





import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCamelCase	:					str			  =					16
__lowerCamelCase	:					Union[str, Any]			  =					32



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase = 16							,       _lowerCAmelCase = "bert-base-cased"					)						->			Union[str, Any]:
 UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained(_lowerCAmelCase					)
 UpperCamelCase     : Dict	     =       load_dataset("glue"							,       "mrpc"					)

 def tokenize_function(_lowerCAmelCase					):
  # max_length=None => use the model max length (it's actually the default)
  UpperCamelCase     : Any	     =       tokenizer(examples["sentence1"]							,       examples["sentence2"]							,       truncation=_lowerCAmelCase							,       max_length=_lowerCAmelCase					)
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 UpperCamelCase     : Any	     =       datasets.map(
     _lowerCAmelCase							,       batched=_lowerCAmelCase							,       remove_columns=["idx", "sentence1", "sentence2"]							,       load_from_cache_file=_lowerCAmelCase					)

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 UpperCamelCase     : List[str]	     =       tokenized_datasets.rename_column("label"							,       "labels"					)

 def collate_fn(_lowerCAmelCase					):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(_lowerCAmelCase							,       padding="max_length"							,       max_length=128							,       return_tensors="pt"					)
  return tokenizer.pad(_lowerCAmelCase							,       padding="longest"							,       return_tensors="pt"					)

 # Instantiate dataloaders.
 UpperCamelCase     : List[str]	     =       DataLoader(
     tokenized_datasets["train"]							,       shuffle=_lowerCAmelCase							,       collate_fn=_lowerCAmelCase							,       batch_size=_lowerCAmelCase					)
 UpperCamelCase     : List[Any]	     =       DataLoader(
     tokenized_datasets["validation"]							,       shuffle=_lowerCAmelCase							,       collate_fn=_lowerCAmelCase							,       batch_size=_lowerCAmelCase					)

 return train_dataloader, eval_dataloader



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 model.eval()
 UpperCamelCase     : Any	     =       0
 for step, batch in enumerate(_lowerCAmelCase					):
  # We could avoid this line since we set the accelerator with `device_placement=True`.
  batch.to(accelerator.device					)
  with torch.no_grad():
   UpperCamelCase     : List[Any]	     =       model(**_lowerCAmelCase					)
  UpperCamelCase     : Union[str, Any]	     =       outputs.logits.argmax(dim=-1					)
  # It is slightly faster to call this once, than multiple times
  UpperCamelCase     ,   UpperCamelCase     : List[str]	     =       accelerator.gather(
      (predictions, batch["labels"])					)  # If we are in a multiprocess environment, the last batch has duplicates
  if accelerator.use_distributed:
   if step == len(_lowerCAmelCase					) - 1:
    UpperCamelCase     : Union[str, Any]	     =       predictions[: len(eval_dataloader.dataset					) - samples_seen]
    UpperCamelCase     : Tuple	     =       references[: len(eval_dataloader.dataset					) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=_lowerCAmelCase							,       references=_lowerCAmelCase							,       )

 UpperCamelCase     : List[str]	     =       metric.compute()
 return eval_metric["accuracy"]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Union[str, Any]:
 # Initialize accelerator
 UpperCamelCase     : List[Any]	     =       Accelerator()

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCamelCase     : List[str]	     =       config["lr"]
 UpperCamelCase     : Union[str, Any]	     =       int(config["num_epochs"]					)
 UpperCamelCase     : int	     =       int(config["seed"]					)
 UpperCamelCase     : Tuple	     =       int(config["batch_size"]					)
 UpperCamelCase     : Tuple	     =       args.model_name_or_path

 set_seed(_lowerCAmelCase					)
 UpperCamelCase     ,   UpperCamelCase     : Union[str, Any]	     =       get_dataloaders(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase     : int	     =       AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase							,       return_dict=_lowerCAmelCase					)

 # Instantiate optimizer
 UpperCamelCase     : List[str]	     =       (
     AdamW
     if accelerator.state.deepspeed_plugin is None
     or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
     else DummyOptim
 )
 UpperCamelCase     : Tuple	     =       optimizer_cls(params=model.parameters()							,       lr=_lowerCAmelCase					)

 if accelerator.state.deepspeed_plugin is not None:
  UpperCamelCase     : Any	     =       accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  UpperCamelCase     : List[Any]	     =       1
 UpperCamelCase     : Union[str, Any]	     =       (len(_lowerCAmelCase					) * num_epochs) // gradient_accumulation_steps

 # Instantiate scheduler
 if (
     accelerator.state.deepspeed_plugin is None
     or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
 ):
  UpperCamelCase     : Dict	     =       get_linear_schedule_with_warmup(
      optimizer=_lowerCAmelCase							,       num_warmup_steps=0							,       num_training_steps=_lowerCAmelCase							,       )
 else:
  UpperCamelCase     : Optional[int]	     =       DummyScheduler(_lowerCAmelCase							,       total_num_steps=_lowerCAmelCase							,       warmup_num_steps=0					)

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Union[str, Any]	     =       accelerator.prepare(
     _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 # We need to keep track of how many total steps we have iterated over
 UpperCamelCase     : int	     =       0
 # We also need to keep track of the stating epoch so files are named properly
 UpperCamelCase     : Optional[Any]	     =       0
 UpperCamelCase     : int	     =       evaluate.load("glue"							,       "mrpc"					)
 UpperCamelCase     : List[str]	     =       num_epochs

 if args.partial_train_epoch is not None:
  UpperCamelCase     : Any	     =       args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint					)
  UpperCamelCase     : List[str]	     =       args.resume_from_checkpoint.split("epoch_"					)[1]
  UpperCamelCase     : List[str]	     =       ""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  UpperCamelCase     : List[Any]	     =       int(_lowerCAmelCase					) + 1
  UpperCamelCase     : List[Any]	     =       evaluation_loop(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
  accelerator.print("resumed checkpoint performance:"							,       _lowerCAmelCase					)
  accelerator.print("resumed checkpoint's scheduler's lr:"							,       lr_scheduler.get_lr()[0]					)
  accelerator.print("resumed optimizers's lr:"							,       optimizer.param_groups[0]["lr"]					)
  with open(os.path.join(args.output_dir							,       F"""state_{starting_epoch-1}.json"""					)							,       "r"					) as f:
   UpperCamelCase     : Union[str, Any]	     =       json.load(_lowerCAmelCase					)
   assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
   assert (
       resumed_state["lr"] == lr_scheduler.get_lr()[0]
   ), "Scheduler learning rate mismatch, loading from checkpoint failed"
   assert (
       resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
   ), "Optimizer learning rate mismatch, loading from checkpoint failed"
   assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
   return

    # Now we train the model
 UpperCamelCase     : List[Any]	     =       {}
 for epoch in range(_lowerCAmelCase							,       _lowerCAmelCase					):
  model.train()
  for step, batch in enumerate(_lowerCAmelCase					):
   UpperCamelCase     : Tuple	     =       model(**_lowerCAmelCase					)
   UpperCamelCase     : List[str]	     =       outputs.loss
   UpperCamelCase     : Optional[Any]	     =       loss / gradient_accumulation_steps
   accelerator.backward(_lowerCAmelCase					)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  UpperCamelCase     : Dict	     =       F"""epoch_{epoch}"""
  UpperCamelCase     : Union[str, Any]	     =       os.path.join(args.output_dir							,       _lowerCAmelCase					)
  accelerator.save_state(_lowerCAmelCase					)
  UpperCamelCase     : List[Any]	     =       evaluation_loop(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)
  UpperCamelCase     : str	     =       accuracy
  UpperCamelCase     : Optional[int]	     =       lr_scheduler.get_lr()[0]
  UpperCamelCase     : Dict	     =       optimizer.param_groups[0]["lr"]
  UpperCamelCase     : Optional[Any]	     =       epoch
  UpperCamelCase     : Optional[Any]	     =       overall_step
  accelerator.print(F"""epoch {epoch}:"""							,       _lowerCAmelCase					)

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   with open(os.path.join(args.output_dir							,       F"""state_{epoch}.json"""					)							,       "w"					) as f:
    json.dump(_lowerCAmelCase							,       _lowerCAmelCase					)



def 		A_    (					)						->			str:
 UpperCamelCase     : Dict	     =       argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."					)
 parser.add_argument(
     "--model_name_or_path"							,       type=_lowerCAmelCase							,       default="bert-base-cased"							,       help="Path to pretrained model or model identifier from huggingface.co/models."							,       required=_lowerCAmelCase							,       )
 parser.add_argument(
     "--output_dir"							,       type=_lowerCAmelCase							,       default="."							,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."							,       )
 parser.add_argument(
     "--resume_from_checkpoint"							,       type=_lowerCAmelCase							,       default=_lowerCAmelCase							,       help="If the training should continue from a checkpoint folder."							,       )
 parser.add_argument(
     "--partial_train_epoch"							,       type=_lowerCAmelCase							,       default=_lowerCAmelCase							,       help="If passed, the training will stop after this number of epochs."							,       )
 parser.add_argument(
     "--num_epochs"							,       type=_lowerCAmelCase							,       default=2							,       help="Number of train epochs."							,       )
 UpperCamelCase     : Optional[int]	     =       parser.parse_args()
 UpperCamelCase     : str	     =       {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

 training_function(_lowerCAmelCase							,       _lowerCAmelCase					)


if __name__ == "__main__":
    main()







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

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

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



def 		A_    (					_lowerCAmelCase					)						->			Optional[Any]:
 UpperCamelCase     : Tuple	     =       numpy.dtype(numpy.uintaa					).newbyteorder(">"					)
 return numpy.frombuffer(bytestream.read(4					)							,       dtype=_lowerCAmelCase					)[0]



@deprecated(_lowerCAmelCase							,       "Please use tf.data to implement this functionality."					)
def 		A_    (					_lowerCAmelCase					)						->			int:
 print("Extracting"							,       f.name					)
 with gzip.GzipFile(fileobj=_lowerCAmelCase					) as bytestream:
  UpperCamelCase     : Dict	     =       _readaa(_lowerCAmelCase					)
  if magic != 2051:
   raise ValueError(
       "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)					)
  UpperCamelCase     : Optional[int]	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : int	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : Union[str, Any]	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : List[Any]	     =       bytestream.read(rows * cols * num_images					)
  UpperCamelCase     : List[str]	     =       numpy.frombuffer(_lowerCAmelCase							,       dtype=numpy.uinta					)
  UpperCamelCase     : Optional[Any]	     =       data.reshape(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       1					)
  return data



@deprecated(_lowerCAmelCase							,       "Please use tf.one_hot on tensors."					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : List[str]	     =       labels_dense.shape[0]
 UpperCamelCase     : str	     =       numpy.arange(_lowerCAmelCase					) * num_classes
 UpperCamelCase     : Optional[Any]	     =       numpy.zeros((num_labels, num_classes)					)
 UpperCamelCase     : Dict	     =       1
 return labels_one_hot



@deprecated(_lowerCAmelCase							,       "Please use tf.data to implement this functionality."					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=10					)						->			str:
 print("Extracting"							,       f.name					)
 with gzip.GzipFile(fileobj=_lowerCAmelCase					) as bytestream:
  UpperCamelCase     : int	     =       _readaa(_lowerCAmelCase					)
  if magic != 2049:
   raise ValueError(
       "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)					)
  UpperCamelCase     : List[str]	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : List[Any]	     =       bytestream.read(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       numpy.frombuffer(_lowerCAmelCase							,       dtype=numpy.uinta					)
  if one_hot:
   return _dense_to_one_hot(_lowerCAmelCase							,       _lowerCAmelCase					)
  return labels



class  A__       :





      @deprecated(
          A_	,							"Please use alternatives such as official/mnist/_DataSet.py"
          " from tensorflow/models."	,							)
      def __init__(			self	,							A_	,							A_	,							A_=False	,							A_=False	,							A_=dtypes.floataa	,							A_=True	,							A_=None	,							):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : int	     =       random_seed.get_seed(A_       )
       # If op level seed is not set, use whatever graph level seed is returned
       numpy.random.seed(seeda if seed is None else seeda       )
       UpperCamelCase     : Optional[Any]	     =       dtypes.as_dtype(A_       ).base_dtype
       if dtype not in (dtypes.uinta, dtypes.floataa):
        raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype       )
       if fake_data:
        UpperCamelCase     : List[str]	     =       1_0000
        UpperCamelCase     : int	     =       one_hot
       else:
        assert (
            images.shape[0] == labels.shape[0]
        ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
        UpperCamelCase     : Optional[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
         UpperCamelCase     : int	     =       images.reshape(
             images.shape[0]	,							images.shape[1] * images.shape[2]       )
        if dtype == dtypes.floataa:
         # Convert from [0, 255] -> [0.0, 1.0].
         UpperCamelCase     : str	     =       images.astype(numpy.floataa       )
         UpperCamelCase     : str	     =       numpy.multiply(A_	,							1.0 / 2_55.0       )
       UpperCamelCase     : Optional[int]	     =       images
       UpperCamelCase     : str	     =       labels
       UpperCamelCase     : Optional[Any]	     =       0
       UpperCamelCase     : Optional[int]	     =       0





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._images





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._labels





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._num_examples





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._epochs_completed





      def 	__UpperCamelCase(			self	,							A_	,							A_=False	,							A_=True       ):
       '''simple docstring'''




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



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



@deprecated(
    _lowerCAmelCase							,       "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=dtypes.floataa							,       _lowerCAmelCase=True							,       _lowerCAmelCase=5000							,       _lowerCAmelCase=None							,       _lowerCAmelCase=DEFAULT_SOURCE_URL							,       )						->			List[str]:
 if fake_data:

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

  UpperCamelCase     : Any	     =       fake()
  UpperCamelCase     : List[str]	     =       fake()
  UpperCamelCase     : Union[str, Any]	     =       fake()
  return _Datasets(train=_lowerCAmelCase							,       validation=_lowerCAmelCase							,       test=_lowerCAmelCase					)

 if not source_url:  # empty string check
  UpperCamelCase     : str	     =       DEFAULT_SOURCE_URL

 UpperCamelCase     : List[str]	     =       "train-images-idx3-ubyte.gz"
 UpperCamelCase     : Optional[int]	     =       "train-labels-idx1-ubyte.gz"
 UpperCamelCase     : List[str]	     =       "t10k-images-idx3-ubyte.gz"
 UpperCamelCase     : Union[str, Any]	     =       "t10k-labels-idx1-ubyte.gz"

 UpperCamelCase     : Optional[int]	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + train_images_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : List[str]	     =       _extract_images(_lowerCAmelCase					)

 UpperCamelCase     : Dict	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + train_labels_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : List[Any]	     =       _extract_labels(_lowerCAmelCase							,       one_hot=_lowerCAmelCase					)

 UpperCamelCase     : Any	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + test_images_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : Any	     =       _extract_images(_lowerCAmelCase					)

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

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

 UpperCamelCase     : str	     =       train_images[:validation_size]
 UpperCamelCase     : int	     =       train_labels[:validation_size]
 UpperCamelCase     : List[str]	     =       train_images[validation_size:]
 UpperCamelCase     : Union[str, Any]	     =       train_labels[validation_size:]

 UpperCamelCase     : List[str]	     =       {"dtype": dtype, "reshape": reshape, "seed": seed}

 UpperCamelCase     : List[str]	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)
 UpperCamelCase     : Any	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)

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