							).groups()[0]							)
			new_lines += blocks
		else:
			new_lines.append(lines[line_idx]							)
			line_idx += 1

	if overwrite:
		with open(SCREAMING_SNAKE_CASE__       ,'''w'''       ,encoding='''utf-8'''							) as f:
			f.write('''\n'''.join(SCREAMING_SNAKE_CASE__							)							)
	elif "\n".join(SCREAMING_SNAKE_CASE__							) != content:
		return True





def lowerCAmelCase__(__snake_case = False							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		[os.path.join(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for f in os.listdir(SCREAMING_SNAKE_CASE__							) if f.endswith('''.py'''							)]
	lowerCamelCase__								=		[sort_auto_mapping(SCREAMING_SNAKE_CASE__       ,overwrite=SCREAMING_SNAKE_CASE__							) for fname in fnames]

	if not overwrite and any(SCREAMING_SNAKE_CASE__							):
		lowerCamelCase__								=		[f for f, d in zip(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) if d]
		raise ValueError(
		    F'The following files have auto mappings that need sorting: {", ".join(SCREAMING_SNAKE_CASE__							)}. Run `make style` to fix'
		    ''' this.'''							)


if __name__ == "__main__":
			_a 						=      argparse.ArgumentParser()
			parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
			_a 						=      parser.parse_args()

			sort_all_auto_mappings(not args.check_only)







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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=numpy.dtype(numpy.uintaa				).newbyteorder('>'				)
     return numpy.frombuffer(bytestream.read(4				)			,     dtype=SCREAMING_SNAKE_CASE__				)[0]

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please use tf.data to implement this functionality.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_51:
               raise ValueError(
                   'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(rows * cols * num_images				)
          __UpperCamelCase    							=numpy.frombuffer(SCREAMING_SNAKE_CASE__			,     dtype=numpy.uinta				)
          __UpperCamelCase    							=data.reshape(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     1				)
          return data

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

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please use tf.data to implement this functionality.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					Dict=False			,     SCREAMING_SNAKE_CASE__			:					str=10				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_49:
               raise ValueError(
                   'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=numpy.frombuffer(SCREAMING_SNAKE_CASE__			,     dtype=numpy.uinta				)
          if one_hot:
               return _dense_to_one_hot(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
          return labels






class     UpperCAmelCase__       :



    """simple docstring"""






    @deprecated(
        A_					,   'Please use alternatives such as official/mnist/_DataSet.py'
        ' from tensorflow/models.'					,   )
    def __init__(			self					,   A_					,   A_					,   A_=False					,   A_=False					,   A_=dtypes.floataa					,   A_=True					,   A_=None					,   )  ->       Optional[int]:
         __UpperCamelCase							,      __UpperCamelCase    							=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    							=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    							=10000
              __UpperCamelCase    							=one_hot
         else:
              assert (
                  images.shape[0] == labels.shape[0]
              ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
              __UpperCamelCase    							=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    							=images.reshape(
                       images.shape[0]					,   images.shape[1] * images.shape[2]    )
              if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   __UpperCamelCase    							=images.astype(numpy.floataa    )
                   __UpperCamelCase    							=numpy.multiply(A_					,   1.0 / 255.0    )
         __UpperCamelCase    							=images
         __UpperCamelCase    							=labels
         __UpperCamelCase    							=0
         __UpperCamelCase    							=0






    @property
    def _a					(			self    )  ->       Tuple:
         return self._images






    @property
    def _a					(			self    )  ->       Union[str, Any]:
         return self._labels






    @property
    def _a					(			self    )  ->       Optional[Any]:
         return self._num_examples






    @property
    def _a					(			self    )  ->       List[str]:
         return self._epochs_completed






    def _a					(			self					,   A_					,   A_=False					,   A_=True    )  ->       Optional[Any]:
         if fake_data:
              __UpperCamelCase    							=[1] * 784
              __UpperCamelCase    							=[1] + [0] * 9 if self.one_hot else 0
              return (
                  [fake_image for _ in range(A_    )],
                  [fake_label for _ in range(A_    )],
              )
         __UpperCamelCase    							=self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
              __UpperCamelCase    							=numpy.arange(self._num_examples    )
              numpy.random.shuffle(A_    )
              __UpperCamelCase    							=self.images[perma]
              __UpperCamelCase    							=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    							=self._num_examples - start
              __UpperCamelCase    							=self._images[start : self._num_examples]
              __UpperCamelCase    							=self._labels[start : self._num_examples]
              # Shuffle the data
              if shuffle:
                   __UpperCamelCase    							=numpy.arange(self._num_examples    )
                   numpy.random.shuffle(A_    )
                   __UpperCamelCase    							=self.images[perm]
                   __UpperCamelCase    							=self.labels[perm]
              # Start next epoch
              __UpperCamelCase    							=0
              __UpperCamelCase    							=batch_size - rest_num_examples
              __UpperCamelCase    							=self._index_in_epoch
              __UpperCamelCase    							=self._images[start:end]
              __UpperCamelCase    							=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    							=self._index_in_epoch
              return self._images[start:end], self._labels[start:end]

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





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

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

          __UpperCamelCase    							=fake()
          __UpperCamelCase    							=fake()
          __UpperCamelCase    							=fake()
          return _Datasets(train=SCREAMING_SNAKE_CASE__			,     validation=SCREAMING_SNAKE_CASE__			,     test=SCREAMING_SNAKE_CASE__				)

     if not source_url:  # empty string check
          __UpperCamelCase    							=DEFAULT_SOURCE_URL

     __UpperCamelCase    							='train-images-idx3-ubyte.gz'
     __UpperCamelCase    							='train-labels-idx1-ubyte.gz'
     __UpperCamelCase    							='t10k-images-idx3-ubyte.gz'
     __UpperCamelCase    							='t10k-labels-idx1-ubyte.gz'

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

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

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

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

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

     __UpperCamelCase    							=train_images[:validation_size]
     __UpperCamelCase    							=train_labels[:validation_size]
     __UpperCamelCase    							=train_images[validation_size:]
     __UpperCamelCase    							=train_labels[validation_size:]

     __UpperCamelCase    							={'dtype': dtype, 'reshape': reshape, 'seed': seed}

     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

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





