





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ["vqvae"]






							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					AutoencoderKL,			_UpperCAmelCase     :					UNetaDConditionModel,			_UpperCAmelCase     :					Mel,			_UpperCAmelCase     :					Union[DDIMScheduler, DDPMScheduler],			)				->			Union[str, Any]:





														"""simple docstring"""
														super().__init__()
														self.register_modules(unet=_UpperCAmelCase,			scheduler=_UpperCAmelCase,			mel=_UpperCAmelCase,			vqvae=_UpperCAmelCase						)






							def  A_     ( self     :					List[str]						)				->			int:





														"""simple docstring"""
														return 5_0 if isinstance(self.scheduler,			_UpperCAmelCase						) else 1_0_0_0






							@torch.no_grad()
							def __call__( self     :					Optional[Any],			_UpperCAmelCase     :					int = 1,			_UpperCAmelCase     :					str = None,			_UpperCAmelCase     :					np.ndarray = None,			_UpperCAmelCase     :					int = 0,			_UpperCAmelCase     :					int = 0,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					torch.Generator = None,			_UpperCAmelCase     :					float = 0,			_UpperCAmelCase     :					float = 0,			_UpperCAmelCase     :					torch.Generator = None,			_UpperCAmelCase     :					float = 0,			_UpperCAmelCase     :					torch.Tensor = None,			_UpperCAmelCase     :					torch.Tensor = None,			_UpperCAmelCase     :					Optional[int]=True,			)				->			Union[
							    Union[AudioPipelineOutput, ImagePipelineOutput],
							    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
							]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						steps or self.get_default_steps()
														self.scheduler.set_timesteps(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						step_generator or generator
														# For backwards compatibility
														if type(self.unet.config.sample_size						) == int:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(self.unet.config.sample_size, self.unet.config.sample_size)
														if noise is None:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						randn_tensor(
																					    (
																					        batch_size,
																					        self.unet.config.in_channels,
																					        self.unet.config.sample_size[0],
																					        self.unet.config.sample_size[1],
																					    ),			generator=_UpperCAmelCase,			device=self.device,			)
														SCREAMING_SNAKE_CASE__       :      str     =						noise
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						None

														if audio_file is not None or raw_audio is not None:
																					self.mel.load_audio(_UpperCAmelCase,			_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.mel.audio_slice_to_image(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      str     =						np.frombuffer(input_image.tobytes(),			dtype="uint8"						).reshape(
																					    (input_image.height, input_image.width)						)
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						(input_image / 2_5_5) * 2 - 1
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.tensor(input_image[np.newaxis, :, :],			dtype=torch.float						).to(self.device						)

																					if self.vqvae is not None:
																												SCREAMING_SNAKE_CASE__       :      Any     =						self.vqvae.encode(torch.unsqueeze(_UpperCAmelCase,			0						)						).latent_dist.sample(
																												    generator=_UpperCAmelCase						)[0]
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self.vqvae.config.scaling_factor * input_images

																					if start_step > 0:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.scheduler.add_noise(_UpperCAmelCase,			_UpperCAmelCase,			self.scheduler.timesteps[start_step - 1]						)

																					SCREAMING_SNAKE_CASE__       :      List[str]     =						(
																					    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
																					)
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						int(mask_start_secs * pixels_per_second						)
																					SCREAMING_SNAKE_CASE__       :      str     =						int(mask_end_secs * pixels_per_second						)
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self.scheduler.add_noise(_UpperCAmelCase,			_UpperCAmelCase,			torch.tensor(self.scheduler.timesteps[start_step:]						)						)

														for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
																					if isinstance(self.unet,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.unet(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)["sample"]
																					else:
																												SCREAMING_SNAKE_CASE__       :      Dict     =						self.unet(_UpperCAmelCase,			_UpperCAmelCase						)["sample"]

																					if isinstance(self.scheduler,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      int     =						self.scheduler.step(
																												    model_output=_UpperCAmelCase,			timestep=_UpperCAmelCase,			sample=_UpperCAmelCase,			eta=_UpperCAmelCase,			generator=_UpperCAmelCase,			)["prev_sample"]
																					else:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.scheduler.step(
																												    model_output=_UpperCAmelCase,			timestep=_UpperCAmelCase,			sample=_UpperCAmelCase,			generator=_UpperCAmelCase,			)["prev_sample"]

																					if mask is not None:
																												if mask_start > 0:
																																			SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						mask[:, step, :, :mask_start]
																												if mask_end > 0:
																																			SCREAMING_SNAKE_CASE__       :      Any     =						mask[:, step, :, -mask_end:]

														if self.vqvae is not None:
																					# 0.18215 was scaling factor used in training to ensure unit variance
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						1 / self.vqvae.config.scaling_factor * images
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.vqvae.decode(_UpperCAmelCase						)["sample"]

														SCREAMING_SNAKE_CASE__       :      Dict     =						(images / 2 + 0.5).clamp(0,			1						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						images.cpu().permute(0,			2,			3,			1						).numpy()
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						(images * 2_5_5).round().astype("uint8"						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						list(
														    (Image.fromarray(_[:, :, 0]						) for _ in images)
														    if images.shape[3] == 1
														    else (Image.fromarray(_UpperCAmelCase,			mode="RGB"						).convert("L"						) for _ in images)						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						[self.mel.image_to_audio(_UpperCAmelCase						) for _ in images]
														if not return_dict:
																					return images, (self.mel.get_sample_rate(), audios)

														return BaseOutput(**AudioPipelineOutput(np.array(_UpperCAmelCase						)[:, np.newaxis, :]						),			**ImagePipelineOutput(_UpperCAmelCase						)						)






							@torch.no_grad()
							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[Image.Image],			_UpperCAmelCase     :					int = 5_0						)				->			np.ndarray:





														"""simple docstring"""
														assert isinstance(self.scheduler,			_UpperCAmelCase						)
														self.scheduler.set_timesteps(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						np.array(
														    [np.frombuffer(image.tobytes(),			dtype="uint8"						).reshape((1, image.height, image.width)						) for image in images]						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(sample / 2_5_5) * 2 - 1
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						torch.Tensor(_UpperCAmelCase						).to(self.device						)

														for t in self.progress_bar(torch.flip(self.scheduler.timesteps,			(0,)						)						):
																					SCREAMING_SNAKE_CASE__       :      Any     =						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.scheduler.alphas_cumprod[t]
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						(
																					    self.scheduler.alphas_cumprod[prev_timestep]
																					    if prev_timestep >= 0
																					    else self.scheduler.final_alpha_cumprod
																					)
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1 - alpha_prod_t
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self.unet(_UpperCAmelCase,			_UpperCAmelCase						)["sample"]
																					SCREAMING_SNAKE_CASE__       :      str     =						(1 - alpha_prod_t_prev) ** 0.5 * model_output
																					SCREAMING_SNAKE_CASE__       :      Dict     =						(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

														return sample






							@staticmethod
							def  A_     ( _UpperCAmelCase     :					torch.Tensor,			_UpperCAmelCase     :					torch.Tensor,			_UpperCAmelCase     :					float						)				->			torch.Tensor:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						acos(torch.dot(torch.flatten(_UpperCAmelCase						),			torch.flatten(_UpperCAmelCase						)						) / torch.norm(_UpperCAmelCase						) / torch.norm(_UpperCAmelCase						)						)
														return sin((1 - alpha) * theta						) * xa / sin(_UpperCAmelCase						) + sin(alpha * theta						) * xa / sin(_UpperCAmelCase						)













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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCamelCase :    Optional[Any]  							= '''https://storage.googleapis.com/cvdf-datasets/mnist/'''




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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 _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    List[str]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      int     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_51:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						bytestream.read(rows * cols * num_images       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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 _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						labels_dense.shape[0]
							SCREAMING_SNAKE_CASE__       :      List[str]     =						numpy.arange(SCREAMING_SNAKE_CASE__       ) * num_classes
							SCREAMING_SNAKE_CASE__       :      Dict     =						numpy.zeros((num_labels, num_classes)       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						1
							return labels_one_hot




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.data to implement this functionality."       )
def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=False			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=10       )	->    Optional[int]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_49:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						bytestream.read(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						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 						lowerCamelCase       :







							"""simple docstring"""




							@deprecated(
							    _UpperCAmelCase,			"Please use alternatives such as official/mnist/_DataSet.py"
							    " from tensorflow/models.",			)
							def __init__( self     :					int,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[str]=False,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Union[str, Any]=dtypes.floataa,			_UpperCAmelCase     :					Optional[Any]=True,			_UpperCAmelCase     :					Optional[int]=None,			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						random_seed.get_seed(_UpperCAmelCase						)
														# If op level seed is not set, use whatever graph level seed is returned
														numpy.random.seed(seeda if seed is None else seeda						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						dtypes.as_dtype(_UpperCAmelCase						).base_dtype
														if dtype not in (dtypes.uinta, dtypes.floataa):
																					raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype						)
														if fake_data:
																					SCREAMING_SNAKE_CASE__       :      str     =						1_0_0_0_0
																					SCREAMING_SNAKE_CASE__       :      str     =						one_hot
														else:
																					assert (
																					    images.shape[0] == labels.shape[0]
																					), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																					SCREAMING_SNAKE_CASE__       :      List[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
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						images.reshape(
																												    images.shape[0],			images.shape[1] * images.shape[2]						)
																					if dtype == dtypes.floataa:
																												# Convert from [0, 255] -> [0.0, 1.0].
																												SCREAMING_SNAKE_CASE__       :      Any     =						images.astype(numpy.floataa						)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.multiply(_UpperCAmelCase,			1.0 / 255.0						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						images
														SCREAMING_SNAKE_CASE__       :      str     =						labels
														SCREAMING_SNAKE_CASE__       :      Any     =						0
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0






							@property
							def  A_     ( self     :					List[Any]						)				->			int:





														"""simple docstring"""
														return self._images






							@property
							def  A_     ( self     :					List[str]						)				->			Optional[int]:





														"""simple docstring"""
														return self._labels






							@property
							def  A_     ( self     :					Optional[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														return self._num_examples






							@property
							def  A_     ( self     :					List[Any]						)				->			Tuple:





														"""simple docstring"""
														return self._epochs_completed






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any]=False,			_UpperCAmelCase     :					str=True						)				->			Any:





														"""simple docstring"""
														if fake_data:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[1] * 7_8_4
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[1] + [0] * 9 if self.one_hot else 0
																					return (
																					    [fake_image for _ in range(_UpperCAmelCase						)],
																					    [fake_label for _ in range(_UpperCAmelCase						)],
																					)
														SCREAMING_SNAKE_CASE__       :      str     =						self._index_in_epoch
														# Shuffle for the first epoch
														if self._epochs_completed == 0 and start == 0 and shuffle:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						numpy.arange(self._num_examples						)
																					numpy.random.shuffle(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.images[perma]
																					SCREAMING_SNAKE_CASE__       :      Union[str, 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
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self._num_examples - start
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self._images[start : self._num_examples]
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._labels[start : self._num_examples]
																					# Shuffle the data
																					if shuffle:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.arange(self._num_examples						)
																												numpy.random.shuffle(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						self.images[perm]
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.labels[perm]
																					# Start next epoch
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						batch_size - rest_num_examples
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self._index_in_epoch
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._images[start:end]
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self._index_in_epoch
																					return self._images[start:end], self._labels[start:end]




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please write your own downloading logic."       )
def _a (  SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]       )	->    str:

							'''simple docstring'''






							if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
														gfile.MakeDirs(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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:
																					SCREAMING_SNAKE_CASE__       :      Any     =						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 _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  str=False			,				SCREAMING_SNAKE_CASE__      :  Any=False			,				SCREAMING_SNAKE_CASE__      :  List[Any]=dtypes.floataa			,				SCREAMING_SNAKE_CASE__      :  str=True			,				SCREAMING_SNAKE_CASE__      :  Dict=50_00			,				SCREAMING_SNAKE_CASE__      :  str=None			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=DEFAULT_SOURCE_URL			,				)	->    Any:

							'''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__       )

														SCREAMING_SNAKE_CASE__       :      int     =						fake()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						fake()
														SCREAMING_SNAKE_CASE__       :      Any     =						fake()
														return _Datasets(train=SCREAMING_SNAKE_CASE__			,				validation=SCREAMING_SNAKE_CASE__			,				test=SCREAMING_SNAKE_CASE__       )

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

							SCREAMING_SNAKE_CASE__       :      Dict     =						"train-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"train-labels-idx1-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"t10k-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      str     =						"t10k-labels-idx1-ubyte.gz"

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Dict     =						_extract_images(SCREAMING_SNAKE_CASE__       )

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

							SCREAMING_SNAKE_CASE__       :      Dict     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_extract_images(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_labels_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Any     =						_extract_labels(SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__       )

							if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						(
														    "Validation size should be between 0 and "
														    f'''{len(SCREAMING_SNAKE_CASE__       )}. Received: {validation_size}.'''
														)
														raise ValueError(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      str     =						train_images[:validation_size]
							SCREAMING_SNAKE_CASE__       :      str     =						train_labels[:validation_size]
							SCREAMING_SNAKE_CASE__       :      Any     =						train_images[validation_size:]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						train_labels[validation_size:]

							SCREAMING_SNAKE_CASE__       :      Any     =						{"dtype": dtype, "reshape": reshape, "seed": seed}

							SCREAMING_SNAKE_CASE__       :      Tuple     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )

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






