



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__snake_case		:Optional[Any]   	=  logging.get_logger(__name__)

__snake_case		:str   	=  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case		:Optional[int]   	=  {
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__snake_case		:Dict   	=  {
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
__snake_case		:List[Any]   	=  {
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

__snake_case		:int   	=  {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
__snake_case		:int   	=  {
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
__snake_case		:List[str]   	=  {
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


__snake_case		:Tuple   	=  {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
__snake_case		:Optional[Any]   	=  {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
__snake_case		:str   	=  {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}

class 					_A (			__snake_case					):
       UpperCamelCase__						:					Union[str, Any]		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Optional[int]		      =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					str		      =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					List[str]		      =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class 					_A (			__snake_case					):
       UpperCamelCase__						:					Optional[int]		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					Optional[Any]		      =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					str		      =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					Optional[int]		      =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


__snake_case		:Any   	=  collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__snake_case		:Dict   	=  collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__snake_case		:List[Any]   	=  r'''\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '''

@add_start_docstrings(__snake_case					)
class 					_A :



       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  int = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = False  ,	__SCREAMING_SNAKE_CASE							:  int = False  ,	__SCREAMING_SNAKE_CASE							:  str = None  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	__SCREAMING_SNAKE_CASE							:  Tuple = None  ,	**__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	):


           '''simple docstring'''

           if titles is None and texts is None:
               return super().__call__(
                   __UpperCamelCase  ,	padding=__UpperCamelCase  ,	truncation=__UpperCamelCase  ,	max_length=__UpperCamelCase  ,	return_tensors=__UpperCamelCase  ,	return_attention_mask=__UpperCamelCase  ,	**__UpperCamelCase  ,	)
           elif titles is None or texts is None:
               __a		 =       titles if texts is None else texts
               return super().__call__(
                   __UpperCamelCase  ,	__UpperCamelCase  ,	padding=__UpperCamelCase  ,	truncation=__UpperCamelCase  ,	max_length=__UpperCamelCase  ,	return_tensors=__UpperCamelCase  ,	return_attention_mask=__UpperCamelCase  ,	**__UpperCamelCase  ,	)
           __a		 =       titles if not isinstance(__UpperCamelCase  ,	__UpperCamelCase) else [titles]
           __a		 =       texts if not isinstance(__UpperCamelCase  ,	__UpperCamelCase) else [texts]
           __a		 =       len(__UpperCamelCase)
           __a		 =       questions if not isinstance(__UpperCamelCase  ,	__UpperCamelCase) else [questions] * n_passages
           if len(__UpperCamelCase) != len(__UpperCamelCase):
               raise ValueError(
                   F'There should be as many titles than texts but got {len(__UpperCamelCase)} titles and {len(__UpperCamelCase)} texts.')
           __a		 =       super().__call__(__UpperCamelCase  ,	__UpperCamelCase  ,	padding=__UpperCamelCase  ,	truncation=__UpperCamelCase)['''input_ids''']
           __a		 =       super().__call__(__UpperCamelCase  ,	add_special_tokens=__UpperCamelCase  ,	padding=__UpperCamelCase  ,	truncation=__UpperCamelCase)['''input_ids''']
           __a		 =       {
               '''input_ids''': [
                   (encoded_question_and_title + encoded_text)[:max_length]
                   if max_length is not None and truncation
                   else encoded_question_and_title + encoded_text
                   for encoded_question_and_title, encoded_text in zip(__UpperCamelCase  ,	__UpperCamelCase)
               ]
           }
           if return_attention_mask is not False:
               __a		 =       []
               for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
               __a		 =       attention_mask
           return self.pad(__UpperCamelCase  ,	padding=__UpperCamelCase  ,	max_length=__UpperCamelCase  ,	return_tensors=__UpperCamelCase)



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 16  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 64  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any] = 4  ,	):


           '''simple docstring'''

           __a		 =       reader_input['''input_ids''']
           __a ,     __a ,     __a		 =       reader_output[:3]
           __a		 =       len(__UpperCamelCase)
           __a		 =       sorted(range(__UpperCamelCase)  ,	reverse=__UpperCamelCase  ,	key=relevance_logits.__getitem__)
           __a		 =       []
           for doc_id in sorted_docs:
               __a		 =       list(input_ids[doc_id])
               # assuming question & title information is at the beginning of the sequence
               __a		 =       sequence_ids.index(self.sep_token_id  ,	2) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                   __a		 =       sequence_ids.index(self.pad_token_id)
               else:
                   __a		 =       len(__UpperCamelCase)

               __a		 =       self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len]  ,	end_logits=end_logits[doc_id][passage_offset:sequence_len]  ,	max_answer_length=__UpperCamelCase  ,	top_spans=__UpperCamelCase  ,	)
               for start_index, end_index in best_spans:
                   start_index += passage_offset
                   end_index += passage_offset
                   nbest_spans_predictions.append(
                       DPRSpanPrediction(
                           span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]  ,	relevance_score=relevance_logits[doc_id]  ,	doc_id=__UpperCamelCase  ,	start_index=__UpperCamelCase  ,	end_index=__UpperCamelCase  ,	text=self.decode(sequence_ids[start_index : end_index + 1])  ,	))
               if len(__UpperCamelCase) >= num_spans:
                   break
           return nbest_spans_predictions[:num_spans]





       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	):


           '''simple docstring'''

           __a		 =       []
           for start_index, start_score in enumerate(__UpperCamelCase):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                   scores.append(((start_index, start_index + answer_length), start_score + end_score))
           __a		 =       sorted(__UpperCamelCase  ,	key=lambda __SCREAMING_SNAKE_CASE: x[1]  ,	reverse=__UpperCamelCase)
           __a		 =       []
           for (start_index, end_index), score in scores:
               if start_index > end_index:
                   raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]')
               __a		 =       end_index - start_index + 1
               if length > max_answer_length:
                   raise ValueError(F'Span is too long: {length} > {max_answer_length}')
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals):
                   continue
               chosen_span_intervals.append((start_index, end_index))

               if len(__UpperCamelCase) == top_spans:
                   break
           return chosen_span_intervals


@add_end_docstrings(__snake_case					)
class 					_A (			__snake_case   ,__snake_case					):
       UpperCamelCase__						:					List[str]		      =		VOCAB_FILES_NAMES
       UpperCamelCase__						:					int		      =		READER_PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__						:					Tuple		      =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__						:					Optional[Any]		      =		READER_PRETRAINED_INIT_CONFIGURATION
       UpperCamelCase__						:					List[Any]		      =		['''input_ids''', '''attention_mask''']










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

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__snake_case		:Optional[int]   	=  '''https://storage.googleapis.com/cvdf-datasets/mnist/'''





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       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   __snake_case			(    _UpperCAmelCase       ):
    print('''Extracting'''     ,				f.name       )
    with gzip.GzipFile(fileobj=_UpperCAmelCase       ) as bytestream:
        __a		 =       _readaa(_UpperCAmelCase       )
        if magic != 2051:
            raise ValueError(
                '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)       )
        __a		 =       _readaa(_UpperCAmelCase       )
        __a		 =       _readaa(_UpperCAmelCase       )
        __a		 =       _readaa(_UpperCAmelCase       )
        __a		 =       bytestream.read(rows * cols * num_images       )
        __a		 =       numpy.frombuffer(_UpperCAmelCase     ,				dtype=numpy.uinta       )
        __a		 =       data.reshape(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				1       )
        return data





@deprecated(_UpperCAmelCase     ,				'''Please use tf.one_hot on tensors.'''       )
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       labels_dense.shape[0]
    __a		 =       numpy.arange(_UpperCAmelCase       ) * num_classes
    __a		 =       numpy.zeros((num_labels, num_classes)       )
    __a		 =       1
    return labels_one_hot





@deprecated(_UpperCAmelCase     ,				'''Please use tf.data to implement this functionality.'''       )
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=10       ):
    print('''Extracting'''     ,				f.name       )
    with gzip.GzipFile(fileobj=_UpperCAmelCase       ) as bytestream:
        __a		 =       _readaa(_UpperCAmelCase       )
        if magic != 2049:
            raise ValueError(
                '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)       )
        __a		 =       _readaa(_UpperCAmelCase       )
        __a		 =       bytestream.read(_UpperCAmelCase       )
        __a		 =       numpy.frombuffer(_UpperCAmelCase     ,				dtype=numpy.uinta       )
        if one_hot:
            return _dense_to_one_hot(_UpperCAmelCase     ,				_UpperCAmelCase       )
        return labels

class 					_A :



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


           '''simple docstring'''

           __a ,     __a		 =       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)
           __a		 =       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:
               __a		 =       10_000
               __a		 =       one_hot
           else:
               assert (
                   images.shape[0] == labels.shape[0]
               ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
               __a		 =       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
                   __a		 =       images.reshape(
                       images.shape[0]  ,	images.shape[1] * images.shape[2])
               if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   __a		 =       images.astype(numpy.floataa)
                   __a		 =       numpy.multiply(__SCREAMING_SNAKE_CASE  ,	1.0 / 2_55.0)
           __a		 =       images
           __a		 =       labels
           __a		 =       0
           __a		 =       0



       @property
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           return self._images



       @property
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           return self._labels



       @property
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           return self._num_examples



       @property
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           return self._epochs_completed





       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple=False  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=True):


           '''simple docstring'''

           if fake_data:
               __a		 =       [1] * 784
               __a		 =       [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)],
               )
           __a		 =       self._index_in_epoch
           # Shuffle for the first epoch
           if self._epochs_completed == 0 and start == 0 and shuffle:
               __a		 =       numpy.arange(self._num_examples)
               numpy.random.shuffle(__SCREAMING_SNAKE_CASE)
               __a		 =       self.images[perma]
               __a		 =       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
               __a		 =       self._num_examples - start
               __a		 =       self._images[start : self._num_examples]
               __a		 =       self._labels[start : self._num_examples]
               # Shuffle the data
               if shuffle:
                   __a		 =       numpy.arange(self._num_examples)
                   numpy.random.shuffle(__SCREAMING_SNAKE_CASE)
                   __a		 =       self.images[perm]
                   __a		 =       self.labels[perm]
               # Start next epoch
               __a		 =       0
               __a		 =       batch_size - rest_num_examples
               __a		 =       self._index_in_epoch
               __a		 =       self._images[start:end]
               __a		 =       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
               __a		 =       self._index_in_epoch
               return self._images[start:end], self._labels[start:end]





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





@deprecated(
    _UpperCAmelCase     ,				'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''       )
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=False     ,				_UpperCAmelCase=dtypes.floataa     ,				_UpperCAmelCase=True     ,				_UpperCAmelCase=5000     ,				_UpperCAmelCase=None     ,				_UpperCAmelCase=DEFAULT_SOURCE_URL     ,				):
    if fake_data:

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

        __a		 =       fake()
        __a		 =       fake()
        __a		 =       fake()
        return _Datasets(train=_UpperCAmelCase     ,				validation=_UpperCAmelCase     ,				test=_UpperCAmelCase       )

    if not source_url:  # empty string check
        __a		 =       DEFAULT_SOURCE_URL

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

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

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

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

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

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

    __a		 =       train_images[:validation_size]
    __a		 =       train_labels[:validation_size]
    __a		 =       train_images[validation_size:]
    __a		 =       train_labels[validation_size:]

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

    __a		 =       _DataSet(_UpperCAmelCase     ,				_UpperCAmelCase     ,				**_UpperCAmelCase       )
    __a		 =       _DataSet(_UpperCAmelCase     ,				_UpperCAmelCase     ,				**_UpperCAmelCase       )
    __a		 =       _DataSet(_UpperCAmelCase     ,				_UpperCAmelCase     ,				**_UpperCAmelCase       )

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





