





'''simple docstring'''



import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel




def 				snake_case_      (		SCREAMING_SNAKE_CASE__=32					,	SCREAMING_SNAKE_CASE__=10					,	SCREAMING_SNAKE_CASE__=1_00					,	SCREAMING_SNAKE_CASE__=10_26					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__="data/tokenized_stories_train_wikitext103.jbl"					,	SCREAMING_SNAKE_CASE__="igf_context_pairs.jbl"					,	):







  '''simple docstring'''



  set_seed(3       )
  # generate train_data and objective_set
  _snake_case		,		_snake_case     		=				generate_datasets(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	number=SCREAMING_SNAKE_CASE__					,	min_len=10_26					,	trim=SCREAMING_SNAKE_CASE__       )
  # keeps model same across runs
  set_seed(4       )
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  _snake_case     		=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )

  # load pretrained model
  _snake_case     		=				load_gpta("gpt2"       ).to(SCREAMING_SNAKE_CASE__       )
  print("computing perplexity on objective set"       )
  _snake_case     		=				compute_perplexity(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ).item()
  print("perplexity on objective set:"					,	SCREAMING_SNAKE_CASE__       )

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=15					,	SCREAMING_SNAKE_CASE__=1_28					,	SCREAMING_SNAKE_CASE__=1_00					,	SCREAMING_SNAKE_CASE__="igf_model.pt"					,	):







  '''simple docstring'''



  set_seed(42       )

  # Load pre-trained model
  _snake_case     		=				GPTaLMHeadModel.from_pretrained("gpt2"       )

  # Initialize secondary learner to use embedding weights of model
  _snake_case     		=				SecondaryLearner(SCREAMING_SNAKE_CASE__       )

  # Train secondary learner
  _snake_case     		=				train_secondary_learner(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	max_epochs=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__					,	eval_freq=1_00					,	igf_model_path=SCREAMING_SNAKE_CASE__					,	)

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=32					,	SCREAMING_SNAKE_CASE__=10_00					,	SCREAMING_SNAKE_CASE__=16					,	SCREAMING_SNAKE_CASE__=1.0					,	SCREAMING_SNAKE_CASE__=recopy_gpta					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=10					,	SCREAMING_SNAKE_CASE__="gpt2_finetuned.pt"					,	):







  '''simple docstring'''



  _snake_case     		=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )
  _snake_case     		=				RandomSampler(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				DataLoader(SCREAMING_SNAKE_CASE__					,	sampler=SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				max_steps // (len(SCREAMING_SNAKE_CASE__       )) + 1
  _snake_case     		=				0
  _snake_case     		=				torch.zeros((1, context_len)					,	dtype=torch.long					,	device=SCREAMING_SNAKE_CASE__       )
  _snake_case		,		_snake_case		,		_snake_case     		=				recopy_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(SCREAMING_SNAKE_CASE__       )
    secondary_learner.eval()
  _snake_case     		=				[]
  _snake_case     		=				0

  _snake_case     		=				[]
  _snake_case     		=				[]

  # Compute the performance of the transformer model at the beginning
  _snake_case     		=				compute_perplexity(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  test_perps.append(SCREAMING_SNAKE_CASE__       )
  print("Test perplexity, step"					,	SCREAMING_SNAKE_CASE__					,	":"					,	SCREAMING_SNAKE_CASE__       )
  for epoch in range(int(SCREAMING_SNAKE_CASE__       )       ):
    for step, example in enumerate(SCREAMING_SNAKE_CASE__       ):
      torch.cuda.empty_cache()
      _snake_case     		=				random.randint(0					,	example.size(2       ) - context_len - 1       )
      _snake_case     		=				example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      _snake_case     		=				model(SCREAMING_SNAKE_CASE__					,	labels=SCREAMING_SNAKE_CASE__       )
      _snake_case     		=				True

      if secondary_learner is not None:
        _snake_case     		=				secondary_learner.forward(
            torch.tensor(SCREAMING_SNAKE_CASE__					,	dtype=torch.long					,	device=SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )       )[0].item()
        observed_qs.append(float(SCREAMING_SNAKE_CASE__       )       )

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 10:
          _snake_case     		=				-1
        if predicted_q < threshold:
          _snake_case     		=				False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()       )       )
        _snake_case     		=				outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        _snake_case     		=				0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters()					,	3.0       )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          _snake_case     		=				compute_perplexity(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
          test_perps.append(SCREAMING_SNAKE_CASE__       )

          print("Test perplexity, step"					,	SCREAMING_SNAKE_CASE__					,	":"					,	SCREAMING_SNAKE_CASE__       )
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 60:
        break
    if max_steps > 0 and global_step > 60:
      break

    # save finetuned transformer model
  torch.save(model.state_dict()					,	SCREAMING_SNAKE_CASE__       )
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"       )

  # Required parameters
  parser.add_argument(
      "--data_dir"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	help="The input data dir. Should contain data files for WikiText."					,	)
  parser.add_argument(
      "--model_name_or_path"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	help="Path to pretrained model or model identifier from huggingface.co/models"					,	)
  parser.add_argument(
      "--data_file"					,	type=SCREAMING_SNAKE_CASE__					,	default=SCREAMING_SNAKE_CASE__					,	help=(
          "A jbl file containing tokenized data which can be split as objective dataset, "
          "train_dataset and test_dataset."
      )					,	)

  parser.add_argument(
      "--igf_data_file"					,	type=SCREAMING_SNAKE_CASE__					,	default=SCREAMING_SNAKE_CASE__					,	help="A jbl file containing the context and information gain pairs to train secondary learner."					,	)

  parser.add_argument(
      "--output_dir"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_SNAKE_CASE__					,	required=SCREAMING_SNAKE_CASE__					,	help="The output directory where the final fine-tuned model is stored."					,	)

  parser.add_argument(
      "--tokenizer_name"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_SNAKE_CASE__					,	help="Pretrained tokenizer name or path if not the same as model_name"					,	)
  parser.add_argument("--seed"					,	type=SCREAMING_SNAKE_CASE__					,	default=SCREAMING_SNAKE_CASE__					,	help="A seed for reproducible training."       )

  parser.add_argument(
      "--context_len"					,	default=32					,	type=SCREAMING_SNAKE_CASE__					,	help=(
          "The maximum total input sequence length after tokenization. Sequences longer "
          "than this will be truncated, sequences shorter will be padded."
      )					,	)

  parser.add_argument(
      "--size_objective_set"					,	default=1_00					,	type=SCREAMING_SNAKE_CASE__					,	help="number of articles that are long enough to be used as our objective set"					,	)
  parser.add_argument(
      "--eval_freq"					,	default=1_00					,	type=SCREAMING_SNAKE_CASE__					,	help="secondary model evaluation is triggered at eval_freq"       )

  parser.add_argument("--max_steps"					,	default=10_00					,	type=SCREAMING_SNAKE_CASE__					,	help="To calculate training epochs"       )

  parser.add_argument(
      "--secondary_learner_batch_size"					,	default=1_28					,	type=SCREAMING_SNAKE_CASE__					,	help="batch size of training data for secondary learner"					,	)

  parser.add_argument(
      "--batch_size"					,	default=16					,	type=SCREAMING_SNAKE_CASE__					,	help="batch size of training data of language model(gpt2) "       )

  parser.add_argument(
      "--eval_interval"					,	default=10					,	type=SCREAMING_SNAKE_CASE__					,	help=(
          "decay the selectivity of our secondary learner filter from"
          "1 standard deviation above average to 1 below average after 10 batches"
      )					,	)

  parser.add_argument(
      "--number"					,	default=1_00					,	type=SCREAMING_SNAKE_CASE__					,	help="The number of examples split to be used as objective_set/test_data"       )

  parser.add_argument(
      "--min_len"					,	default=10_26					,	type=SCREAMING_SNAKE_CASE__					,	help="The minimum length of the article to be used as objective set"       )

  parser.add_argument(
      "--secondary_learner_max_epochs"					,	default=15					,	type=SCREAMING_SNAKE_CASE__					,	help="number of epochs to train secondary learner"       )

  parser.add_argument("--trim"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_SNAKE_CASE__					,	help="truncate the example if it exceeds context length"       )

  parser.add_argument(
      "--threshold"					,	default=1.0					,	type=SCREAMING_SNAKE_CASE__					,	help=(
          "The threshold value used by secondary learner to filter the train_data and allow only"
          " informative data as input to the model"
      )					,	)

  parser.add_argument("--finetuned_model_name"					,	default="gpt2_finetuned.pt"					,	type=SCREAMING_SNAKE_CASE__					,	help="finetuned_model_name"       )

  parser.add_argument(
      "--recopy_model"					,	default=SCREAMING_SNAKE_CASE__					,	type=SCREAMING_SNAKE_CASE__					,	help="Reset the model to the original pretrained GPT-2 weights after each iteration"					,	)

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=32					,	max_steps=10					,	size_objective_set=1_00					,	min_len=10_26					,	trim=SCREAMING_SNAKE_CASE__					,	data_file="data/tokenized_stories_train_wikitext103.jbl"					,	igf_data_file="igf_context_pairs.jbl"					,	)

  # Load train data for secondary learner
  _snake_case     		=				joblib.load("data/IGF_values.jbl"       )

  # Train secondary learner
  _snake_case     		=				training_secondary_learner(
      SCREAMING_SNAKE_CASE__					,	secondary_learner_max_epochs=15					,	secondary_learner_batch_size=1_28					,	eval_freq=1_00					,	igf_model_path="igf_model.pt"					,	)

  # load pretrained gpt2 model
  _snake_case     		=				GPTaLMHeadModel.from_pretrained("gpt2"       )
  set_seed(42       )

  # Generate train and test data to train and evaluate gpt2 model
  _snake_case		,		_snake_case     		=				generate_datasets(
      context_len=32					,	file="data/tokenized_stories_train_wikitext103.jbl"					,	number=1_00					,	min_len=10_26					,	trim=SCREAMING_SNAKE_CASE__       )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	context_len=32					,	max_steps=10_00					,	batch_size=16					,	threshold=1.0					,	recopy_model=SCREAMING_SNAKE_CASE__					,	secondary_learner=SCREAMING_SNAKE_CASE__					,	eval_interval=10					,	finetuned_model_name="gpt2_finetuned.pt"					,	)


if __name__ == "__main__":
 main()









'''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__       )


