





'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[int]      =   StableDiffusionLDMaDPipeline
     UpperCAmelCase__       :						List[Any]      =   TEXT_TO_IMAGE_PARAMS
     UpperCAmelCase__       :						Union[str, Any]      =   TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCAmelCase__       :						List[Any]      =   TEXT_TO_IMAGE_IMAGE_PARAMS

     def 	UpperCamelCase(					self	):
       torch.manual_seed(0	)
       lowercase     		=				UNetaDConditionModel(
           block_out_channels=(32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=4						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=32						,   )
       lowercase     		=				DDIMScheduler(
           beta_start=0.00085						,   beta_end=0.012						,   beta_schedule="scaled_linear"						,   clip_sample=lowerCamelCase						,   set_alpha_to_one=lowerCamelCase						,   )
       torch.manual_seed(0	)
       lowercase     		=				AutoencoderKL(
           block_out_channels=[32, 64]						,   in_channels=6						,   out_channels=6						,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,   latent_channels=4						,   )
       torch.manual_seed(0	)
       lowercase     		=				CLIPTextConfig(
           bos_token_id=0						,   eos_token_id=2						,   hidden_size=32						,   intermediate_size=37						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_000						,   )
       lowercase     		=				CLIPTextModel(lowerCamelCase	)
       lowercase     		=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

       lowercase     		=				{
           "unet": unet,
           "scheduler": scheduler,
           "vae": vae,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "safety_checker": None,
           "feature_extractor": None,
       }
       return components

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=0	):
       if str(lowerCamelCase	).startswith("mps"	):
         lowercase     		=				torch.manual_seed(lowerCamelCase	)
       else:
         lowercase     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       lowercase     		=				{
           "prompt": "A painting of a squirrel eating a burger",
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       lowercase     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator

       lowercase     		=				self.get_dummy_components()
       lowercase     		=				StableDiffusionLDMaDPipeline(**lowerCamelCase	)
       lowercase     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				ldmad_pipe(**lowerCamelCase	)
       lowercase		,		lowercase     		=				output.rgb, output.depth

       lowercase     		=				rgb[0, -3:, -3:, -1]
       lowercase     		=				depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       lowercase     		=				np.array(
           [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]	)
       lowercase     		=				np.array([103.46727, 85.812004, 87.849236]	)

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb	).max() < 1e-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth	).max() < 1e-2

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.get_dummy_components()
       lowercase     		=				StableDiffusionLDMaDPipeline(**lowerCamelCase	)
       lowercase     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				3 * [inputs["prompt"]]

       # forward
       lowercase     		=				ldmad_pipe(**lowerCamelCase	)
       lowercase		,		lowercase     		=				output.rgb, output.depth
       lowercase     		=				rgb_slice_a[0, -3:, -3:, -1]
       lowercase     		=				depth_slice_a[0, -3:, -1]

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				3 * [inputs.pop("prompt"	)]

       lowercase     		=				ldmad_pipe.tokenizer(
           lowerCamelCase						,   padding="max_length"						,   max_length=ldmad_pipe.tokenizer.model_max_length						,   truncation=lowerCamelCase						,   return_tensors="pt"						,   )
       lowercase     		=				text_inputs["input_ids"].to(lowerCamelCase	)

       lowercase     		=				ldmad_pipe.text_encoder(lowerCamelCase	)[0]

       lowercase     		=				prompt_embeds

       # forward
       lowercase     		=				ldmad_pipe(**lowerCamelCase	)
       lowercase		,		lowercase     		=				output.rgb, output.depth
       lowercase     		=				rgb_slice_a[0, -3:, -3:, -1]
       lowercase     		=				depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()	).max() < 1e-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()	).max() < 1e-4






     def 	UpperCamelCase(					self	):
       lowercase     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase     		=				self.get_dummy_components()
       lowercase     		=				PNDMScheduler(skip_prk_steps=lowerCamelCase	)
       lowercase     		=				StableDiffusionLDMaDPipeline(**lowerCamelCase	)
       lowercase     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				"french fries"
       lowercase     		=				ldmad_pipe(**lowerCamelCase						,   negative_prompt=lowerCamelCase	)

       lowercase		,		lowercase     		=				output.rgb, output.depth
       lowercase     		=				rgb[0, -3:, -3:, -1]
       lowercase     		=				depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       lowercase     		=				np.array(
           [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]	)
       lowercase     		=				np.array([107.84738, 84.62802, 89.962135]	)
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb	).max() < 1e-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth	).max() < 1e-2







@slow
@require_torch_gpu
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase="cpu"						,   lowerCamelCase=torch.floataa						,   lowerCamelCase=0	):
       lowercase     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       lowercase     		=				np.random.RandomState(lowerCamelCase	).standard_normal((1, 4, 64, 64)	)
       lowercase     		=				torch.from_numpy(lowerCamelCase	).to(device=lowerCamelCase						,   dtype=lowerCamelCase	)
       lowercase     		=				{
           "prompt": "a photograph of an astronaut riding a horse",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs






     def 	UpperCamelCase(					self	):
       lowercase     		=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"	)
       lowercase     		=				ldmad_pipe.to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_inputs(lowerCamelCase	)
       lowercase     		=				ldmad_pipe(**lowerCamelCase	)
       lowercase		,		lowercase     		=				output.rgb, output.depth
       lowercase     		=				rgb[0, -3:, -3:, -1].flatten()
       lowercase     		=				rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       lowercase     		=				np.array(
           [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]	)
       lowercase     		=				np.array(
           [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]	)
       assert np.abs(rgb_slice - expected_slice_rgb	).max() < 3e-3
       assert np.abs(depth_slice - expected_slice_depth	).max() < 3e-3







@nightly
@require_torch_gpu
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase="cpu"						,   lowerCamelCase=torch.floataa						,   lowerCamelCase=0	):
       lowercase     		=				torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
       lowercase     		=				np.random.RandomState(lowerCamelCase	).standard_normal((1, 4, 64, 64)	)
       lowercase     		=				torch.from_numpy(lowerCamelCase	).to(device=lowerCamelCase						,   dtype=lowerCamelCase	)
       lowercase     		=				{
           "prompt": "a photograph of an astronaut riding a horse",
           "latents": latents,
           "generator": generator,
           "num_inference_steps": 50,
           "guidance_scale": 7.5,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       lowercase     		=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"	).to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_inputs(lowerCamelCase	)
       lowercase     		=				ldmad_pipe(**lowerCamelCase	)
       lowercase		,		lowercase     		=				output.rgb, output.depth

       lowercase     		=				0.495586
       lowercase     		=				0.33795515
       lowercase     		=				112.48518
       lowercase     		=				98.489746
       assert np.abs(expected_rgb_mean - rgb.mean()	) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()	) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()	) < 1e-3
       assert np.abs(expected_depth_std - depth.std()	) < 1e-3






     def 	UpperCamelCase(					self	):
       lowercase     		=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"	).to(lowerCamelCase	)
       ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_inputs(lowerCamelCase	)
       lowercase     		=				ldmad_pipe(**lowerCamelCase	)
       lowercase		,		lowercase     		=				output.rgb, output.depth

       lowercase     		=				0.4194127
       lowercase     		=				0.35375586
       lowercase     		=				0.5638502
       lowercase     		=				0.34686103

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()	) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()	) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()	) < 1e-3
       assert np.abs(expected_depth_std - depth.std()	) < 1e-3









'''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__       )


