






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			:		List[Any]   				=    collections.namedtuple("_Datasets", ["train", "validation", "test"])

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





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    str:


				'''simple docstring'''




				lowerCAmelCase__      			=						numpy.dtype(numpy.uintaa       ).newbyteorder('''>'''       )
				return numpy.frombuffer(bytestream.read(4       )     ,					dtype=lowercase__       )[0]





@deprecated(lowercase__     ,					'''Please use tf.data to implement this functionality.'''       )
def 	lowerCAmelCase_		(lowercase__						:       int       )		->    List[str]:


				'''simple docstring'''




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





@deprecated(lowercase__     ,					'''Please use tf.one_hot on tensors.'''       )
def 	lowerCAmelCase_		(lowercase__						:       Dict     ,					lowercase__						:       str       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						labels_dense.shape[0]
				lowerCAmelCase__      			=						numpy.arange(lowercase__       ) * num_classes
				lowerCAmelCase__      			=						numpy.zeros((num_labels, num_classes)       )
				lowerCAmelCase__      			=						1
				return labels_one_hot





@deprecated(lowercase__     ,					'''Please use tf.data to implement this functionality.'''       )
def 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       List[Any]=False     ,					lowercase__						:       List[str]=10       )		->    List[Any]:


				'''simple docstring'''




				print('''Extracting'''     ,					f.name       )
				with gzip.GzipFile(fileobj=lowercase__       ) as bytestream:
								lowerCAmelCase__      			=						_readaa(lowercase__       )
								if magic != 20_49:
												raise ValueError(
												    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)       )
								lowerCAmelCase__      			=						_readaa(lowercase__       )
								lowerCAmelCase__      			=						bytestream.read(lowercase__       )
								lowerCAmelCase__      			=						numpy.frombuffer(lowercase__     ,					dtype=numpy.uinta       )
								if one_hot:
												return _dense_to_one_hot(lowercase__     ,					lowercase__       )
								return labels
class  lowerCAmelCase_       :




							@deprecated(
							    SCREAMING_SNAKE_CASE_ ,							'''Please use alternatives such as official/mnist/_DataSet.py'''
							    ''' from tensorflow/models.''' ,							)
							def __init__(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   int ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=False ,							SCREAMING_SNAKE_CASE_     :   str=False ,							SCREAMING_SNAKE_CASE_     :   Dict=dtypes.floataa ,							SCREAMING_SNAKE_CASE_     :   List[str]=True ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						random_seed.get_seed(SCREAMING_SNAKE_CASE_	)
											# 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__      			=						dtypes.as_dtype(SCREAMING_SNAKE_CASE_	).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__      			=						10_000
															lowerCAmelCase__      			=						one_hot
											else:
															assert (
															    images.shape[0] == labels.shape[0]
															), f'images.shape: {images.shape} labels.shape: {labels.shape}'
															lowerCAmelCase__      			=						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__      			=						images.reshape(
																			    images.shape[0] ,							images.shape[1] * images.shape[2]	)
															if dtype == dtypes.floataa:
																			# Convert from [0, 255] -> [0.0, 1.0].
																			lowerCAmelCase__      			=						images.astype(numpy.floataa	)
																			lowerCAmelCase__      			=						numpy.multiply(SCREAMING_SNAKE_CASE_ ,							1.0 / 255.0	)
											lowerCAmelCase__      			=						images
											lowerCAmelCase__      			=						labels
											lowerCAmelCase__      			=						0
											lowerCAmelCase__      			=						0




							@property
							def 						__snake_case					(   self     :   Optional[Any]	):
											return self._images




							@property
							def 						__snake_case					(   self     :   Dict	):
											return self._labels




							@property
							def 						__snake_case					(   self     :   Tuple	):
											return self._num_examples




							@property
							def 						__snake_case					(   self     :   Tuple	):
											return self._epochs_completed




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





@deprecated(lowercase__     ,					'''Please write your own downloading logic.'''       )
def 	lowerCAmelCase_		(lowercase__						:       Optional[Any]     ,					lowercase__						:       List[Any]     ,					lowercase__						:       List[Any]       )		->    List[Any]:


				'''simple docstring'''




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





@deprecated(
    lowercase__     ,					'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''       )
def 	lowerCAmelCase_		(lowercase__						:       str     ,					lowercase__						:       Union[str, Any]=False     ,					lowercase__						:       str=False     ,					lowercase__						:       Optional[Any]=dtypes.floataa     ,					lowercase__						:       Dict=True     ,					lowercase__						:       Optional[int]=50_00     ,					lowercase__						:       str=None     ,					lowercase__						:       int=DEFAULT_SOURCE_URL     ,					)		->    Union[str, Any]:


				'''simple docstring'''




				if fake_data:

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

								lowerCAmelCase__      			=						fake()
								lowerCAmelCase__      			=						fake()
								lowerCAmelCase__      			=						fake()
								return _Datasets(train=lowercase__     ,					validation=lowercase__     ,					test=lowercase__       )

				if not source_url:  # empty string check
								lowerCAmelCase__      			=						DEFAULT_SOURCE_URL

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

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

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

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

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

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

				lowerCAmelCase__      			=						train_images[:validation_size]
				lowerCAmelCase__      			=						train_labels[:validation_size]
				lowerCAmelCase__      			=						train_images[validation_size:]
				lowerCAmelCase__      			=						train_labels[validation_size:]

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

				lowerCAmelCase__      			=						_DataSet(lowercase__     ,					lowercase__     ,					**lowercase__       )
				lowerCAmelCase__      			=						_DataSet(lowercase__     ,					lowercase__     ,					**lowercase__       )
				lowerCAmelCase__      			=						_DataSet(lowercase__     ,					lowercase__     ,					**lowercase__       )

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













from ...configuration_utils import PretrainedConfig
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:List[str]		      =    'bert-generation'




							def __init__(   self     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   List[str]=50_358 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=1_024 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=24 ,							SCREAMING_SNAKE_CASE_     :   Any=16 ,							SCREAMING_SNAKE_CASE_     :   Tuple=4_096 ,							SCREAMING_SNAKE_CASE_     :   str="gelu" ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=512 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1e-12 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=2 ,							SCREAMING_SNAKE_CASE_     :   int=1 ,							SCREAMING_SNAKE_CASE_     :   str="absolute" ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							**SCREAMING_SNAKE_CASE_     :   str ,							):
											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_ ,							bos_token_id=SCREAMING_SNAKE_CASE_ ,							eos_token_id=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						layer_norm_eps
											lowerCAmelCase__      			=						position_embedding_type
											lowerCAmelCase__      			=						use_cache





