





'''simple docstring'''


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase					:     int												=logging.get_logger(__name__)

lowerCAmelCase					:     List[str]												='''▁'''

lowerCAmelCase					:     List[str]												={
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase					:     Optional[Any]												={
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

lowerCAmelCase					:     int												={
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
lowerCAmelCase					:     str												={
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			["input_ids", "attention_mask"]

     __A      =			[]
     __A      =			[]





     def __init__( self		:					Any	,      lowercase		:					Any	,      lowercase		:					List[Any]	,      lowercase		:					int=None	,      lowercase		:					Optional[Any]=None	,      lowercase		:					Union[str, Any]="<s>"	,      lowercase		:					Any="</s>"	,      lowercase		:					Optional[int]="</s>"	,      lowercase		:					List[Any]="<pad>"	,      lowercase		:					Optional[int]="<unk>"	,      lowercase		:					Optional[int]="m2m100"	,      lowercase		:					Optional[Dict[str, Any]] = None	,      lowercase		:					Any=8	,      **lowercase		:					int	,      ):





        """simple docstring"""




        lowercase_			:Optional[int]    			= {} if sp_model_kwargs is None else sp_model_kwargs

        lowercase_			:Optional[Any]    			= language_codes
        lowercase_			:Tuple    			= FAIRSEQ_LANGUAGE_CODES[language_codes]
        lowercase_			:List[Any]    			= {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

        lowercase_			:Union[str, Any]    			= kwargs.get("additional_special_tokens"	,      []						)
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(lowercase						)
            for lang_code in fairseq_language_code
            if self.get_lang_token(lowercase						) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=lowercase	,      tgt_lang=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      language_codes=lowercase	,      sp_model_kwargs=self.sp_model_kwargs	,      num_madeup_words=lowercase	,      **lowercase	,      )

        lowercase_			:Optional[int]    			= vocab_file
        lowercase_			:Any    			= load_json(lowercase						)
        lowercase_			:Optional[Any]    			= {v: k for k, v in self.encoder.items()}
        lowercase_			:List[str]    			= spm_file
        lowercase_			:List[str]    			= load_spm(lowercase	,      self.sp_model_kwargs						)

        lowercase_			:Optional[int]    			= len(self.encoder						)

        lowercase_			:int    			= {
            self.get_lang_token(lowercase						): self.encoder_size + i for i, lang_code in enumerate(lowercase						)
        }
        lowercase_			:List[Any]    			= {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase						)}
        lowercase_			:List[Any]    			= {v: k for k, v in self.lang_token_to_id.items()}

        lowercase_			:int    			= src_lang if src_lang is not None else "en"
        lowercase_			:Union[str, Any]    			= tgt_lang
        lowercase_			:List[Any]    			= self.get_lang_id(self._src_lang						)
        self.set_src_lang_special_tokens(self._src_lang						)

        lowercase_			:int    			= num_madeup_words





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





        """simple docstring"""




        return len(self.encoder						) + len(self.lang_token_to_id						)





     @property
     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        return self._src_lang





     @src_lang.setter
     def 							lowercase__      ( self		:					Optional[int]	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:str    			= new_src_lang
        self.set_src_lang_special_tokens(self._src_lang						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str						):





        """simple docstring"""




        return self.sp_model.encode(lowercase	,      out_type=lowercase						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict						):





        """simple docstring"""




        if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
        return self.encoder.get(lowercase	,      self.encoder[self.unk_token]						)





     def 							lowercase__      ( self		:					Any	,      lowercase		:					int						):





        """simple docstring"""




        if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
        return self.decoder.get(lowercase	,      self.unk_token						)





     def 							lowercase__      ( self		:					int	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= []
        lowercase_			:Any    			= ""
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              out_string += self.sp_model.decode(lowercase						) + token
              lowercase_			:str    			= []
           else:
              current_sub_tokens.append(lowercase						)
        out_string += self.sp_model.decode(lowercase						)
        return out_string.strip()





     def 							lowercase__      ( self		:					Any	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					bool = False						):





        """simple docstring"""




        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowercase	,      token_ids_a=lowercase	,      already_has_special_tokens=lowercase						)

        lowercase_			:List[Any]    			= [1] * len(self.prefix_tokens						)
        lowercase_			:List[Any]    			= [1] * len(self.suffix_tokens						)
        if token_ids_a is None:
           return prefix_ones + ([0] * len(lowercase						)) + suffix_ones
        return prefix_ones + ([0] * len(lowercase						)) + ([0] * len(lowercase						)) + suffix_ones





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None						):





        """simple docstring"""




        if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:str    			= {self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab





     def __getstate__( self		:					str						):





        """simple docstring"""




        lowercase_			:Any    			= self.__dict__.copy()
        lowercase_			:str    			= None
        return state





     def __setstate__( self		:					Tuple	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= d

        # for backward compatibility
        if not hasattr(self	,      "sp_model_kwargs"						):
           lowercase_			:List[str]    			= {}

        lowercase_			:List[Any]    			= load_spm(self.spm_file	,      self.sp_model_kwargs						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:Dict    			= Path(lowercase						)
        if not save_dir.is_dir():
           raise OSError(F'{save_directory} should be a directory'						)
        lowercase_			:Dict    			= save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        lowercase_			:Dict    			= save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder	,      lowercase						)

        if os.path.abspath(self.spm_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.spm_file						):
           copyfile(self.spm_file	,      lowercase						)
        elif not os.path.isfile(self.spm_file						):
           with open(lowercase	,      "wb"						) as fi:
              lowercase_			:List[str]    			= self.sp_model.serialized_model_proto()
              fi.write(lowercase						)

        return (str(lowercase						), str(lowercase						))





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					List[str]	,      lowercase		:					str = "en"	,      lowercase		:					Optional[List[str]] = None	,      lowercase		:					str = "ro"	,      **lowercase		:					Optional[int]	,      ):





        """simple docstring"""




        lowercase_			:int    			= src_lang
        lowercase_			:Optional[int]    			= tgt_lang
        self.set_src_lang_special_tokens(self.src_lang						)
        return super().prepare_seqaseq_batch(lowercase	,      lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Any	,      lowercase		:					Optional[str]	,      lowercase		:					Optional[str]	,      **lowercase		:					Union[str, Any]						):





        """simple docstring"""




        if src_lang is None or tgt_lang is None:
           raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
        lowercase_			:List[str]    			= src_lang
        lowercase_			:Union[str, Any]    			= self(lowercase	,      add_special_tokens=lowercase	,      **lowercase						)
        lowercase_			:str    			= self.get_lang_id(lowercase						)
        lowercase_			:Union[str, Any]    			= tgt_lang_id
        return inputs





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        self.set_src_lang_special_tokens(self.src_lang						)





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        self.set_tgt_lang_special_tokens(self.tgt_lang						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:List[str]    			= self.get_lang_token(lowercase						)
        lowercase_			:List[str]    			= self.lang_token_to_id[lang_token]
        lowercase_			:List[Any]    			= [self.cur_lang_id]
        lowercase_			:str    			= [self.eos_token_id]





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.get_lang_token(lowercase						)
        lowercase_			:Tuple    			= self.lang_token_to_id[lang_token]
        lowercase_			:Dict    			= [self.cur_lang_id]
        lowercase_			:List[Any]    			= [self.eos_token_id]





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					str						):





        """simple docstring"""




        return self.lang_code_to_token[lang]







     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.get_lang_token(lowercase						)
        return self.lang_token_to_id[lang_token]







def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					Dict[str, Any] ):
   lowercase_			:List[str]    			= sentencepiece.SentencePieceProcessor(**__lowerCamelCase )
   spm.Load(str(__lowerCamelCase ) )
   return spm
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   with open(__lowerCamelCase	,"r" ) as f:
      return json.load(__lowerCamelCase )


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					str ):
   with open(__lowerCamelCase	,"w" ) as f:
      json.dump(__lowerCamelCase	,__lowerCamelCase	,indent=2 )









'''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

lowerCAmelCase					:     Dict												=collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase					:     str												='''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:Tuple    			= 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 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:Union[str, Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_51:
         raise ValueError(
             "Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= _readaa(__lowerCamelCase )
      lowercase_			:Optional[Any]    			= bytestream.read(rows * cols * num_images )
      lowercase_			:List[str]    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      lowercase_			:Dict    			= data.reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,1 )
      return data
@deprecated(__lowerCamelCase	,"Please use tf.one_hot on tensors." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Tuple ):
   lowercase_			:int    			= labels_dense.shape[0]
   lowercase_			:Any    			= numpy.arange(__lowerCamelCase ) * num_classes
   lowercase_			:Optional[int]    			= numpy.zeros((num_labels, num_classes) )
   lowercase_			:List[Any]    			= 1
   return labels_one_hot
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Optional[Any]=False	,__lowerCamelCase					:					List[str]=10 ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:List[Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_49:
         raise ValueError(
             "Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
      lowercase_			:List[str]    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= bytestream.read(__lowerCamelCase )
      lowercase_			:int    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      if one_hot:
         return _dense_to_one_hot(__lowerCamelCase	,__lowerCamelCase )
      return labels
class       a_   :





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





        """simple docstring"""




        lowercase_			,							lowercase_			:Union[str, Any]    			= random_seed.get_seed(lowercase						)
        # If op level seed is not set, use whatever graph level seed is returned
        numpy.random.seed(seeda if seed is None else seeda						)
        lowercase_			:Optional[int]    			= dtypes.as_dtype(lowercase						).base_dtype
        if dtype not in (dtypes.uinta, dtypes.floataa):
           raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
        if fake_data:
           lowercase_			:List[str]    			= 10_000
           lowercase_			:int    			= one_hot
        else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
           lowercase_			:List[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
              lowercase_			:Optional[int]    			= images.reshape(
                  images.shape[0]	,      images.shape[1] * images.shape[2]						)
           if dtype == dtypes.floataa:
              # Convert from [0, 255] -> [0.0, 1.0].
              lowercase_			:Any    			= images.astype(numpy.floataa						)
              lowercase_			:Optional[int]    			= numpy.multiply(lowercase	,      1.0 / 2_55.0						)
        lowercase_			:Tuple    			= images
        lowercase_			:Any    			= labels
        lowercase_			:Dict    			= 0
        lowercase_			:Optional[Any]    			= 0





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





        """simple docstring"""




        return self._images





     @property
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        return self._labels





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





        """simple docstring"""




        return self._num_examples





     @property
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        return self._epochs_completed







     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					int	,      lowercase		:					List[Any]=False	,      lowercase		:					int=True						):





        """simple docstring"""




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







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


@deprecated(
    __lowerCamelCase	,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					int=False	,__lowerCamelCase					:					Dict=False	,__lowerCamelCase					:					List[Any]=dtypes.floataa	,__lowerCamelCase					:					List[Any]=True	,__lowerCamelCase					:					int=50_00	,__lowerCamelCase					:					Optional[Any]=None	,__lowerCamelCase					:					str=DEFAULT_SOURCE_URL	,):
   if fake_data:

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

      lowercase_			:int    			= fake()
      lowercase_			:Optional[Any]    			= fake()
      lowercase_			:Tuple    			= fake()
      return _Datasets(train=__lowerCamelCase	,validation=__lowerCamelCase	,test=__lowerCamelCase )

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

   lowercase_			:Optional[int]    			= "train-images-idx3-ubyte.gz"
   lowercase_			:Optional[int]    			= "train-labels-idx1-ubyte.gz"
   lowercase_			:Optional[Any]    			= "t10k-images-idx3-ubyte.gz"
   lowercase_			:int    			= "t10k-labels-idx1-ubyte.gz"

   lowercase_			:Tuple    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + train_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Any    			= _extract_images(__lowerCamelCase )

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

   lowercase_			:Any    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_images(__lowerCamelCase )

   lowercase_			:int    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_labels_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_labels(__lowerCamelCase	,one_hot=__lowerCamelCase )

   if not 0 <= validation_size <= len(__lowerCamelCase ):
      lowercase_			:Union[str, Any]    			= (
          "Validation size should be between 0 and "
          F'{len(__lowerCamelCase )}. Received: {validation_size}.'
      )
      raise ValueError(__lowerCamelCase )

   lowercase_			:int    			= train_images[:validation_size]
   lowercase_			:Optional[int]    			= train_labels[:validation_size]
   lowercase_			:List[str]    			= train_images[validation_size:]
   lowercase_			:int    			= train_labels[validation_size:]

   lowercase_			:Dict    			= {"dtype": dtype, "reshape": reshape, "seed": seed}

   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:str    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )

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


