

"""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    : Optional[int]				 =						collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__UpperCamelCase    : Union[str, Any]				 =						'''https://storage.googleapis.com/cvdf-datasets/mnist/'''

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

@deprecated(A_		,	'''Please use tf.one_hot on tensors.'''					)
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		List[Any]    							=      labels_dense.shape[0]
	lowerCAmelCase__      :		Dict    							=      numpy.arange(A_					) * num_classes
	lowerCAmelCase__      :		int    							=      numpy.zeros((num_labels, num_classes)					)
	lowerCAmelCase__      :		List[Any]    							=      1
	return labels_one_hot

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







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



					@deprecated(
					    lowercase_  ,'''Please use alternatives such as official/mnist/_DataSet.py'''
					    ''' from tensorflow/models.'''  ,)
					def __init__(			self     :							int  ,lowercase_     :							Union[str, Any]  ,lowercase_     :							List[str]  ,lowercase_     :							Dict=False  ,lowercase_     :							Tuple=False  ,lowercase_     :							Union[str, Any]=dtypes.floataa  ,lowercase_     :							Tuple=True  ,lowercase_     :							Union[str, Any]=None  ,):
						lowerCAmelCase__							,lowerCAmelCase__      :		List[Any]    							=      random_seed.get_seed(lowercase_						)
						# 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__      :		Optional[Any]    							=      dtypes.as_dtype(lowercase_						).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__      :		List[Any]    							=      1_0_0_0_0
							lowerCAmelCase__      :		List[str]    							=      one_hot
						else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F'images.shape: {images.shape} labels.shape: {labels.shape}'
							lowerCAmelCase__      :		List[str]    							=      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__      :		Tuple    							=      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[int]    							=      images.astype(numpy.floataa						)
								lowerCAmelCase__      :		List[str]    							=      numpy.multiply(lowercase_  ,1.0 / 255.0						)
						lowerCAmelCase__      :		Tuple    							=      images
						lowerCAmelCase__      :		int    							=      labels
						lowerCAmelCase__      :		Optional[int]    							=      0
						lowerCAmelCase__      :		Union[str, Any]    							=      0







					@property
					def 			__lowerCAmelCase					(			self     :							Tuple						):
						return self._images







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self._labels







					@property
					def 			__lowerCAmelCase					(			self     :							int						):
						return self._num_examples







					@property
					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return self._epochs_completed







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

@deprecated(A_		,	'''Please write your own downloading logic.'''					)
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	if not gfile.Exists(A_					):
		gfile.MakeDirs(A_					)
	lowerCAmelCase__      :		int    							=      os.path.join(A_		,	A_					)
	if not gfile.Exists(A_					):
		urllib.request.urlretrieve(A_		,	A_					)  # noqa: S310
		with gfile.GFile(A_					) as f:
			lowerCAmelCase__      :		Dict    							=      f.size()
		print('''Successfully downloaded'''		,	A_		,	A_		,	'''bytes.'''					)
	return filepath





@deprecated(
    A_		,	'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''					)
def     __SCREAMING_SNAKE_CASE    ( A_		,	A_=False		,	A_=False		,	A_=dtypes.floataa		,	A_=True		,	A_=50_00		,	A_=None		,	A_=DEFAULT_SOURCE_URL		,	):
	if fake_data:

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

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

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

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

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

	lowerCAmelCase__      :		Tuple    							=      _maybe_download(
	    A_		,	A_		,	source_url + train_labels_file					)
	with gfile.Open(A_		,	'''rb'''					) as f:
		lowerCAmelCase__      :		Optional[int]    							=      _extract_labels(A_		,	one_hot=A_					)

	lowerCAmelCase__      :		int    							=      _maybe_download(
	    A_		,	A_		,	source_url + test_images_file					)
	with gfile.Open(A_		,	'''rb'''					) as f:
		lowerCAmelCase__      :		str    							=      _extract_images(A_					)

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

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

	lowerCAmelCase__      :		List[Any]    							=      train_images[:validation_size]
	lowerCAmelCase__      :		int    							=      train_labels[:validation_size]
	lowerCAmelCase__      :		Dict    							=      train_images[validation_size:]
	lowerCAmelCase__      :		List[Any]    							=      train_labels[validation_size:]

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

	lowerCAmelCase__      :		Union[str, Any]    							=      _DataSet(A_		,	A_		,	**A_					)
	lowerCAmelCase__      :		Union[str, Any]    							=      _DataSet(A_		,	A_		,	**A_					)
	lowerCAmelCase__      :		Optional[int]    							=      _DataSet(A_		,	A_		,	**A_					)

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







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCamelCase    : Tuple				 =						logging.get_logger(__name__)

__UpperCamelCase    : Optional[int]				 =						{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_			):






					"""simple docstring"""



					lowercase__												=			"bit"
					lowercase__												=			["preactivation", "bottleneck"]
					lowercase__												=			["SAME", "VALID"]







					def __init__(			self     :							int  ,lowercase_     :							Optional[int]=3  ,lowercase_     :							int=6_4  ,lowercase_     :							Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]  ,lowercase_     :							Union[str, Any]=[3, 4, 6, 3]  ,lowercase_     :							List[Any]="preactivation"  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Dict=None  ,lowercase_     :							Optional[Any]=3_2  ,lowercase_     :							Any=0.0  ,lowercase_     :							Optional[Any]=False  ,lowercase_     :							List[Any]=3_2  ,lowercase_     :							Optional[Any]=1  ,lowercase_     :							Optional[int]=None  ,lowercase_     :							int=None  ,**lowercase_     :							List[str]  ,):
						super().__init__(**lowercase_						)
						if layer_type not in self.layer_types:
							raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
						if global_padding is not None:
							if global_padding.upper() in self.supported_padding:
								lowerCAmelCase__      :		Optional[Any]    							=      global_padding.upper()
							else:
								raise ValueError(F'Padding strategy {global_padding} not supported'						)
						lowerCAmelCase__      :		int    							=      num_channels
						lowerCAmelCase__      :		int    							=      embedding_size
						lowerCAmelCase__      :		Any    							=      hidden_sizes
						lowerCAmelCase__      :		int    							=      depths
						lowerCAmelCase__      :		Union[str, Any]    							=      layer_type
						lowerCAmelCase__      :		Optional[int]    							=      hidden_act
						lowerCAmelCase__      :		int    							=      global_padding
						lowerCAmelCase__      :		List[Any]    							=      num_groups
						lowerCAmelCase__      :		List[Any]    							=      drop_path_rate
						lowerCAmelCase__      :		Union[str, Any]    							=      embedding_dynamic_padding
						lowerCAmelCase__      :		int    							=      output_stride
						lowerCAmelCase__      :		Optional[Any]    							=      width_factor

						lowerCAmelCase__      :		List[str]    							=      ['''stem'''] + [F'stage{idx}' for idx in range(1  ,len(lowercase_						) + 1						)]
						lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      get_aligned_output_features_output_indices(
						    out_features=lowercase_  ,out_indices=lowercase_  ,stage_names=self.stage_names						)




