



"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel



@require_tf
class      A__  :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      BlenderbotSmallConfig
	SCREAMING_SNAKE_CASE        =      {}
	SCREAMING_SNAKE_CASE        =      'gelu'

	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Dict=13			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=7			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=True			,     _SCREAMING_SNAKE_CASE:						int=False			,     _SCREAMING_SNAKE_CASE:						Dict=99			,     _SCREAMING_SNAKE_CASE:						Tuple=32			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=2			,     _SCREAMING_SNAKE_CASE:						List[Any]=4			,     _SCREAMING_SNAKE_CASE:						str=37			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=20			,     _SCREAMING_SNAKE_CASE:						Optional[int]=2			,     _SCREAMING_SNAKE_CASE:						List[Any]=1			,     _SCREAMING_SNAKE_CASE:						str=0			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			parent
				__lowerCAmelCase :    Tuple				=			batch_size
				__lowerCAmelCase :    Optional[int]				=			seq_length
				__lowerCAmelCase :    Optional[int]				=			is_training
				__lowerCAmelCase :    Optional[Any]				=			use_labels
				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    List[Any]				=			hidden_size
				__lowerCAmelCase :    List[str]				=			num_hidden_layers
				__lowerCAmelCase :    Optional[Any]				=			num_attention_heads
				__lowerCAmelCase :    Optional[int]				=			intermediate_size

				__lowerCAmelCase :    List[str]				=			hidden_dropout_prob
				__lowerCAmelCase :    Optional[Any]				=			attention_probs_dropout_prob
				__lowerCAmelCase :    List[Any]				=			max_position_embeddings
				__lowerCAmelCase :    List[Any]				=			eos_token_id
				__lowerCAmelCase :    List[str]				=			pad_token_id
				__lowerCAmelCase :    int				=			bos_token_id

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

				"""simple docstring"""

				__lowerCAmelCase :    str				=			ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size)
				__lowerCAmelCase :    Union[str, Any]				=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)			,     1)
				__lowerCAmelCase :    List[Any]				=			tf.concat([input_ids, eos_tensor]			,     axis=1)

				__lowerCAmelCase :    int				=			ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size)

				__lowerCAmelCase :    str				=			self.config_cls(
				    vocab_size=self.vocab_size			,     d_model=self.hidden_size			,     encoder_layers=self.num_hidden_layers			,     decoder_layers=self.num_hidden_layers			,     encoder_attention_heads=self.num_attention_heads			,     decoder_attention_heads=self.num_attention_heads			,     encoder_ffn_dim=self.intermediate_size			,     decoder_ffn_dim=self.intermediate_size			,     dropout=self.hidden_dropout_prob			,     attention_dropout=self.attention_probs_dropout_prob			,     max_position_embeddings=self.max_position_embeddings			,     eos_token_ids=[2]			,     bos_token_id=self.bos_token_id			,     pad_token_id=self.pad_token_id			,     decoder_start_token_id=self.pad_token_id			,     **self.config_updates			,     )
				__lowerCAmelCase :    Tuple				=			prepare_blenderbot_small_inputs_dict(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return config, inputs_dict







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			TFBlenderbotSmallModel(config=_SCREAMING_SNAKE_CASE).get_decoder()
				__lowerCAmelCase :    List[str]				=			inputs_dict["input_ids"]

				__lowerCAmelCase :    Dict				=			input_ids[:1, :]
				__lowerCAmelCase :    Optional[Any]				=			inputs_dict["attention_mask"][:1, :]
				__lowerCAmelCase :    str				=			inputs_dict["head_mask"]
				__lowerCAmelCase :    List[str]				=			1

				# first forward pass
				__lowerCAmelCase :    Union[str, Any]				=			model(_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     use_cache=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase		,      __lowerCAmelCase :    int				=			outputs.to_tuple()

				# create hypothetical next token and extent to next_input_ids
				__lowerCAmelCase :    List[Any]				=			ids_tensor((self.batch_size, 3)			,     config.vocab_size)
				__lowerCAmelCase :    List[str]				=			tf.cast(ids_tensor((self.batch_size, 3)			,     2)			,     tf.inta)

				# append to next input_ids and
				__lowerCAmelCase :    str				=			tf.concat([input_ids, next_tokens]			,     axis=-1)
				__lowerCAmelCase :    str				=			tf.concat([attention_mask, next_attn_mask]			,     axis=-1)

				__lowerCAmelCase :    Optional[int]				=			model(_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    List[str]				=			model(_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     past_key_values=_SCREAMING_SNAKE_CASE)[0]

				self.parent.assertEqual(next_tokens.shape[1]			,     output_from_past.shape[1])

				# select random slice
				__lowerCAmelCase :    Any				=			int(ids_tensor((1,)			,     output_from_past.shape[-1]))
				__lowerCAmelCase :    Union[str, Any]				=			output_from_no_past[:, -3:, random_slice_idx]
				__lowerCAmelCase :    Union[str, Any]				=			output_from_past[:, :, random_slice_idx]

				# test that outputs are equal for slice
				tf.debugging.assert_near(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     rtol=1e-3)






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,)		->  int:
			if attention_mask is None:
						__lowerCAmelCase :    Union[str, Any]				=			tf.cast(tf.math.not_equal(__snake_case   ,config.pad_token_id      )   ,tf.inta      )
			if decoder_attention_mask is None:
						__lowerCAmelCase :    List[str]				=			tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape   ,dtype=tf.inta      ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,config.pad_token_id      )   ,tf.inta      ),
						    ]   ,axis=-1   ,)
			if head_mask is None:
						__lowerCAmelCase :    str				=			tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
			if decoder_head_mask is None:
						__lowerCAmelCase :    List[str]				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
			if cross_attn_head_mask is None:
						__lowerCAmelCase :    int				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}



@require_tf
class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      (
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	SCREAMING_SNAKE_CASE        =      (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE        =      (
	    {
	        'conversational': TFBlenderbotSmallForConditionalGeneration,
	        'feature-extraction': TFBlenderbotSmallModel,
	        'summarization': TFBlenderbotSmallForConditionalGeneration,
	        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
	        'translation': TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE        =      True
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			TFBlenderbotSmallModelTester(self)
				__lowerCAmelCase :    List[str]				=			ConfigTester(self			,     config_class=_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			str:

				"""simple docstring"""

				self.config_tester.run_common_tests()







	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE)






@require_tokenizers
@require_tf
class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      [
	    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
	    '  i\'m going to throw up.\nand why is that?'
	]
	SCREAMING_SNAKE_CASE        =      'facebook/blenderbot_small-90M'

	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

				return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")

	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
				return model







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.tokenizer(self.src_text			,     return_tensors="tf")
				__lowerCAmelCase :    Optional[Any]				=			self.model.generate(
				    model_inputs.input_ids			,     attention_mask=model_inputs.attention_mask			,     num_beams=2			,     use_cache=_SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    List[str]				=			self.tokenizer.batch_decode(generated_ids.numpy()			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)[0]
				assert generated_words in (
				    "i don't know. i just feel like i'm going to throw up. it's not fun.",
				    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
				    "i'm not sure. i just feel like i've been in a bad situation.",
				)




"""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      )