


"""simple docstring"""


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase   :List[str]						     =		get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class       A__						(    __SCREAMING_SNAKE_CASE			,  unittest.TestCase):


     """simple docstring"""
     snake_case__						:       List[str]				    =XGLMTokenizer
     snake_case__						:       str				    =XGLMTokenizerFast
     snake_case__						:       Tuple				    =True
     snake_case__						:       Optional[int]				    =True

     def 		a__    (						self:    Optional[int]						)->     Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase       :       str   =						XGLMTokenizer(_SCREAMING_SNAKE_CASE  ,		keep_accents=_SCREAMING_SNAKE_CASE						)
           tokenizer.save_pretrained(self.tmpdirname						)

     def 		a__    (						self:    Optional[Any]						)->     int:
           lowerCamelCase       :       Union[str, Any]   =						"<pad>"
           lowerCamelCase       :       List[Any]   =						1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE						)  ,		_SCREAMING_SNAKE_CASE						)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE						)  ,		_SCREAMING_SNAKE_CASE						)

     def 		a__    (						self:    str						)->     Tuple:
           lowerCamelCase       :       List[Any]   =						list(self.get_tokenizer().get_vocab().keys()						)

           self.assertEqual(vocab_keys[0]  ,		"""<s>"""						)
           self.assertEqual(vocab_keys[1]  ,		"""<pad>"""						)
           self.assertEqual(len(_SCREAMING_SNAKE_CASE						)  ,		1_008						)

     def 		a__    (						self:    Any						)->     int:
           self.assertEqual(self.get_tokenizer().vocab_size  ,		1_008						)

     def 		a__    (						self:    List[str]						)->     List[Any]:
           lowerCamelCase       :       int   =						XGLMTokenizer(_SCREAMING_SNAKE_CASE  ,		keep_accents=_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       int   =						tokenizer.tokenize("""This is a test"""						)
           self.assertListEqual(_SCREAMING_SNAKE_CASE  ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)  ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,		)

           lowerCamelCase       :       int   =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
           self.assertListEqual(
               _SCREAMING_SNAKE_CASE  ,		[
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """9""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """é""",
                   """.""",
               ]  ,		)
           lowerCamelCase       :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
           self.assertListEqual(
               _SCREAMING_SNAKE_CASE  ,		[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]  ,		)

           lowerCamelCase       :       str   =						tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						)
           self.assertListEqual(
               _SCREAMING_SNAKE_CASE  ,		[
                   SPIECE_UNDERLINE + """I""",
                   SPIECE_UNDERLINE + """was""",
                   SPIECE_UNDERLINE + """b""",
                   """or""",
                   """n""",
                   SPIECE_UNDERLINE + """in""",
                   SPIECE_UNDERLINE + """""",
                   """<unk>""",
                   """2""",
                   """0""",
                   """0""",
                   """0""",
                   """,""",
                   SPIECE_UNDERLINE + """and""",
                   SPIECE_UNDERLINE + """this""",
                   SPIECE_UNDERLINE + """is""",
                   SPIECE_UNDERLINE + """f""",
                   """al""",
                   """s""",
                   """<unk>""",
                   """.""",
               ]  ,		)

     @cached_property
     def 		a__    (						self:    Any						)->     int:
           return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""						)

     def 		a__    (						self:    Optional[Any]						)->     Optional[int]:

           with tempfile.NamedTemporaryFile() as f:
                 shutil.copyfile(_SCREAMING_SNAKE_CASE  ,		f.name						)
                 lowerCamelCase       :       int   =						XGLMTokenizer(f.name  ,		keep_accents=_SCREAMING_SNAKE_CASE						)
                 lowerCamelCase       :       List[Any]   =						pickle.dumps(_SCREAMING_SNAKE_CASE						)
           pickle.loads(_SCREAMING_SNAKE_CASE						)

     def 		a__    (						self:    Tuple						)->     Union[str, Any]:

           if not self.test_rust_tokenizer:
                 return

           lowerCamelCase       :       Any   =						self.get_tokenizer()
           lowerCamelCase       :       Union[str, Any]   =						self.get_rust_tokenizer()

           lowerCamelCase       :       Union[str, Any]   =						"I was born in 92000, and this is falsé."

           lowerCamelCase       :       Dict   =						tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
           lowerCamelCase       :       Dict   =						rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE						)
           self.assertListEqual(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       Optional[int]   =						tokenizer.encode(_SCREAMING_SNAKE_CASE  ,		add_special_tokens=_SCREAMING_SNAKE_CASE						)
           lowerCamelCase       :       Any   =						rust_tokenizer.encode(_SCREAMING_SNAKE_CASE  ,		add_special_tokens=_SCREAMING_SNAKE_CASE						)
           self.assertListEqual(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE						)

           lowerCamelCase       :       Dict   =						self.get_rust_tokenizer()
           lowerCamelCase       :       Union[str, Any]   =						tokenizer.encode(_SCREAMING_SNAKE_CASE						)
           lowerCamelCase       :       Union[str, Any]   =						rust_tokenizer.encode(_SCREAMING_SNAKE_CASE						)
           self.assertListEqual(_SCREAMING_SNAKE_CASE  ,		_SCREAMING_SNAKE_CASE						)

     @slow
     def 		a__    (						self:    int						)->     Optional[int]:
           lowerCamelCase       :       Optional[int]   =						"Hello World!"
           lowerCamelCase       :       int   =						[2, 31_227, 4_447, 35]

           self.assertListEqual(_SCREAMING_SNAKE_CASE  ,		self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE						)						)

     @slow
     def 		a__    (						self:    Optional[int]						)->     Optional[Any]:
           lowerCamelCase       :       List[Any]   =						(
               "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
               " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
           )
           # fmt: off
           lowerCamelCase       :       Any   =						[2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
           # fmt: on

           self.assertListEqual(_SCREAMING_SNAKE_CASE  ,		self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE						)						)







     @slow
     def 		a__    (						self:    Any						)->     Tuple:
           lowerCamelCase       :       Optional[int]   =						{
               "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
               "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
           }  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=_SCREAMING_SNAKE_CASE  ,		model_name="""facebook/xglm-564M"""  ,		padding=_SCREAMING_SNAKE_CASE  ,		)









"""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

__snake_case				:  Union[str, Any] =       collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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


def      _lowercase							(    __snake_case      )		->  List[Any]:
			__lowerCAmelCase :    int				=			numpy.dtype(numpy.uintaa      ).newbyteorder(">"      )
			return numpy.frombuffer(bytestream.read(4      )   ,dtype=__snake_case      )[0]


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


@deprecated(__snake_case   ,"Please use tf.one_hot on tensors."      )
def      _lowercase							(    __snake_case   ,__snake_case      )		->  Any:
			__lowerCAmelCase :    Union[str, Any]				=			labels_dense.shape[0]
			__lowerCAmelCase :    Optional[int]				=			numpy.arange(__snake_case      ) * num_classes
			__lowerCAmelCase :    int				=			numpy.zeros((num_labels, num_classes)      )
			__lowerCAmelCase :    str				=			1
			return labels_one_hot


@deprecated(__snake_case   ,"Please use tf.data to implement this functionality."      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=10      )		->  str:
			print("Extracting"   ,f.name      )
			with gzip.GzipFile(fileobj=__snake_case      ) as bytestream:
						__lowerCAmelCase :    List[str]				=			_readaa(__snake_case      )
						if magic != 2_049:
									raise ValueError(
									    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)      )
						__lowerCAmelCase :    Union[str, Any]				=			_readaa(__snake_case      )
						__lowerCAmelCase :    Union[str, Any]				=			bytestream.read(__snake_case      )
						__lowerCAmelCase :    Dict				=			numpy.frombuffer(__snake_case   ,dtype=numpy.uinta      )
						if one_hot:
									return _dense_to_one_hot(__snake_case   ,__snake_case      )
						return labels



class      A__  :




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			random_seed.get_seed(_SCREAMING_SNAKE_CASE)
				# If op level seed is not set, use whatever graph level seed is returned
				numpy.random.seed(seeda if seed is None else seeda)
				__lowerCAmelCase :    Optional[Any]				=			dtypes.as_dtype(_SCREAMING_SNAKE_CASE).base_dtype
				if dtype not in (dtypes.uinta, dtypes.floataa):
							raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
				if fake_data:
							__lowerCAmelCase :    Tuple				=			1_0000
							__lowerCAmelCase :    Optional[Any]				=			one_hot
				else:
							assert (
							    images.shape[0] == labels.shape[0]
							), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
							__lowerCAmelCase :    Union[str, 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
										__lowerCAmelCase :    List[str]				=			images.reshape(
										    images.shape[0]			,     images.shape[1] * images.shape[2])
							if dtype == dtypes.floataa:
										# Convert from [0, 255] -> [0.0, 1.0].
										__lowerCAmelCase :    Dict				=			images.astype(numpy.floataa)
										__lowerCAmelCase :    int				=			numpy.multiply(_SCREAMING_SNAKE_CASE			,     1.0 / 255.0)
				__lowerCAmelCase :    Optional[Any]				=			images
				__lowerCAmelCase :    int				=			labels
				__lowerCAmelCase :    Optional[Any]				=			0
				__lowerCAmelCase :    Optional[int]				=			0

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				return self._images

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			int:

				"""simple docstring"""

				return self._labels

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			str:

				"""simple docstring"""

				return self._num_examples

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[str]:

				"""simple docstring"""

				return self._epochs_completed







	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						List[str]=True)							->			int:

				"""simple docstring"""

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






@deprecated(__snake_case   ,"Please write your own downloading logic."      )
def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Any:
			if not gfile.Exists(__snake_case      ):
						gfile.MakeDirs(__snake_case      )
			__lowerCAmelCase :    Tuple				=			os.path.join(__snake_case   ,__snake_case      )
			if not gfile.Exists(__snake_case      ):
						urllib.request.urlretrieve(__snake_case   ,__snake_case      )  # noqa: S310
						with gfile.GFile(__snake_case      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			f.size()
						print("Successfully downloaded"   ,__snake_case   ,__snake_case   ,"bytes."      )
			return filepath





@deprecated(
    __snake_case   ,"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"      )
def      _lowercase							(    __snake_case   ,__snake_case=False   ,__snake_case=False   ,__snake_case=dtypes.floataa   ,__snake_case=True   ,__snake_case=5_000   ,__snake_case=None   ,__snake_case=DEFAULT_SOURCE_URL   ,)		->  Tuple:
			if fake_data:

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

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

			if not source_url:  # empty string check
						__lowerCAmelCase :    Optional[Any]				=			DEFAULT_SOURCE_URL

			__lowerCAmelCase :    Dict				=			"train-images-idx3-ubyte.gz"
			__lowerCAmelCase :    int				=			"train-labels-idx1-ubyte.gz"
			__lowerCAmelCase :    List[str]				=			"t10k-images-idx3-ubyte.gz"
			__lowerCAmelCase :    Any				=			"t10k-labels-idx1-ubyte.gz"

			__lowerCAmelCase :    Any				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			_extract_images(__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + train_labels_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    Optional[int]				=			_extract_labels(__snake_case   ,one_hot=__snake_case      )

			__lowerCAmelCase :    Optional[int]				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + test_images_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    List[Any]				=			_extract_images(__snake_case      )

			__lowerCAmelCase :    str				=			_maybe_download(
			    __snake_case   ,__snake_case   ,source_url + test_labels_file      )
			with gfile.Open(__snake_case   ,"rb"      ) as f:
						__lowerCAmelCase :    List[Any]				=			_extract_labels(__snake_case   ,one_hot=__snake_case      )

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

			__lowerCAmelCase :    Any				=			train_images[:validation_size]
			__lowerCAmelCase :    Any				=			train_labels[:validation_size]
			__lowerCAmelCase :    List[str]				=			train_images[validation_size:]
			__lowerCAmelCase :    Optional[Any]				=			train_labels[validation_size:]

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

			__lowerCAmelCase :    str				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Dict				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )
			__lowerCAmelCase :    Union[str, Any]				=			_DataSet(__snake_case   ,__snake_case   ,**__snake_case      )

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