


"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  "▁"

lowercase_			     =  {"vocab_file": "sentencepiece.bpe.model"}

lowercase_			     =  {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

lowercase_			     =  {
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
lowercase_			     =  ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				VOCAB_FILES_NAMES
		__UpperCAmelCase		:			Optional[Any]										=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCAmelCase		:			Optional[int]										=				PRETRAINED_VOCAB_FILES_MAP
		__UpperCAmelCase		:			Dict										=				['input_ids', 'attention_mask']

		__UpperCAmelCase		:			Optional[Any]										=				[]
		__UpperCAmelCase		:			str										=				[]

		def __init__(  self							, _a							, _a="<s>"							, _a="</s>"							, _a="</s>"							, _a="<s>"							, _a="<unk>"							, _a="<pad>"							, _a="<mask>"							, _a=None							, _a=None							, _a=None							, _a = None							, _a=None							, _a=False							, **_a							, ):
				__a           =			AddedToken(_SCREAMING_SNAKE_CASE							, lstrip=_SCREAMING_SNAKE_CASE							, rstrip=_SCREAMING_SNAKE_CASE      ) if isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE      ) else mask_token

				__a           =			{} if sp_model_kwargs is None else sp_model_kwargs
				__a           =			legacy_behaviour

				super().__init__(
				    bos_token=_SCREAMING_SNAKE_CASE							, eos_token=_SCREAMING_SNAKE_CASE							, unk_token=_SCREAMING_SNAKE_CASE							, sep_token=_SCREAMING_SNAKE_CASE							, cls_token=_SCREAMING_SNAKE_CASE							, pad_token=_SCREAMING_SNAKE_CASE							, mask_token=_SCREAMING_SNAKE_CASE							, tokenizer_file=_SCREAMING_SNAKE_CASE							, src_lang=_SCREAMING_SNAKE_CASE							, tgt_lang=_SCREAMING_SNAKE_CASE							, additional_special_tokens=_SCREAMING_SNAKE_CASE							, sp_model_kwargs=self.sp_model_kwargs							, legacy_behaviour=_SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE							, )

				__a           =			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.Load(str(_SCREAMING_SNAKE_CASE      )      )
				__a           =			vocab_file
				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
				# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
				# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

				# Mimic fairseq token-to-id alignment for the first 4 token
				__a           =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				__a           =			1

				__a           =			len(self.sp_model      )
				__a           =			{
				    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE      )
				}
				__a           =			{v: k for k, v in self.lang_code_to_id.items()}
				__a           =			len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

				self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
				__a           =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
				__a           =			list(self.lang_code_to_id.keys()      )

				if additional_special_tokens is not None:
						# Only add those special tokens if they are not already there.
						self._additional_special_tokens.extend(
						    [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

				__a           =			src_lang if src_lang is not None else '''eng_Latn'''
				__a           =			self.lang_code_to_id[self._src_lang]
				__a           =			tgt_lang
				self.set_src_lang_special_tokens(self._src_lang      )

		def __getstate__(  self      ):
				__a           =			self.__dict__.copy()
				__a           =			None
				__a           =			self.sp_model.serialized_model_proto()
				return state

		def __setstate__(  self							, _a      ):
				__a           =			d

				# for backward compatibility
				if not hasattr(self							, '''sp_model_kwargs'''      ):
						__a           =			{}

				__a           =			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token

		@property
		def 			__UpperCAmelCase       (  self      ):
				return self._src_lang

		@src_lang.setter
		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			new_src_lang
				self.set_src_lang_special_tokens(self._src_lang      )

		def 			__UpperCAmelCase       (  self							, _a							, _a = None							, _a = False      ):





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

				__a           =			[1] * len(self.prefix_tokens      )
				__a           =			[1] * len(self.suffix_tokens      )
				if token_ids_a is None:
						return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE      )) + suffix_ones
				return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE      )) + ([0] * len(_SCREAMING_SNAKE_CASE      )) + suffix_ones

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):





				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 			__UpperCAmelCase       (  self							, _a							, _a = None      ):
				__a           =			[self.sep_token_id]
				__a           =			[self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep      ) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, **_a      ):





				if src_lang is None or tgt_lang is None:
						raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''      )
				__a           =			src_lang
				__a           =			self(_SCREAMING_SNAKE_CASE							, add_special_tokens=_SCREAMING_SNAKE_CASE							, return_tensors=_SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE      )
				__a           =			self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE      )
				__a           =			tgt_lang_id
				return inputs

		def 			__UpperCAmelCase       (  self      ):
				__a           =			{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
				vocab.update(self.added_tokens_encoder      )
				return vocab

		def 			__UpperCAmelCase       (  self							, _a      ):
				return self.sp_model.encode(_SCREAMING_SNAKE_CASE							, out_type=_SCREAMING_SNAKE_CASE      )

		def 			__UpperCAmelCase       (  self							, _a      ):





				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				__a           =			self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE      )

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

		def 			__UpperCAmelCase       (  self							, _a      ):





				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			''''''.join(_SCREAMING_SNAKE_CASE      ).replace(_SCREAMING_SNAKE_CASE							, ''' '''      ).strip()
				return out_string

		def 			__UpperCAmelCase       (  self							, _a							, _a = None      ):





				if not os.path.isdir(_SCREAMING_SNAKE_CASE      ):
						logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
						return
				__a           =			os.path.join(
				    _SCREAMING_SNAKE_CASE							, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

				if os.path.abspath(self.vocab_file      ) != os.path.abspath(_SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.vocab_file      ):
						copyfile(self.vocab_file							, _SCREAMING_SNAKE_CASE      )
				elif not os.path.isfile(self.vocab_file      ):
						with open(_SCREAMING_SNAKE_CASE							, '''wb'''      ) as fi:
								__a           =			self.sp_model.serialized_model_proto()
								fi.write(_SCREAMING_SNAKE_CASE      )

				return (out_vocab_file,)

		def 			__UpperCAmelCase       (  self							, _a							, _a = "eng_Latn"							, _a = None							, _a = "fra_Latn"							, **_a							, ):
				__a           =			src_lang
				__a           =			tgt_lang
				return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE      )

		def 			__UpperCAmelCase       (  self      ):
				return self.set_src_lang_special_tokens(self.src_lang      )

		def 			__UpperCAmelCase       (  self      ):
				return self.set_tgt_lang_special_tokens(self.tgt_lang      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.lang_code_to_id[src_lang]
				if self.legacy_behaviour:
						__a           =			[]
						__a           =			[self.eos_token_id, self.cur_lang_code]
				else:
						__a           =			[self.cur_lang_code]
						__a           =			[self.eos_token_id]





		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			self.lang_code_to_id[lang]
				if self.legacy_behaviour:
						__a           =			[]
						__a           =			[self.eos_token_id, self.cur_lang_code]
				else:
						__a           =			[self.cur_lang_code]
						__a           =			[self.eos_token_id]










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

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					numpy.dtype(numpy.uintaa       ).newbyteorder('''>'''       )
  return numpy.frombuffer(bytestream.read(4       ) ,			dtype=SCREAMING_SNAKE_CASE       )[0]


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.data to implement this functionality.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  print('''Extracting''' ,			f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE       ) as bytestream:
    A_      :				Union[str, Any]		     =					_readaa(SCREAMING_SNAKE_CASE       )
    if magic != 2_051:
      raise ValueError(
          '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)       )
    A_      :				str		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Tuple		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Dict		     =					_readaa(SCREAMING_SNAKE_CASE       )
    A_      :				Tuple		     =					bytestream.read(rows * cols * num_images       )
    A_      :				Dict		     =					numpy.frombuffer(SCREAMING_SNAKE_CASE ,			dtype=numpy.uinta       )
    A_      :				Union[str, Any]		     =					data.reshape(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			1       )
    return data


@deprecated(SCREAMING_SNAKE_CASE ,			'''Please use tf.one_hot on tensors.'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Optional[Any]		     =					labels_dense.shape[0]
  A_      :				List[str]		     =					numpy.arange(SCREAMING_SNAKE_CASE       ) * num_classes
  A_      :				int		     =					numpy.zeros((num_labels, num_classes)       )
  A_      :				Tuple		     =					1
  return labels_one_hot


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






class   _lowerCamelCase :




       """simple docstring"""



       @deprecated(
           _SCREAMING_SNAKE_CASE				,       '''Please use alternatives such as official/mnist/_DataSet.py'''
           ''' from tensorflow/models.'''				,       )
       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=dtypes.floataa				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=None				,       )->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				List[str]		     =					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_      :				Tuple		     =					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_      :				Optional[Any]		     =					1_0000
           A_      :				List[str]		     =					one_hot
         else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
           A_      :				List[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
             A_      :				int		     =					images.reshape(
                 images.shape[0]				,       images.shape[1] * images.shape[2]		)
           if dtype == dtypes.floataa:
             # Convert from [0, 255] -> [0.0, 1.0].
             A_      :				Optional[int]		     =					images.astype(numpy.floataa		)
             A_      :				List[str]		     =					numpy.multiply(_SCREAMING_SNAKE_CASE				,       1.0 / 2_5_5.0		)
         A_      :				int		     =					images
         A_      :				Optional[int]		     =					labels
         A_      :				List[str]		     =					0
         A_      :				List[Any]		     =					0



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




         '''simple docstring'''





         return self._images



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




         '''simple docstring'''





         return self._labels



       @property
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         return self._num_examples



       @property
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         return self._epochs_completed







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True		)->str:




         '''simple docstring'''





         if fake_data:
           A_      :				Any		     =					[1] * 784
           A_      :				int		     =					[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_      :				Any		     =					self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
           A_      :				Any		     =					numpy.arange(self._num_examples		)
           numpy.random.shuffle(_SCREAMING_SNAKE_CASE		)
           A_      :				Optional[Any]		     =					self.images[perma]
           A_      :				List[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
           A_      :				Tuple		     =					self._num_examples - start
           A_      :				Union[str, Any]		     =					self._images[start : self._num_examples]
           A_      :				List[Any]		     =					self._labels[start : self._num_examples]
           # Shuffle the data
           if shuffle:
             A_      :				List[Any]		     =					numpy.arange(self._num_examples		)
             numpy.random.shuffle(_SCREAMING_SNAKE_CASE		)
             A_      :				Tuple		     =					self.images[perm]
             A_      :				int		     =					self.labels[perm]
           # Start next epoch
           A_      :				Tuple		     =					0
           A_      :				Optional[Any]		     =					batch_size - rest_num_examples
           A_      :				Tuple		     =					self._index_in_epoch
           A_      :				Union[str, Any]		     =					self._images[start:end]
           A_      :				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
           A_      :				List[str]		     =					self._index_in_epoch
           return self._images[start:end], self._labels[start:end]




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







@deprecated(
    SCREAMING_SNAKE_CASE ,			'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''       )
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=False ,			SCREAMING_SNAKE_CASE=dtypes.floataa ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=5_000 ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=DEFAULT_SOURCE_URL ,			):
  if fake_data:

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

    A_      :				List[str]		     =					fake()
    A_      :				Tuple		     =					fake()
    A_      :				Union[str, Any]		     =					fake()
    return _Datasets(train=SCREAMING_SNAKE_CASE ,			validation=SCREAMING_SNAKE_CASE ,			test=SCREAMING_SNAKE_CASE       )

  if not source_url:  # empty string check
    A_      :				List[str]		     =					DEFAULT_SOURCE_URL

  A_      :				List[Any]		     =					'''train-images-idx3-ubyte.gz'''
  A_      :				Tuple		     =					'''train-labels-idx1-ubyte.gz'''
  A_      :				Optional[int]		     =					'''t10k-images-idx3-ubyte.gz'''
  A_      :				Any		     =					'''t10k-labels-idx1-ubyte.gz'''

  A_      :				Dict		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + train_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Optional[int]		     =					_extract_images(SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + train_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Tuple		     =					_extract_labels(SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE       )

  A_      :				Dict		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + test_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				List[str]		     =					_extract_images(SCREAMING_SNAKE_CASE       )

  A_      :				int		     =					_maybe_download(
      SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			source_url + test_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE ,			'''rb'''       ) as f:
    A_      :				Any		     =					_extract_labels(SCREAMING_SNAKE_CASE ,			one_hot=SCREAMING_SNAKE_CASE       )

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

  A_      :				Optional[Any]		     =					train_images[:validation_size]
  A_      :				Optional[Any]		     =					train_labels[:validation_size]
  A_      :				Any		     =					train_images[validation_size:]
  A_      :				Any		     =					train_labels[validation_size:]

  A_      :				Optional[Any]		     =					{'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

  A_      :				List[str]		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					_DataSet(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )

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




