"""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

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

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






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							numpy.dtype(numpy.uintaa				).newbyteorder('>'				)
							return numpy.frombuffer(bytestream.read(4				)       ,    dtype=UpperCamelCase__				)[0]






@deprecated(UpperCamelCase__       ,    'Please use tf.data to implement this functionality.'				)
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							print('Extracting'       ,    f.name				)
							with gzip.GzipFile(fileobj=UpperCamelCase__				) as bytestream:
														A__	     =							_readaa(UpperCamelCase__				)
														if magic != 2_051:
																					raise ValueError(
																					    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
														A__	     =							_readaa(UpperCamelCase__				)
														A__	     =							_readaa(UpperCamelCase__				)
														A__	     =							_readaa(UpperCamelCase__				)
														A__	     =							bytestream.read(rows * cols * num_images				)
														A__	     =							numpy.frombuffer(UpperCamelCase__       ,    dtype=numpy.uinta				)
														A__	     =							data.reshape(UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    1				)
														return data






@deprecated(UpperCamelCase__       ,    'Please use tf.one_hot on tensors.'				)
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							labels_dense.shape[0]
							A__	     =							numpy.arange(UpperCamelCase__				) * num_classes
							A__	     =							numpy.zeros((num_labels, num_classes)				)
							A__	     =							1
							return labels_one_hot






@deprecated(UpperCamelCase__       ,    'Please use tf.data to implement this functionality.'				)
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=False       ,    UpperCamelCase__=10				):






							"""simple docstring"""





							print('Extracting'       ,    f.name				)
							with gzip.GzipFile(fileobj=UpperCamelCase__				) as bytestream:
														A__	     =							_readaa(UpperCamelCase__				)
														if magic != 2_049:
																					raise ValueError(
																					    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
														A__	     =							_readaa(UpperCamelCase__				)
														A__	     =							bytestream.read(UpperCamelCase__				)
														A__	     =							numpy.frombuffer(UpperCamelCase__       ,    dtype=numpy.uinta				)
														if one_hot:
																					return _dense_to_one_hot(UpperCamelCase__       ,    UpperCamelCase__				)
														return labels
class     UpperCamelCase__:





						@deprecated(
						    __UpperCAmelCase		,'Please use alternatives such as official/mnist/_DataSet.py'
						    ' from tensorflow/models.'		,)
						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=False		,__UpperCAmelCase=False		,__UpperCAmelCase=dtypes.floataa		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,)					->				Union[str, Any]:
													A__     ,  A__	     =							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     )
													A__	     =							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:
																				A__	     =							1_00_00
																				A__	     =							one_hot
													else:
																				assert (
																				    images.shape[0] == labels.shape[0]
																				), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																				A__	     =							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
																											A__	     =							images.reshape(
																											    images.shape[0]		,images.shape[1] * images.shape[2]     )
																				if dtype == dtypes.floataa:
																											# Convert from [0, 255] -> [0.0, 1.0].
																											A__	     =							images.astype(numpy.floataa     )
																											A__	     =							numpy.multiply(__UpperCAmelCase		,1.0 / 2_5_5.0     )
													A__	     =							images
													A__	     =							labels
													A__	     =							0
													A__	     =							0





						@property
						def 				snake_case__	(       self     )					->				Dict:
													return self._images





						@property
						def 				snake_case__	(       self     )					->				Optional[Any]:
													return self._labels





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





						@property
						def 				snake_case__	(       self     )					->				str:
													return self._epochs_completed





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=False		,__UpperCAmelCase=True     )					->				Tuple:
													if fake_data:
																				A__	     =							[1] * 7_84
																				A__	     =							[1] + [0] * 9 if self.one_hot else 0
																				return (
																				    [fake_image for _ in range(__UpperCAmelCase     )],
																				    [fake_label for _ in range(__UpperCAmelCase     )],
																				)
													A__	     =							self._index_in_epoch
													# Shuffle for the first epoch
													if self._epochs_completed == 0 and start == 0 and shuffle:
																				A__	     =							numpy.arange(self._num_examples     )
																				numpy.random.shuffle(__UpperCAmelCase     )
																				A__	     =							self.images[perma]
																				A__	     =							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
																				A__	     =							self._num_examples - start
																				A__	     =							self._images[start : self._num_examples]
																				A__	     =							self._labels[start : self._num_examples]
																				# Shuffle the data
																				if shuffle:
																											A__	     =							numpy.arange(self._num_examples     )
																											numpy.random.shuffle(__UpperCAmelCase     )
																											A__	     =							self.images[perm]
																											A__	     =							self.labels[perm]
																				# Start next epoch
																				A__	     =							0
																				A__	     =							batch_size - rest_num_examples
																				A__	     =							self._index_in_epoch
																				A__	     =							self._images[start:end]
																				A__	     =							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
																				A__	     =							self._index_in_epoch
																				return self._images[start:end], self._labels[start:end]






@deprecated(UpperCamelCase__       ,    'Please write your own downloading logic.'				)
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							if not gfile.Exists(UpperCamelCase__				):
														gfile.MakeDirs(UpperCamelCase__				)
							A__	     =							os.path.join(UpperCamelCase__       ,    UpperCamelCase__				)
							if not gfile.Exists(UpperCamelCase__				):
														urllib.request.urlretrieve(UpperCamelCase__       ,    UpperCamelCase__				)  # noqa: S310
														with gfile.GFile(UpperCamelCase__				) as f:
																					A__	     =							f.size()
														print('Successfully downloaded'       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    'bytes.'				)
							return filepath






@deprecated(
    UpperCamelCase__       ,    'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'				)
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=False       ,    UpperCamelCase__=False       ,    UpperCamelCase__=dtypes.floataa       ,    UpperCamelCase__=True       ,    UpperCamelCase__=5_000       ,    UpperCamelCase__=None       ,    UpperCamelCase__=DEFAULT_SOURCE_URL       ,    ):






							"""simple docstring"""





							if fake_data:

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

														A__	     =							fake()
														A__	     =							fake()
														A__	     =							fake()
														return _Datasets(train=UpperCamelCase__       ,    validation=UpperCamelCase__       ,    test=UpperCamelCase__				)

							if not source_url:  # empty string check
														A__	     =							DEFAULT_SOURCE_URL

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

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

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

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

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

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

							A__	     =							train_images[:validation_size]
							A__	     =							train_labels[:validation_size]
							A__	     =							train_images[validation_size:]
							A__	     =							train_labels[validation_size:]

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

							A__	     =							_DataSet(UpperCamelCase__       ,    UpperCamelCase__       ,    **UpperCamelCase__				)
							A__	     =							_DataSet(UpperCamelCase__       ,    UpperCamelCase__       ,    **UpperCamelCase__				)
							A__	     =							_DataSet(UpperCamelCase__       ,    UpperCamelCase__       ,    **UpperCamelCase__				)

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






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
				import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'     )

													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





