
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

lowercase						:      List[str]									= collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      numpy.dtype(numpy.uintaa).newbyteorder(">")
							return numpy.frombuffer(bytestream.read(4)		,						dtype=_lowerCamelCase)[0]




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					Any:






							'''simple docstring'''





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




@deprecated(_lowerCamelCase		,						"Please use tf.one_hot on tensors.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    List[str])      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      labels_dense.shape[0]
							__UpperCamelCase			:     str         =      numpy.arange(_lowerCamelCase) * num_classes
							__UpperCamelCase			:     str         =      numpy.zeros((num_labels, num_classes))
							__UpperCamelCase			:     Tuple         =      1
							return labels_one_hot




@deprecated(_lowerCamelCase		,						"Please use tf.data to implement this functionality.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Optional[int]=False		,						_lowerCamelCase				:    Optional[int]=10)      ->					Dict:






							'''simple docstring'''





							print("Extracting"		,						f.name)
							with gzip.GzipFile(fileobj=_lowerCamelCase) as bytestream:
														__UpperCamelCase			:     int         =      _readaa(_lowerCamelCase)
														if magic != 2_049:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name))
														__UpperCamelCase			:     Any         =      _readaa(_lowerCamelCase)
														__UpperCamelCase			:     List[Any]         =      bytestream.read(_lowerCamelCase)
														__UpperCamelCase			:     Dict         =      numpy.frombuffer(_lowerCamelCase		,						dtype=numpy.uinta)
														if one_hot:
																					return _dense_to_one_hot(_lowerCamelCase		,						_lowerCamelCase)
														return labels




class 				lowerCamelCase__							:




	'''simple docstring'''




	@deprecated(
	    a  ,     "Please use alternatives such as official/mnist/_DataSet.py"
	    " from tensorflow/models."  ,     )
	def __init__(      self	:int  ,     a	:Any  ,     a	:List[str]  ,     a	:Union[str, Any]=False  ,     a	:List[Any]=False  ,     a	:Dict=dtypes.floataa  ,     a	:int=True  ,     a	:Optional[int]=None  ,     )	->				List[str]:
								__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      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			:     str         =      1_0_0_0_0
															__UpperCamelCase			:     Optional[Any]         =      one_hot
								else:
															assert (
															    images.shape[0] == labels.shape[0]
															), f'images.shape: {images.shape} labels.shape: {labels.shape}'
															__UpperCamelCase			:     Any         =      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			:     Union[str, 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			:     List[Any]         =      images.astype(numpy.floataa		)
																						__UpperCamelCase			:     Optional[Any]         =      numpy.multiply(a  ,     1.0 / 255.0		)
								__UpperCamelCase			:     Optional[Any]         =      images
								__UpperCamelCase			:     List[Any]         =      labels
								__UpperCamelCase			:     str         =      0
								__UpperCamelCase			:     Union[str, Any]         =      0







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								return self._images







	@property
	def      _lowerCamelCase       (      self	:Any		)	->				Dict:
								return self._labels







	@property
	def      _lowerCamelCase       (      self	:List[str]		)	->				str:
								return self._num_examples







	@property
	def      _lowerCamelCase       (      self	:Tuple		)	->				Dict:
								return self._epochs_completed







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




@deprecated(_lowerCamelCase		,						"Please write your own downloading logic.")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Union[str, Any])      ->					Tuple:






							'''simple docstring'''





							if not gfile.Exists(_lowerCamelCase):
														gfile.MakeDirs(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      os.path.join(_lowerCamelCase		,						_lowerCamelCase)
							if not gfile.Exists(_lowerCamelCase):
														urllib.request.urlretrieve(_lowerCamelCase		,						_lowerCamelCase)  # noqa: S310
														with gfile.GFile(_lowerCamelCase) as f:
																					__UpperCamelCase			:     Any         =      f.size()
														print("Successfully downloaded"		,						_lowerCamelCase		,						_lowerCamelCase		,						"bytes.")
							return filepath




@deprecated(
    _lowerCamelCase		,						"Please use alternatives such as:" " tensorflow_datasets.load('mnist')")
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any]=False		,						_lowerCamelCase				:    str=False		,						_lowerCamelCase				:    List[str]=dtypes.floataa		,						_lowerCamelCase				:    Any=True		,						_lowerCamelCase				:    Union[str, Any]=5_000		,						_lowerCamelCase				:    str=None		,						_lowerCamelCase				:    Optional[int]=DEFAULT_SOURCE_URL		,						)      ->					List[Any]:






							'''simple docstring'''





							if fake_data:

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

														__UpperCamelCase			:     Optional[int]         =      fake()
														__UpperCamelCase			:     Tuple         =      fake()
														__UpperCamelCase			:     List[str]         =      fake()
														return _Datasets(train=_lowerCamelCase		,						validation=_lowerCamelCase		,						test=_lowerCamelCase)

							if not source_url:  # empty string check
														__UpperCamelCase			:     str         =      DEFAULT_SOURCE_URL

							__UpperCamelCase			:     Optional[int]         =      "train-images-idx3-ubyte.gz"
							__UpperCamelCase			:     Dict         =      "train-labels-idx1-ubyte.gz"
							__UpperCamelCase			:     List[str]         =      "t10k-images-idx3-ubyte.gz"
							__UpperCamelCase			:     List[str]         =      "t10k-labels-idx1-ubyte.gz"

							__UpperCamelCase			:     Optional[int]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_images_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_images(_lowerCamelCase)

							__UpperCamelCase			:     Optional[Any]         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + train_labels_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     int         =      _extract_labels(_lowerCamelCase		,						one_hot=_lowerCamelCase)

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

							__UpperCamelCase			:     str         =      _maybe_download(
							    _lowerCamelCase		,						_lowerCamelCase		,						source_url + test_labels_file)
							with gfile.Open(_lowerCamelCase		,						"rb") as f:
														__UpperCamelCase			:     List[str]         =      _extract_labels(_lowerCamelCase		,						one_hot=_lowerCamelCase)

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

							__UpperCamelCase			:     Any         =      train_images[:validation_size]
							__UpperCamelCase			:     Optional[Any]         =      train_labels[:validation_size]
							__UpperCamelCase			:     Optional[int]         =      train_images[validation_size:]
							__UpperCamelCase			:     Tuple         =      train_labels[validation_size:]

							__UpperCamelCase			:     List[str]         =      {"dtype": dtype, "reshape": reshape, "seed": seed}

							__UpperCamelCase			:     Union[str, Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     str         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      _DataSet(_lowerCamelCase		,						_lowerCamelCase		,						**_lowerCamelCase)

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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase						:      List[Any]									= {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Dict									= [
		    'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SwinForImageClassification',
		    'SwinForMaskedImageModeling',
		    'SwinModel',
		    'SwinPreTrainedModel',
		    'SwinBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Optional[int]									= [
		    'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFSwinForImageClassification',
		    'TFSwinForMaskedImageModeling',
		    'TFSwinModel',
		    'TFSwinPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swin import (
				    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinBackbone,
				    SwinForImageClassification,
				    SwinForMaskedImageModeling,
				    SwinModel,
				    SwinPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				    TFSwinPreTrainedModel,
				)

else:
		import sys

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)