






import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


__UpperCamelCase      :      Union[str, Any]     						=						logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase      :      List[Any]     						=						"""cuda""" if torch.cuda.is_available() else """cpu"""





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase=100				,  lowerCamelCase=" "					):




 '''simple docstring'''







 __lowercase             =  text.split(lowerCamelCase					)
 return [character.join(text[i : i + n]					).strip() for i in range(0				,  len(lowerCamelCase					)				,  lowerCamelCase					)]





def 				snake_case  (     lowerCamelCase					):




 '''simple docstring'''







 __lowercase       ,						__lowercase             =  [], []
 for title, text in zip(documents["""title"""]				,  documents["""text"""]					):
  if text is not None:
   for passage in split_text(lowerCamelCase					):
    titles.append(title if title is not None else """"""					)
    texts.append(lowerCamelCase					)
 return {"title": titles, "text": texts}





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




 '''simple docstring'''







 __lowercase             =  ctx_tokenizer(
     documents["""title"""]				,  documents["""text"""]				,  truncation=lowerCamelCase				,  padding="""longest"""				,  return_tensors="""pt"""					)["""input_ids"""]
 __lowercase             =  ctx_encoder(input_ids.to(device=lowerCamelCase					)				,  return_dict=lowerCamelCase					).pooler_output
 return {"embeddings": embeddings.detach().cpu().numpy()}





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  ):




 '''simple docstring'''







 logger.info("""Step 1 - Create the dataset"""					)
 ######################################

 # The dataset needed for RAG must have three columns:
 # - title (string): title of the document
 # - text (string): text of a passage of the document
 # - embeddings (array of dimension d): DPR representation of the passage
 # Let's say you have documents in tab-separated csv files with columns "title" and "text"
 assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

 # You can load a Dataset object this way
 __lowercase             =  load_dataset(
     """csv"""				,  data_files=[rag_example_args.csv_path]				,  split="""train"""				,  delimiter="""\t"""				,  column_names=["""title""", """text"""]					)

 # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

 # Then split the documents into passages of 100 words
 __lowercase             =  dataset.map(lowerCamelCase				,  batched=lowerCamelCase				,  num_proc=processing_args.num_proc					)

 # And compute the embeddings
 __lowercase             =  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=lowerCamelCase					)
 __lowercase             =  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
 __lowercase             =  Features(
     {"""text""": Value("""string"""					), """title""": Value("""string"""					), """embeddings""": Sequence(Value("""float32"""					)					)}					)  # optional, save as float32 instead of float64 to save space
 __lowercase             =  dataset.map(
     partial(lowerCamelCase				,  ctx_encoder=lowerCamelCase				,  ctx_tokenizer=lowerCamelCase					)				,  batched=lowerCamelCase				,  batch_size=processing_args.batch_size				,  features=lowerCamelCase				,  )

 # And finally save your dataset
 __lowercase             =  os.path.join(rag_example_args.output_dir				,  """my_knowledge_dataset"""					)
 dataset.save_to_disk(lowerCamelCase					)
 # from datasets import load_from_disk
 # dataset = load_from_disk(passages_path)  # to reload the dataset

 ######################################
 logger.info("""Step 2 - Index the dataset"""					)
 ######################################

 # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
 __lowercase             =  faiss.IndexHNSWFlat(index_hnsw_args.d				,  index_hnsw_args.m				,  faiss.METRIC_INNER_PRODUCT					)
 dataset.add_faiss_index("""embeddings"""				,  custom_index=lowerCamelCase					)

 # And save the index
 __lowercase             =  os.path.join(rag_example_args.output_dir				,  """my_knowledge_dataset_hnsw_index.faiss"""					)
 dataset.get_index("""embeddings"""					).save(lowerCamelCase					)
 # dataset.load_faiss_index("embeddings", index_path)  # to reload the index


@dataclass
class        __UpperCamelCase    :
 __snake_case	:str   =       field(
     default=str(Path(_lowerCAmelCase   ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'   )   ,			metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}   ,			)
 __snake_case	:Optional[str]   =       field(
     default=_lowerCAmelCase   ,			metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}   ,			)
 __snake_case	:str   =       field(
     default='facebook/rag-sequence-nq'   ,			metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}   ,			)
 __snake_case	:str   =       field(
     default='facebook/dpr-ctx_encoder-multiset-base'   ,			metadata={
         'help': (
             'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
             ' \'facebook/dpr-ctx_encoder-multiset-base\''
         )
     }   ,			)
 __snake_case	:Optional[str]   =       field(
     default=str(Path(_lowerCAmelCase   ).parent / 'test_run' / 'dummy-kb'   )   ,			metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}   ,			)


@dataclass
class        __UpperCamelCase    :
 __snake_case	:Optional[int]   =       field(
     default=_lowerCAmelCase   ,			metadata={
         'help': 'The number of processes to use to split the documents into passages. Default is single process.'
     }   ,			)
 __snake_case	:int   =       field(
     default=1_6   ,			metadata={
         'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
     }   ,			)


@dataclass
class        __UpperCamelCase    :
 __snake_case	:int   =       field(
     default=7_6_8   ,			metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}   ,			)
 __snake_case	:int   =       field(
     default=1_2_8   ,			metadata={
         'help': (
             'The number of bi-directional links created for every new element during the HNSW index construction.'
         )
     }   ,			)


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      __UpperCamelCase      :      str     						=						HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      __UpperCamelCase      :      Optional[Any]     						=						parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            __UpperCamelCase      :      str     						=						rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)









import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort


class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Union[str, Any]   =       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							str=0			)		-> str:
		"""simple docstring"""







		__lowercase             =  np.random.RandomState(_lowerCAmelCase			)
		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """guidance_scale""": 7.5,
		    """output_type""": """numpy""",
		}
		return inputs







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Union[str, Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  PNDMScheduler.from_config(pipe.scheduler.config       ,				skip_prk_steps=_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Tuple			)		-> int:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							Union[str, Any]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  pipe(**_lowerCAmelCase			).images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2







	def  _a   (  self      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

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

		__lowercase             =  pipe.tokenizer(
		    _lowerCAmelCase       ,				padding="""max_length"""       ,				max_length=pipe.tokenizer.model_max_length       ,				truncation=_lowerCAmelCase       ,				return_tensors="""np"""       ,				)
		__lowercase             =  text_inputs["""input_ids"""]

		__lowercase             =  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]

		__lowercase             =  prompt_embeds

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

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







	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * ["""this is a negative prompt"""]
		__lowercase             =  negative_prompt
		__lowercase             =  3 * [inputs["""prompt"""]]

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

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

		__lowercase             =  []
		for p in [prompt, negative_prompt]:
			__lowercase             =  pipe.tokenizer(
			    _lowerCAmelCase       ,				padding="""max_length"""       ,				max_length=pipe.tokenizer.model_max_length       ,				truncation=_lowerCAmelCase       ,				return_tensors="""np"""       ,				)
			__lowercase             =  text_inputs["""input_ids"""]

			embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]			)

		__lowercase       ,						__lowercase             =  embeds

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

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


@nightly
@require_onnxruntime
@require_torch_gpu
class        __UpperCamelCase    (     unittest.TestCase   ):







	@property
	def  _a   (  self      :							Dict			)		-> str:
		"""simple docstring"""







		return (
		    "CUDAExecutionProvider",
		    {
		        "gpu_mem_limit": "15000000000",  # 15GB
		        "arena_extend_strategy": "kSameAsRequested",
		    },
		)







	@property
	def  _a   (  self      :							Optional[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  ort.SessionOptions()
		__lowercase             =  False
		return options







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """CompVis/stable-diffusion-v1-4"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """A painting of a squirrel eating a burger"""
		np.random.seed(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=6.0       ,				num_inference_steps=10       ,				output_type="""np"""			)
		__lowercase             =  output.images

		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]			)

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







	def  _a   (  self      :							Tuple			)		-> Any:
		"""simple docstring"""







		__lowercase             =  DDIMScheduler.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				subfolder="""scheduler"""       ,				revision="""onnx"""			)
		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				scheduler=_lowerCAmelCase       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """open neural network exchange"""
		__lowercase             =  np.random.RandomState(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)
		__lowercase             =  output.images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]			)

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







	def  _a   (  self      :							Dict			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  LMSDiscreteScheduler.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				subfolder="""scheduler"""       ,				revision="""onnx"""			)
		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				scheduler=_lowerCAmelCase       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """open neural network exchange"""
		__lowercase             =  np.random.RandomState(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)
		__lowercase             =  output.images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]			)

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







	def  _a   (  self      :							str			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  0

		def test_callback_fn(_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							np.ndarray			) -> None:
			__lowercase             =  True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 0:
				assert latents.shape == (1, 4, 64, 64)
				__lowercase             =  latents[0, -3:, -3:, -1]
				__lowercase             =  np.array(
				    [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]			)

				assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3
			elif step == 5:
				assert latents.shape == (1, 4, 64, 64)
				__lowercase             =  latents[0, -3:, -3:, -1]
				__lowercase             =  np.array(
				    [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]			)

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

		__lowercase             =  False

		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """Andromeda galaxy in a bottle"""

		__lowercase             =  np.random.RandomState(0			)
		pipe(
		    prompt=_lowerCAmelCase       ,				num_inference_steps=5       ,				guidance_scale=7.5       ,				generator=_lowerCAmelCase       ,				callback=_lowerCAmelCase       ,				callback_steps=1       ,				)
		assert test_callback_fn.has_been_called
		assert number_of_steps == 6







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		assert isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			)
		assert pipe.safety_checker is None

		__lowercase             =  pipe("""example prompt"""       ,				num_inference_steps=2			).images[0]
		assert image is not None

		# check that there's no error when saving a pipeline with one of the models being None
		with tempfile.TemporaryDirectory() as tmpdirname:
			pipe.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase			)

		# sanity check that the pipeline still works
		assert pipe.safety_checker is None
		__lowercase             =  pipe("""example prompt"""       ,				num_inference_steps=2			).images[0]
		assert image is not None

