





'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   StableDiffusionInstructPixaPixPipeline
     UpperCAmelCase__       :						List[str]      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
     UpperCAmelCase__       :						Optional[int]      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase__       :						Any      =   IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__       :						Any      =   IMAGE_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=8						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=32						,   )
       lowercase     		=				PNDMScheduler(skip_prk_steps=lowerCamelCase	)
       torch.manual_seed(0	)
       lowercase     		=				AutoencoderKL(
           block_out_channels=[32, 64]						,   in_channels=3						,   out_channels=3						,   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	):
       lowercase     		=				floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
       lowercase     		=				image.cpu().permute(0						,   2						,   3						,   1	)[0]
       lowercase     		=				Image.fromarray(np.uinta(lowerCamelCase	)	).convert("RGB"	)
       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",
           "image": image,
           "generator": generator,
           "num_inference_steps": 2,
           "guidance_scale": 6.0,
           "image_guidance_scale": 1,
           "output_type": "numpy",
       }
       return inputs

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

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				sd_pipe(**lowerCamelCase	).images
       lowercase     		=				image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       lowercase     		=				np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3

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

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				"french fries"
       lowercase     		=				sd_pipe(**lowerCamelCase						,   negative_prompt=lowerCamelCase	)
       lowercase     		=				output.images
       lowercase     		=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       lowercase     		=				np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3

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

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				[inputs["prompt"]] * 2

       lowercase     		=				np.array(inputs["image"]	).astype(np.floataa	) / 255.0
       lowercase     		=				torch.from_numpy(lowerCamelCase	).unsqueeze(0	).to(lowerCamelCase	)
       lowercase     		=				image / 2 + 0.5
       lowercase     		=				image.permute(0						,   3						,   1						,   2	)
       lowercase     		=				image.repeat(2						,   1						,   1						,   1	)

       lowercase     		=				sd_pipe(**lowerCamelCase	).images
       lowercase     		=				image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       lowercase     		=				np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3

     def 	UpperCamelCase(					self	):
       lowercase     		=				"cpu"  # ensure determinism for the device-dependent torch.Generator
       lowercase     		=				self.get_dummy_components()
       lowercase     		=				EulerAncestralDiscreteScheduler(
           beta_start=0.00085						,   beta_end=0.012						,   beta_schedule="scaled_linear"	)
       lowercase     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       lowercase     		=				sd_pipe.to(lowerCamelCase	)
       sd_pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				self.get_dummy_inputs(lowerCamelCase	)
       lowercase     		=				sd_pipe(**lowerCamelCase	).images
       lowercase     		=				image[0, -3:, -3:, -1]

       lowercase     		=				[round(lowerCamelCase						,   4	) for x in image_slice.flatten().tolist()]
       print(",".join([str(lowerCamelCase	) for x in slice]	)	)

       assert image.shape == (1, 32, 32, 3)
       lowercase     		=				np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3

     def 	UpperCamelCase(					self	):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






     def 	UpperCamelCase(					self	):
       lowercase     		=				self.get_dummy_components()
       lowercase     		=				StableDiffusionInstructPixaPixPipeline(**lowerCamelCase	)
       lowercase     		=				VaeImageProcessor(do_resize=lowerCamelCase						,   do_normalize=lowerCamelCase	)
       lowercase     		=				pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)

       lowercase     		=				pipe(**self.get_dummy_inputs_by_type(lowerCamelCase						,   input_image_type="pt"	)	)[0]

       lowercase     		=				components["vae"]
       lowercase     		=				self.get_dummy_inputs_by_type(lowerCamelCase						,   input_image_type="pt"	)

       for image_param in self.image_latents_params:
         if image_param in inputs.keys():
           lowercase     		=				vae.encode(inputs[image_param]	).latent_dist.mode()

       lowercase     		=				pipe(**lowerCamelCase	)[0]

       lowercase     		=				np.abs(out - out_latents_inputs	).max()
       self.assertLess(lowerCamelCase						,   1e-4						,   "passing latents as image input generate different result from passing image"	)







@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=0	):
       lowercase     		=				torch.manual_seed(lowerCamelCase	)
       lowercase     		=				load_image(
           "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
       lowercase     		=				{
           "prompt": "turn him into a cyborg",
           "image": image,
           "generator": generator,
           "num_inference_steps": 3,
           "guidance_scale": 7.5,
           "image_guidance_scale": 1.0,
           "output_type": "numpy",
       }
       return inputs

     def 	UpperCamelCase(					self	):
       lowercase     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase	)
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       lowercase     		=				self.get_inputs()
       lowercase     		=				pipe(**lowerCamelCase	).images
       lowercase     		=				image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowercase     		=				np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]	)

       assert np.abs(expected_slice - image_slice	).max() < 1e-3

     def 	UpperCamelCase(					self	):
       lowercase     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase	)
       lowercase     		=				LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       lowercase     		=				self.get_inputs()
       lowercase     		=				pipe(**lowerCamelCase	).images
       lowercase     		=				image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowercase     		=				np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]	)

       assert np.abs(expected_slice - image_slice	).max() < 1e-3

     def 	UpperCamelCase(					self	):
       lowercase     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase	)
       lowercase     		=				DDIMScheduler.from_config(pipe.scheduler.config	)
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       lowercase     		=				self.get_inputs()
       lowercase     		=				pipe(**lowerCamelCase	).images
       lowercase     		=				image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowercase     		=				np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]	)

       assert np.abs(expected_slice - image_slice	).max() < 1e-3

     def 	UpperCamelCase(					self	):
       lowercase     		=				0

       def callback_fn(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	) -> None:
         lowercase     		=				True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
           lowercase     		=				latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           lowercase     		=				latents[0, -3:, -3:, -1]
           lowercase     		=				np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]	)

           assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
         elif step == 2:
           lowercase     		=				latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 64)
           lowercase     		=				latents[0, -3:, -3:, -1]
           lowercase     		=				np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]	)

           assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

       lowercase     		=				False

       lowercase     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase						,   torch_dtype=torch.floataa	)
       lowercase     		=				pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       lowercase     		=				self.get_inputs()
       pipe(**lowerCamelCase						,   callback=lowerCamelCase						,   callback_steps=1	)
       assert callback_fn.has_been_called
       assert number_of_steps == 3

     def 	UpperCamelCase(					self	):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowercase     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           "timbrooks/instruct-pix2pix"						,   safety_checker=lowerCamelCase						,   torch_dtype=torch.floataa	)
       lowercase     		=				pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing(1	)
       pipe.enable_sequential_cpu_offload()

       lowercase     		=				self.get_inputs()
       lowercase     		=				pipe(**lowerCamelCase	)

       lowercase     		=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9






     def 	UpperCamelCase(					self	):
       lowercase     		=				self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       lowercase     		=				inputs["image"].resize((504, 504)	)

       lowercase     		=				"timbrooks/instruct-pix2pix"
       lowercase     		=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
           lowerCamelCase						,   safety_checker=lowerCamelCase						,   )
       pipe.to(lowerCamelCase	)
       pipe.set_progress_bar_config(disable=lowerCamelCase	)
       pipe.enable_attention_slicing()

       lowercase     		=				pipe(**lowerCamelCase	)
       lowercase     		=				output.images[0]

       lowercase     		=				image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       lowercase     		=				np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-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__       )


