






'''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						:						Tuple  =					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 __UpperCAmelCase   (					A					:			int		)    ->   Any:
			UpperCAmelCase_      :       Optional[int]          =       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 __UpperCAmelCase   (					A					:			Optional[Any]		)    ->   List[Any]:
			print('''Extracting'''			,       f.name		)
			with gzip.GzipFile(fileobj=A		) as bytestream:
						UpperCAmelCase_      :       List[str]          =       _readaa(A		)
						if magic != 2_0_5_1:
									raise ValueError(
									    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)		)
						UpperCAmelCase_      :       List[str]          =       _readaa(A		)
						UpperCAmelCase_      :       Any          =       _readaa(A		)
						UpperCAmelCase_      :       Dict          =       _readaa(A		)
						UpperCAmelCase_      :       Tuple          =       bytestream.read(rows * cols * num_images		)
						UpperCAmelCase_      :       Optional[int]          =       numpy.frombuffer(A			,       dtype=numpy.uinta		)
						UpperCAmelCase_      :       str          =       data.reshape(A			,       A			,       A			,       1		)
						return data
@deprecated(A			,       '''Please use tf.one_hot on tensors.'''		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			Optional[int]		)    ->   Tuple:
			UpperCAmelCase_      :       Optional[int]          =       labels_dense.shape[0]
			UpperCAmelCase_      :       Optional[Any]          =       numpy.arange(A		) * num_classes
			UpperCAmelCase_      :       int          =       numpy.zeros((num_labels, num_classes)		)
			UpperCAmelCase_      :       List[str]          =       1
			return labels_one_hot
@deprecated(A			,       '''Please use tf.data to implement this functionality.'''		)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Tuple=False			,       A					:			Union[str, Any]=1_0		)    ->   Tuple:
			print('''Extracting'''			,       f.name		)
			with gzip.GzipFile(fileobj=A		) as bytestream:
						UpperCAmelCase_      :       Tuple          =       _readaa(A		)
						if magic != 2_0_4_9:
									raise ValueError(
									    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)		)
						UpperCAmelCase_      :       Tuple          =       _readaa(A		)
						UpperCAmelCase_      :       Optional[Any]          =       bytestream.read(A		)
						UpperCAmelCase_      :       int          =       numpy.frombuffer(A			,       dtype=numpy.uinta		)
						if one_hot:
									return _dense_to_one_hot(A			,       A		)
						return labels


class  snake_case__      :




					@deprecated(
					    snake_case__				, '''Please use alternatives such as official/mnist/_DataSet.py'''
					    ''' from tensorflow/models.'''				, )
					def __init__(  self  :		Any				, _A  :		Tuple				, _A  :		Optional[int]				, _A  :		Optional[Any]=False				, _A  :		Dict=False				, _A  :		Union[str, Any]=dtypes.floataa				, _A  :		Dict=True				, _A  :		List[Any]=None				, )    ->							Optional[Any]:
								UpperCAmelCase_      :       List[Any]          =       random_seed.get_seed(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      )
								UpperCAmelCase_      :       int          =       dtypes.as_dtype(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:
											UpperCAmelCase_      :       Union[str, Any]          =       1_00_00
											UpperCAmelCase_      :       Tuple          =       one_hot
								else:
											assert (
											    images.shape[0] == labels.shape[0]
											), F"images.shape: {images.shape} labels.shape: {labels.shape}"
											UpperCAmelCase_      :       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
														UpperCAmelCase_      :       List[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_      :       int          =       images.astype(numpy.floataa      )
														UpperCAmelCase_      :       str          =       numpy.multiply(snake_case__				, 1.0 / 255.0      )
								UpperCAmelCase_      :       int          =       images
								UpperCAmelCase_      :       List[str]          =       labels
								UpperCAmelCase_      :       Any          =       0
								UpperCAmelCase_      :       str          =       0




					@property
					def 		A      (  self  :		int      )    ->							Optional[int]:
								return self._images




					@property
					def 		A      (  self  :		List[Any]      )    ->							Any:
								return self._labels




					@property
					def 		A      (  self  :		List[Any]      )    ->							Union[str, Any]:
								return self._num_examples




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





					def 		A      (  self  :		int				, _A  :		List[Any]				, _A  :		Optional[int]=False				, _A  :		Tuple=True      )    ->							str:
								if fake_data:
											UpperCAmelCase_      :       Any          =       [1] * 7_84
											UpperCAmelCase_      :       Tuple          =       [1] + [0] * 9 if self.one_hot else 0
											return (
											    [fake_image for _ in range(snake_case__      )],
											    [fake_label for _ in range(snake_case__      )],
											)
								UpperCAmelCase_      :       Tuple          =       self._index_in_epoch
								# Shuffle for the first epoch
								if self._epochs_completed == 0 and start == 0 and shuffle:
											UpperCAmelCase_      :       str          =       numpy.arange(self._num_examples      )
											numpy.random.shuffle(snake_case__      )
											UpperCAmelCase_      :       Optional[int]          =       self.images[perma]
											UpperCAmelCase_      :       Optional[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
											UpperCAmelCase_      :       List[Any]          =       self._num_examples - start
											UpperCAmelCase_      :       int          =       self._images[start : self._num_examples]
											UpperCAmelCase_      :       Union[str, Any]          =       self._labels[start : self._num_examples]
											# Shuffle the data
											if shuffle:
														UpperCAmelCase_      :       str          =       numpy.arange(self._num_examples      )
														numpy.random.shuffle(snake_case__      )
														UpperCAmelCase_      :       Tuple          =       self.images[perm]
														UpperCAmelCase_      :       List[Any]          =       self.labels[perm]
											# Start next epoch
											UpperCAmelCase_      :       List[str]          =       0
											UpperCAmelCase_      :       Union[str, Any]          =       batch_size - rest_num_examples
											UpperCAmelCase_      :       Union[str, Any]          =       self._index_in_epoch
											UpperCAmelCase_      :       List[Any]          =       self._images[start:end]
											UpperCAmelCase_      :       Tuple          =       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_      :       Optional[Any]          =       self._index_in_epoch
											return self._images[start:end], self._labels[start:end]






@deprecated(A			,       '''Please write your own downloading logic.'''		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Dict			,       A					:			List[Any]		)    ->   int:



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







@deprecated(
    A			,       '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Dict=False			,       A					:			List[str]=False			,       A					:			Optional[Any]=dtypes.floataa			,       A					:			int=True			,       A					:			str=5_0_0_0			,       A					:			str=None			,       A					:			List[Any]=DEFAULT_SOURCE_URL			,       )    ->   Optional[int]:



			if fake_data:

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

						UpperCAmelCase_      :       Optional[Any]          =       fake()
						UpperCAmelCase_      :       List[str]          =       fake()
						UpperCAmelCase_      :       Union[str, Any]          =       fake()
						return _Datasets(train=A			,       validation=A			,       test=A		)

			if not source_url:  # empty string check
						UpperCAmelCase_      :       Union[str, Any]          =       DEFAULT_SOURCE_URL

			UpperCAmelCase_      :       Tuple          =       "train-images-idx3-ubyte.gz"
			UpperCAmelCase_      :       Dict          =       "train-labels-idx1-ubyte.gz"
			UpperCAmelCase_      :       str          =       "t10k-images-idx3-ubyte.gz"
			UpperCAmelCase_      :       Tuple          =       "t10k-labels-idx1-ubyte.gz"

			UpperCAmelCase_      :       Optional[Any]          =       _maybe_download(
			    A			,       A			,       source_url + train_images_file		)
			with gfile.Open(A			,       '''rb'''		) as f:
						UpperCAmelCase_      :       List[str]          =       _extract_images(A		)

			UpperCAmelCase_      :       Optional[int]          =       _maybe_download(
			    A			,       A			,       source_url + train_labels_file		)
			with gfile.Open(A			,       '''rb'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       _extract_labels(A			,       one_hot=A		)

			UpperCAmelCase_      :       Union[str, Any]          =       _maybe_download(
			    A			,       A			,       source_url + test_images_file		)
			with gfile.Open(A			,       '''rb'''		) as f:
						UpperCAmelCase_      :       Optional[int]          =       _extract_images(A		)

			UpperCAmelCase_      :       Any          =       _maybe_download(
			    A			,       A			,       source_url + test_labels_file		)
			with gfile.Open(A			,       '''rb'''		) as f:
						UpperCAmelCase_      :       Tuple          =       _extract_labels(A			,       one_hot=A		)

			if not 0 <= validation_size <= len(A		):
						UpperCAmelCase_      :       Any          =       (
						    "Validation size should be between 0 and "
						    F"{len(A		)}. Received: {validation_size}."
						)
						raise ValueError(A		)

			UpperCAmelCase_      :       Optional[Any]          =       train_images[:validation_size]
			UpperCAmelCase_      :       Optional[Any]          =       train_labels[:validation_size]
			UpperCAmelCase_      :       Union[str, Any]          =       train_images[validation_size:]
			UpperCAmelCase_      :       Optional[Any]          =       train_labels[validation_size:]

			UpperCAmelCase_      :       Union[str, Any]          =       {"dtype": dtype, "reshape": reshape, "seed": seed}

			UpperCAmelCase_      :       Optional[int]          =       _DataSet(A			,       A			,       **A		)
			UpperCAmelCase_      :       Tuple          =       _DataSet(A			,       A			,       **A		)
			UpperCAmelCase_      :       Any          =       _DataSet(A			,       A			,       **A		)

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










"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


