





'''simple docstring'''



import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     MegatronBertForCausalLM,
     MegatronBertForMaskedLM,
     MegatronBertForMultipleChoice,
     MegatronBertForNextSentencePrediction,
     MegatronBertForPreTraining,
     MegatronBertForQuestionAnswering,
     MegatronBertForSequenceClassification,
     MegatronBertForTokenClassification,
     MegatronBertModel,
 )


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=7						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=99						,   lowerCamelCase=64						,   lowerCamelCase=32						,   lowerCamelCase=5						,   lowerCamelCase=4						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=512						,   lowerCamelCase=16						,   lowerCamelCase=2						,   lowerCamelCase=0.02						,   lowerCamelCase=3						,   lowerCamelCase=4						,   lowerCamelCase=None						,   ):
       lowercase     		=				parent
       lowercase     		=				batch_size
       lowercase     		=				seq_length
       lowercase     		=				is_training
       lowercase     		=				use_input_mask
       lowercase     		=				use_token_type_ids
       lowercase     		=				use_labels
       lowercase     		=				vocab_size
       lowercase     		=				hidden_size
       lowercase     		=				embedding_size
       lowercase     		=				num_hidden_layers
       lowercase     		=				num_attention_heads
       lowercase     		=				intermediate_size
       lowercase     		=				hidden_act
       lowercase     		=				hidden_dropout_prob
       lowercase     		=				attention_probs_dropout_prob
       lowercase     		=				max_position_embeddings
       lowercase     		=				type_vocab_size
       lowercase     		=				type_sequence_label_size
       lowercase     		=				initializer_range
       lowercase     		=				num_labels
       lowercase     		=				num_choices
       lowercase     		=				scope

     def 	UpperCamelCase(					self	):
       lowercase     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size	)

       lowercase     		=				None
       if self.use_input_mask:
         lowercase     		=				random_attention_mask([self.batch_size, self.seq_length]	)

       lowercase     		=				None
       if self.use_token_type_ids:
         lowercase     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size	)

       lowercase     		=				None
       lowercase     		=				None
       lowercase     		=				None
       if self.use_labels:
         lowercase     		=				ids_tensor([self.batch_size]						,   self.type_sequence_label_size	)
         lowercase     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels	)
         lowercase     		=				ids_tensor([self.batch_size]						,   self.num_choices	)

       lowercase     		=				self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

     def 	UpperCamelCase(					self	):
       return MegatronBertConfig(
           vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   embedding_size=self.embedding_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   is_decoder=UpperCAmelCase_						,   initializer_range=self.initializer_range						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				MegatronBertModel(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_	)
       lowercase     		=				model(UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_	)
       lowercase     		=				model(UpperCAmelCase_	)

       self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)	)
       self.parent.assertEqual(result.pooler_output.shape						,   (self.batch_size, self.hidden_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				MegatronBertForMaskedLM(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				MegatronBertForCausalLM(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				MegatronBertForNextSentencePrediction(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(
           UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_						,   )
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, 2)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				MegatronBertForPreTraining(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(
           UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_						,   next_sentence_label=UpperCAmelCase_						,   )
       self.parent.assertEqual(result.prediction_logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)	)
       self.parent.assertEqual(result.seq_relationship_logits.shape						,   (self.batch_size, 2)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				MegatronBertForQuestionAnswering(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(
           UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   start_positions=UpperCAmelCase_						,   end_positions=UpperCAmelCase_						,   )
       self.parent.assertEqual(result.start_logits.shape						,   (self.batch_size, self.seq_length)	)
       self.parent.assertEqual(result.end_logits.shape						,   (self.batch_size, self.seq_length)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				self.num_labels
       lowercase     		=				MegatronBertForSequenceClassification(UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				self.num_labels
       lowercase     		=				MegatronBertForTokenClassification(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				model(UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       lowercase     		=				self.num_choices
       lowercase     		=				MegatronBertForMultipleChoice(config=UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.eval()
       lowercase     		=				input_ids.unsqueeze(1	).expand(-1						,   self.num_choices						,   -1	).contiguous()
       lowercase     		=				token_type_ids.unsqueeze(1	).expand(-1						,   self.num_choices						,   -1	).contiguous()
       lowercase     		=				input_mask.unsqueeze(1	).expand(-1						,   self.num_choices						,   -1	).contiguous()
       lowercase     		=				model(
           UpperCAmelCase_						,   attention_mask=UpperCAmelCase_						,   token_type_ids=UpperCAmelCase_						,   labels=UpperCAmelCase_						,   )
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)	)






     def 	UpperCamelCase(					self	):
       lowercase     		=				self.prepare_config_and_inputs()
       (
           lowercase	
       )     		=				config_and_inputs
       lowercase     		=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class       __SCREAMING_SNAKE_CASE	(						__lowerCamelCase					,    __lowerCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   (
         (
             MegatronBertModel,
             MegatronBertForMaskedLM,
             MegatronBertForCausalLM,
             MegatronBertForMultipleChoice,
             MegatronBertForNextSentencePrediction,
             MegatronBertForPreTraining,
             MegatronBertForQuestionAnswering,
             MegatronBertForSequenceClassification,
             MegatronBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__       :						List[str]      =   (
         {
             '''feature-extraction''': MegatronBertModel,
             '''fill-mask''': MegatronBertForMaskedLM,
             '''question-answering''': MegatronBertForQuestionAnswering,
             '''text-classification''': MegatronBertForSequenceClassification,
             '''text-generation''': MegatronBertForCausalLM,
             '''token-classification''': MegatronBertForTokenClassification,
             '''zero-shot''': MegatronBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__       :						Tuple      =   True
     # test_resize_embeddings = False
     UpperCAmelCase__       :						int      =   False

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=False	):
       lowercase     		=				super()._prepare_for_class(UpperCAmelCase_						,   UpperCAmelCase_						,   return_labels=UpperCAmelCase_	)

       if return_labels:
         if model_class in get_values(UpperCAmelCase_	):
           lowercase     		=				torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)						,   dtype=torch.long						,   device=UpperCAmelCase_	)
           lowercase     		=				torch.zeros(
               self.model_tester.batch_size						,   dtype=torch.long						,   device=UpperCAmelCase_	)
       return inputs_dict

     def 	UpperCamelCase(					self	):
       lowercase     		=				MegatronBertModelTester(self	)
       lowercase     		=				ConfigTester(self						,   config_class=UpperCAmelCase_						,   hidden_size=37	)

     def 	UpperCamelCase(					self	):
       self.config_tester.run_common_tests()

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_model(*UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_masked_lm(*UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_pretraining(*UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_question_answering(*UpperCAmelCase_	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*UpperCAmelCase_	)






     def 	UpperCamelCase(					self	):
       lowercase     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_token_classification(*UpperCAmelCase_	)







def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return torch.tensor(
      lowerCamelCase_					,	dtype=torch.long					,	device=lowerCamelCase_					,	)


__magic_name__   :						List[Any]    =			1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     @slow
     @unittest.skip("Model is not available."	)
     def 	UpperCamelCase(					self	):
       lowercase     		=				'nvidia/megatron-bert-uncased-345m'
       if "MYDIR" in os.environ:
         lowercase     		=				os.path.join(os.environ["MYDIR"]						,   UpperCAmelCase_	)
       lowercase     		=				MegatronBertModel.from_pretrained(UpperCAmelCase_	)
       model.to(UpperCAmelCase_	)
       model.half()
       lowercase     		=				_long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]	)
       with torch.no_grad():
         lowercase     		=				model(UpperCAmelCase_	)[0]
       lowercase     		=				torch.Size((1, 9, 1_024)	)
       self.assertEqual(output.shape						,   UpperCAmelCase_	)

       lowercase     		=				[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
       for ii in range(3	):
         for jj in range(3	):
           lowercase     		=				output[0, ii, jj]
           lowercase     		=				expected[3 * ii + jj]
           lowercase     		=				'ii={} jj={} a={} b={}'.format(UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_	)
           self.assertTrue(math.isclose(UpperCAmelCase_						,   UpperCAmelCase_						,   rel_tol=UpperCAmelCase_						,   abs_tol=UpperCAmelCase_	)						,   msg=UpperCAmelCase_	)









'''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

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  print("Extracting"					,	f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    if magic != 20_51:
      raise ValueError(
          "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				bytestream.read(rows * cols * num_images       )
    _snake_case     		=				numpy.frombuffer(SCREAMING_SNAKE_CASE__					,	dtype=numpy.uinta       )
    _snake_case     		=				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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				labels_dense.shape[0]
  _snake_case     		=				numpy.arange(SCREAMING_SNAKE_CASE__       ) * num_classes
  _snake_case     		=				numpy.zeros((num_labels, num_classes)       )
  _snake_case     		=				1
  return labels_one_hot




@deprecated(SCREAMING_SNAKE_CASE__					,	"Please use tf.data to implement this functionality."       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  print("Extracting"					,	f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    if magic != 20_49:
      raise ValueError(
          "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				bytestream.read(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				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       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     @deprecated(
         lowerCamelCase						,   "Please use alternatives such as official/mnist/_DataSet.py"
         " from tensorflow/models."						,   )
     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=dtypes.floataa						,   lowerCamelCase=True						,   lowerCamelCase=None						,   ):
       _snake_case		,		_snake_case     		=				random_seed.get_seed(lowerCamelCase	)
       # If op level seed is not set, use whatever graph level seed is returned
       numpy.random.seed(seeda if seed is None else seeda	)
       _snake_case     		=				dtypes.as_dtype(lowerCamelCase	).base_dtype
       if dtype not in (dtypes.uinta, dtypes.floataa):
         raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype	)
       if fake_data:
         _snake_case     		=				10_000
         _snake_case     		=				one_hot
       else:
         assert (
             images.shape[0] == labels.shape[0]
         ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
         _snake_case     		=				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
           _snake_case     		=				images.reshape(
               images.shape[0]						,   images.shape[1] * images.shape[2]	)
         if dtype == dtypes.floataa:
           # Convert from [0, 255] -> [0.0, 1.0].
           _snake_case     		=				images.astype(numpy.floataa	)
           _snake_case     		=				numpy.multiply(lowerCamelCase						,   1.0 / 255.0	)
       _snake_case     		=				images
       _snake_case     		=				labels
       _snake_case     		=				0
       _snake_case     		=				0

     @property
     def 	UpperCamelCase(					self	):
       return self._images

     @property
     def 	UpperCamelCase(					self	):
       return self._labels

     @property
     def 	UpperCamelCase(					self	):
       return self._num_examples

     @property
     def 	UpperCamelCase(					self	):
       return self._epochs_completed






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False						,   lowerCamelCase=True	):
       if fake_data:
         _snake_case     		=				[1] * 784
         _snake_case     		=				[1] + [0] * 9 if self.one_hot else 0
         return (
             [fake_image for _ in range(lowerCamelCase	)],
             [fake_label for _ in range(lowerCamelCase	)],
         )
       _snake_case     		=				self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
         _snake_case     		=				numpy.arange(self._num_examples	)
         numpy.random.shuffle(lowerCamelCase	)
         _snake_case     		=				self.images[perma]
         _snake_case     		=				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
         _snake_case     		=				self._num_examples - start
         _snake_case     		=				self._images[start : self._num_examples]
         _snake_case     		=				self._labels[start : self._num_examples]
         # Shuffle the data
         if shuffle:
           _snake_case     		=				numpy.arange(self._num_examples	)
           numpy.random.shuffle(lowerCamelCase	)
           _snake_case     		=				self.images[perm]
           _snake_case     		=				self.labels[perm]
         # Start next epoch
         _snake_case     		=				0
         _snake_case     		=				batch_size - rest_num_examples
         _snake_case     		=				self._index_in_epoch
         _snake_case     		=				self._images[start:end]
         _snake_case     		=				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
         _snake_case     		=				self._index_in_epoch
         return self._images[start:end], self._labels[start:end]







@deprecated(SCREAMING_SNAKE_CASE__					,	"Please write your own downloading logic."       )
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
    gfile.MakeDirs(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				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:
      _snake_case     		=				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 				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__=50_00					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=DEFAULT_SOURCE_URL					,	):







  '''simple docstring'''



  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__       )

    _snake_case     		=				fake()
    _snake_case     		=				fake()
    _snake_case     		=				fake()
    return _Datasets(train=SCREAMING_SNAKE_CASE__					,	validation=SCREAMING_SNAKE_CASE__					,	test=SCREAMING_SNAKE_CASE__       )

  if not source_url:  # empty string check
    _snake_case     		=				DEFAULT_SOURCE_URL

  _snake_case     		=				"train-images-idx3-ubyte.gz"
  _snake_case     		=				"train-labels-idx1-ubyte.gz"
  _snake_case     		=				"t10k-images-idx3-ubyte.gz"
  _snake_case     		=				"t10k-labels-idx1-ubyte.gz"

  _snake_case     		=				_maybe_download(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	source_url + train_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE__					,	"rb"       ) as f:
    _snake_case     		=				_extract_images(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				_maybe_download(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	source_url + train_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE__					,	"rb"       ) as f:
    _snake_case     		=				_extract_labels(SCREAMING_SNAKE_CASE__					,	one_hot=SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				_maybe_download(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	source_url + test_images_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE__					,	"rb"       ) as f:
    _snake_case     		=				_extract_images(SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				_maybe_download(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	source_url + test_labels_file       )
  with gfile.Open(SCREAMING_SNAKE_CASE__					,	"rb"       ) as f:
    _snake_case     		=				_extract_labels(SCREAMING_SNAKE_CASE__					,	one_hot=SCREAMING_SNAKE_CASE__       )

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

  _snake_case     		=				train_images[:validation_size]
  _snake_case     		=				train_labels[:validation_size]
  _snake_case     		=				train_images[validation_size:]
  _snake_case     		=				train_labels[validation_size:]

  _snake_case     		=				{"dtype": dtype, "reshape": reshape, "seed": seed}

  _snake_case     		=				_DataSet(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				_DataSet(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				_DataSet(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )

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


