





# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class 					__A    (    A_			,				A_			,				A_			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						StableDiffusionControlNetImgaImgPipeline
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCAmelCase_							     =						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""} )
			lowerCAmelCase_							     =						IMAGE_TO_IMAGE_IMAGE_PARAMS





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  sample_size=3_2       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=3_2       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		ControlNetModel(
				    block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  in_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  cross_attention_dim=3_2       ,  conditioning_embedding_out_channels=(1_6, 3_2)       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		DDIMScheduler(
				    beta_start=0.0_0085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''       ,  clip_sample=A_       ,  set_alpha_to_one=A_       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		AutoencoderKL(
				    block_out_channels=[3_2, 6_4]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		CLIPTextConfig(
				    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  )
				lowerCamelCase__								=		CLIPTextModel(A_							)
				lowerCamelCase__								=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

				lowerCamelCase__								=		{
				    '''unet''': unet,
				    '''controlnet''': controlnet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if str(A_							).startswith('''mps'''							):
					lowerCamelCase__								=		torch.manual_seed(A_							)
				else:
					lowerCamelCase__								=		torch.Generator(device=A_							).manual_seed(A_							)

				lowerCamelCase__								=		2
				lowerCamelCase__								=		randn_tensor(
				    (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)       ,  generator=A_       ,  device=torch.device(A_							)       ,  )
				lowerCamelCase__								=		floats_tensor(control_image.shape       ,  rng=random.Random(A_							)							).to(A_							)
				lowerCamelCase__								=		image.cpu().permute(0       ,  2       ,  3       ,  1							)[0]
				lowerCamelCase__								=		Image.fromarray(np.uinta(A_							)							).convert('''RGB'''							).resize((6_4, 6_4)							)
				lowerCamelCase__								=		{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''image''': image,
				    '''control_image''': control_image,
				}

				return inputs





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3							)





			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()       ,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,  )
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self._test_inference_batch_single_identical(expected_max_diff=2E-3							)






class 					__A    (    A_			,				A_			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						StableDiffusionControlNetImgaImgPipeline
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCAmelCase_							     =						frozenset([] )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  sample_size=3_2       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=3_2       ,  )
				torch.manual_seed(0							)

				def init_weights(__lowerCAmelCase							):
					if isinstance(A_       ,  torch.nn.Convad							):
						torch.nn.init.normal(m.weight							)
						m.bias.data.fill_(1.0							)

				lowerCamelCase__								=		ControlNetModel(
				    block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  in_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  cross_attention_dim=3_2       ,  conditioning_embedding_out_channels=(1_6, 3_2)       ,  )
				controlneta.controlnet_down_blocks.apply(A_							)

				torch.manual_seed(0							)
				lowerCamelCase__								=		ControlNetModel(
				    block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  in_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  cross_attention_dim=3_2       ,  conditioning_embedding_out_channels=(1_6, 3_2)       ,  )
				controlneta.controlnet_down_blocks.apply(A_							)

				torch.manual_seed(0							)
				lowerCamelCase__								=		DDIMScheduler(
				    beta_start=0.0_0085       ,  beta_end=0.012       ,  beta_schedule='''scaled_linear'''       ,  clip_sample=A_       ,  set_alpha_to_one=A_       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		AutoencoderKL(
				    block_out_channels=[3_2, 6_4]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
				torch.manual_seed(0							)
				lowerCamelCase__								=		CLIPTextConfig(
				    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1_0_0_0       ,  )
				lowerCamelCase__								=		CLIPTextModel(A_							)
				lowerCamelCase__								=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

				lowerCamelCase__								=		MultiControlNetModel([controlneta, controlneta]							)

				lowerCamelCase__								=		{
				    '''unet''': unet,
				    '''controlnet''': controlnet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if str(A_							).startswith('''mps'''							):
					lowerCamelCase__								=		torch.manual_seed(A_							)
				else:
					lowerCamelCase__								=		torch.Generator(device=A_							).manual_seed(A_							)

				lowerCamelCase__								=		2

				lowerCamelCase__								=		[
				    randn_tensor(
				        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)       ,  generator=A_       ,  device=torch.device(A_							)       ,  ),
				    randn_tensor(
				        (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)       ,  generator=A_       ,  device=torch.device(A_							)       ,  ),
				]

				lowerCamelCase__								=		floats_tensor(control_image[0].shape       ,  rng=random.Random(A_							)							).to(A_							)
				lowerCamelCase__								=		image.cpu().permute(0       ,  2       ,  3       ,  1							)[0]
				lowerCamelCase__								=		Image.fromarray(np.uinta(A_							)							).convert('''RGB'''							).resize((6_4, 6_4)							)
				lowerCamelCase__								=		{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''image''': image,
				    '''control_image''': control_image,
				}

				return inputs





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_dummy_components()
				lowerCamelCase__								=		self.pipeline_class(**A_							)
				pipe.to(A_							)

				lowerCamelCase__								=		10.0
				lowerCamelCase__								=		4

				lowerCamelCase__								=		self.get_dummy_inputs(A_							)
				lowerCamelCase__								=		steps
				lowerCamelCase__								=		scale
				lowerCamelCase__								=		pipe(**A_							)[0]

				lowerCamelCase__								=		self.get_dummy_inputs(A_							)
				lowerCamelCase__								=		steps
				lowerCamelCase__								=		scale
				lowerCamelCase__								=		pipe(**A_       ,  control_guidance_start=0.1       ,  control_guidance_end=0.2							)[0]

				lowerCamelCase__								=		self.get_dummy_inputs(A_							)
				lowerCamelCase__								=		steps
				lowerCamelCase__								=		scale
				lowerCamelCase__								=		pipe(**A_       ,  control_guidance_start=[0.1, 0.3]       ,  control_guidance_end=[0.2, 0.7]							)[0]

				lowerCamelCase__								=		self.get_dummy_inputs(A_							)
				lowerCamelCase__								=		steps
				lowerCamelCase__								=		scale
				lowerCamelCase__								=		pipe(**A_       ,  control_guidance_start=0.4       ,  control_guidance_end=[0.5, 0.8]							)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a							)							) > 1E-3
				assert np.sum(np.abs(output_a - output_a							)							) > 1E-3
				assert np.sum(np.abs(output_a - output_a							)							) > 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3							)





			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()       ,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,  )
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self._test_inference_batch_single_identical(expected_max_diff=2E-3							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_dummy_components()
				lowerCamelCase__								=		self.pipeline_class(**A_							)
				pipe.to(A_							)
				pipe.set_progress_bar_config(disable=A_							)
				with tempfile.TemporaryDirectory() as tmpdir:
					try:
						# save_pretrained is not implemented for Multi-ControlNet
						pipe.save_pretrained(A_							)
					except NotImplementedError:
						pass






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

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''							)

				lowerCamelCase__								=		StableDiffusionControlNetImgaImgPipeline.from_pretrained(
				    '''runwayml/stable-diffusion-v1-5'''       ,  safety_checker=A_       ,  controlnet=A_							)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=A_							)

				lowerCamelCase__								=		torch.Generator(device='''cpu'''							).manual_seed(0							)
				lowerCamelCase__								=		'''evil space-punk bird'''
				lowerCamelCase__								=		load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''							).resize((5_1_2, 5_1_2)							)
				lowerCamelCase__								=		load_image(
				    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''							).resize((5_1_2, 5_1_2)							)

				lowerCamelCase__								=		pipe(
				    A_       ,  A_       ,  control_image=A_       ,  generator=A_       ,  output_type='''np'''       ,  num_inference_steps=5_0       ,  strength=0.6       ,  )

				lowerCamelCase__								=		output.images[0]

				assert image.shape == (5_1_2, 5_1_2, 3)

				lowerCamelCase__								=		load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''							)

				assert np.abs(expected_image - image							).max() < 9E-2







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

_A		       =						collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_51:
               raise ValueError(
                   'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(rows * cols * num_images				)
          __UpperCamelCase    							=numpy.frombuffer(SCREAMING_SNAKE_CASE__			,     dtype=numpy.uinta				)
          __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=labels_dense.shape[0]
     __UpperCamelCase    							=numpy.arange(SCREAMING_SNAKE_CASE__				) * num_classes
     __UpperCamelCase    							=numpy.zeros((num_labels, num_classes)				)
     __UpperCamelCase    							=1
     return labels_one_hot

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please use tf.data to implement this functionality.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					Dict=False			,     SCREAMING_SNAKE_CASE__			:					str=10				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_49:
               raise ValueError(
                   'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=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     UpperCAmelCase__       :



    """simple docstring"""






    @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					,   )  ->       Optional[int]:
         __UpperCamelCase							,      __UpperCamelCase    							=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    							=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    							=10000
              __UpperCamelCase    							=one_hot
         else:
              assert (
                  images.shape[0] == labels.shape[0]
              ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
              __UpperCamelCase    							=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    							=images.reshape(
                       images.shape[0]					,   images.shape[1] * images.shape[2]    )
              if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   __UpperCamelCase    							=images.astype(numpy.floataa    )
                   __UpperCamelCase    							=numpy.multiply(A_					,   1.0 / 255.0    )
         __UpperCamelCase    							=images
         __UpperCamelCase    							=labels
         __UpperCamelCase    							=0
         __UpperCamelCase    							=0






    @property
    def _a					(			self    )  ->       Tuple:
         return self._images






    @property
    def _a					(			self    )  ->       Union[str, Any]:
         return self._labels






    @property
    def _a					(			self    )  ->       Optional[Any]:
         return self._num_examples






    @property
    def _a					(			self    )  ->       List[str]:
         return self._epochs_completed






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

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please write your own downloading logic.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					str				):
     if not gfile.Exists(SCREAMING_SNAKE_CASE__				):
          gfile.MakeDirs(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=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:
               __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					int=False			,     SCREAMING_SNAKE_CASE__			:					str=False			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]=dtypes.floataa			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=True			,     SCREAMING_SNAKE_CASE__			:					str=50_00			,     SCREAMING_SNAKE_CASE__			:					List[Any]=None			,     SCREAMING_SNAKE_CASE__			:					str=DEFAULT_SOURCE_URL			,     ):
     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__				)

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

     if not source_url:  # empty string check
          __UpperCamelCase    							=DEFAULT_SOURCE_URL

     __UpperCamelCase    							='train-images-idx3-ubyte.gz'
     __UpperCamelCase    							='train-labels-idx1-ubyte.gz'
     __UpperCamelCase    							='t10k-images-idx3-ubyte.gz'
     __UpperCamelCase    							='t10k-labels-idx1-ubyte.gz'

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

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

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

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

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

     __UpperCamelCase    							=train_images[:validation_size]
     __UpperCamelCase    							=train_labels[:validation_size]
     __UpperCamelCase    							=train_images[validation_size:]
     __UpperCamelCase    							=train_labels[validation_size:]

     __UpperCamelCase    							={'dtype': dtype, 'reshape': reshape, 'seed': seed}

     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

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





