





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

_lowerCamelCase 					=      collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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






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






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






@deprecated(UpperCamelCase__       , """Please use tf.data to implement this functionality."""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]=False       , UpperCamelCase__:				List[Any]=10     ):
		print("""Extracting"""       , f.name     )
		with gzip.GzipFile(fileobj=UpperCamelCase__     ) as bytestream:
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				if magic != 2_049:
						raise ValueError(
						    """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)     )
				SCREAMING_SNAKE_CASE__								=   _readaa(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   bytestream.read(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   numpy.frombuffer(UpperCamelCase__       , dtype=numpy.uinta     )
				if one_hot:
						return _dense_to_one_hot(UpperCamelCase__       , UpperCamelCase__     )
				return labels




class 	UpperCamelCase_							:


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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		)
									SCREAMING_SNAKE_CASE__								=   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:
											SCREAMING_SNAKE_CASE__								=   1_0000
											SCREAMING_SNAKE_CASE__								=   one_hot
									else:
											assert (
											    images.shape[0] == labels.shape[0]
											), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
											SCREAMING_SNAKE_CASE__								=   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
													SCREAMING_SNAKE_CASE__								=   images.reshape(
													    images.shape[0]				,					images.shape[1] * images.shape[2]		)
											if dtype == dtypes.floataa:
													# Convert from [0, 255] -> [0.0, 1.0].
													SCREAMING_SNAKE_CASE__								=   images.astype(numpy.floataa		)
													SCREAMING_SNAKE_CASE__								=   numpy.multiply(__A				,					1.0 / 2_5_5.0		)
									SCREAMING_SNAKE_CASE__								=   images
									SCREAMING_SNAKE_CASE__								=   labels
									SCREAMING_SNAKE_CASE__								=   0
									SCREAMING_SNAKE_CASE__								=   0


							@property
							def   _snake_case	(				self     :str		) -> List[str]:



									"""simple docstring"""



									return self._images


							@property
							def   _snake_case	(				self     :str		) -> List[str]:



									"""simple docstring"""



									return self._labels


							@property
							def   _snake_case	(				self     :List[str]		) -> str:



									"""simple docstring"""



									return self._num_examples


							@property
							def   _snake_case	(				self     :List[Any]		) -> Optional[Any]:



									"""simple docstring"""



									return self._epochs_completed


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]				,					__A     :str=False				,					__A     :str=True		) -> List[str]:



									"""simple docstring"""



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






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






@deprecated(
    UpperCamelCase__       , """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')"""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Optional[Any]=False       , UpperCamelCase__:				Dict=False       , UpperCamelCase__:				Union[str, Any]=dtypes.floataa       , UpperCamelCase__:				Any=True       , UpperCamelCase__:				List[Any]=5_000       , UpperCamelCase__:				List[Any]=None       , UpperCamelCase__:				List[Any]=DEFAULT_SOURCE_URL       , ):
		if fake_data:

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

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

		if not source_url:  # empty string check
				SCREAMING_SNAKE_CASE__								=   DEFAULT_SOURCE_URL

		SCREAMING_SNAKE_CASE__								=   """train-images-idx3-ubyte.gz"""
		SCREAMING_SNAKE_CASE__								=   """train-labels-idx1-ubyte.gz"""
		SCREAMING_SNAKE_CASE__								=   """t10k-images-idx3-ubyte.gz"""
		SCREAMING_SNAKE_CASE__								=   """t10k-labels-idx1-ubyte.gz"""

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

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

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

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

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

		SCREAMING_SNAKE_CASE__								=   train_images[:validation_size]
		SCREAMING_SNAKE_CASE__								=   train_labels[:validation_size]
		SCREAMING_SNAKE_CASE__								=   train_images[validation_size:]
		SCREAMING_SNAKE_CASE__								=   train_labels[validation_size:]

		SCREAMING_SNAKE_CASE__								=   {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

		SCREAMING_SNAKE_CASE__								=   _DataSet(UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   _DataSet(UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   _DataSet(UpperCamelCase__       , UpperCamelCase__       , **UpperCamelCase__     )

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






from torch import nn






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		if act_fn in ["swish", "silu"]:
				return nn.SiLU()
		elif act_fn == "mish":
				return nn.Mish()
		elif act_fn == "gelu":
				return nn.GELU()
		else:
				raise ValueError(f'''Unsupported activation function: {act_fn}'''     )