


# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     str										=    {
         """en""": """Machine learning is great, isn't it?""",
         """ru""": """Машинное обучение - это здорово, не так ли?""",
         """de""": """Maschinelles Lernen ist großartig, oder?""",
     }

     # BLUE scores as follows:
     # "pair": [fairseq, transformers]
     a_    :     Optional[Any]										=    {
         """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
         """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
         """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
         """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
     }
     a_    :     List[str]										=    F'''{src_lang}-{tgt_lang}'''

     a_    :     str										=    F'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
     os.makedirs(__UpperCamelCase  ,  exist_ok=__UpperCamelCase )
     a_    :     Dict										=    os.path.join(__UpperCamelCase  ,  """README.md""" )
     print(F'''Generating {path}''' )
     with open(__UpperCamelCase  ,  """w"""  ,  encoding="""utf-8""" ) as f:
          f.write(__UpperCamelCase )


# make sure we are under the root of the project
__lowerCamelCase        =     Path(__file__).resolve().parent.parent.parent
__lowerCamelCase        =     repo_dir / '''model_cards'''

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      __lowerCamelCase  ,       __lowerCamelCase  ,       __lowerCamelCase        =     model_name.split('''-''')
      __lowerCamelCase        =     model_cards_dir / '''facebook''' / model_name
      write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)







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   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     int							    =     """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Union[str, Any]=0    ):
            a_    :     Any										=    np.random.RandomState(lowerCamelCase_    )
            a_    :     Optional[Any]										=    {
                """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 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Optional[int]										=    self.get_dummy_inputs()
            a_    :     Optional[Any]										=    pipe(**lowerCamelCase_    ).images
            a_    :     Tuple										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     str										=    np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]    )

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

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     str										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Any										=    PNDMScheduler.from_config(pipe.scheduler.config      ,							skip_prk_steps=lowerCamelCase_    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Dict										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    ).images
            a_    :     Optional[Any]										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Tuple										=    np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]    )

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

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[Any]										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Optional[int]										=    LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Dict										=    self.get_dummy_inputs()
            a_    :     Union[str, Any]										=    pipe(**lowerCamelCase_    ).images
            a_    :     int										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Optional[int]										=    np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

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

       def 						UpperCAmelCase(   self      :     Any    ):
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Optional[Any]										=    EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Optional[Any]										=    self.get_dummy_inputs()
            a_    :     Optional[Any]										=    pipe(**lowerCamelCase_    ).images
            a_    :     Optional[Any]										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Optional[int]										=    np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]    )

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

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     str										=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Dict										=    self.get_dummy_inputs()
            a_    :     Union[str, Any]										=    pipe(**lowerCamelCase_    ).images
            a_    :     Any										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     Tuple										=    np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]    )

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

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            a_    :     Tuple										=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    ).images
            a_    :     str										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            a_    :     List[Any]										=    np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]    )

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

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     int										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     List[str]										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    3 * [inputs["""prompt"""]]

            # forward
            a_    :     Optional[int]										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     List[Any]										=    3 * [inputs.pop("""prompt"""    )]

            a_    :     List[Any]										=    pipe.tokenizer(
                lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=pipe.tokenizer.model_max_length      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
            a_    :     str										=    text_inputs["""input_ids"""]

            a_    :     Dict										=    pipe.text_encoder(input_ids=text_inputs.astype(np.intaa    )    )[0]

            a_    :     List[Any]										=    prompt_embeds

            # forward
            a_    :     Tuple										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

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

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Any										=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint      ,							provider="""CPUExecutionProvider"""    )
            pipe.set_progress_bar_config(disable=lowerCamelCase_    )

            a_    :     int										=    self.get_dummy_inputs()
            a_    :     str										=    3 * ["""this is a negative prompt"""]
            a_    :     int										=    negative_prompt
            a_    :     Dict										=    3 * [inputs["""prompt"""]]

            # forward
            a_    :     Tuple										=    pipe(**lowerCamelCase_    )
            a_    :     Union[str, Any]										=    output.images[0, -3:, -3:, -1]

            a_    :     Tuple										=    self.get_dummy_inputs()
            a_    :     Optional[int]										=    3 * [inputs.pop("""prompt"""    )]

            a_    :     str										=    []
            for p in [prompt, negative_prompt]:
                 a_    :     Optional[int]										=    pipe.tokenizer(
                     lowerCamelCase_      ,							padding="""max_length"""      ,							max_length=pipe.tokenizer.model_max_length      ,							truncation=lowerCamelCase_      ,							return_tensors="""np"""      ,							)
                 a_    :     int										=    text_inputs["""input_ids"""]

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

            a_			,			a_    :     Union[str, Any]										=    embeds

            # forward
            a_    :     Optional[Any]										=    pipe(**lowerCamelCase_    )
            a_    :     int										=    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   a__	(						unittest.TestCase    ):

       @property
       def 						UpperCAmelCase(   self      :     Tuple    ):
            return (
                "CUDAExecutionProvider",
                {
                    "gpu_mem_limit": "15000000000",  # 15GB
                    "arena_extend_strategy": "kSameAsRequested",
                },
            )

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     List[str]										=    ort.SessionOptions()
            a_    :     List[str]										=    False
            return options

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            # using the PNDM scheduler by default
            a_    :     int										=    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_    )

            a_    :     Tuple										=    """A painting of a squirrel eating a burger"""
            np.random.seed(0    )
            a_    :     int										=    sd_pipe([prompt]      ,							guidance_scale=6.0      ,							num_inference_steps=1_0      ,							output_type="""np"""    )
            a_    :     Optional[int]										=    output.images

            a_    :     Tuple										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     Optional[int]										=    np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]    )

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

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     str										=    DDIMScheduler.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							subfolder="""scheduler"""      ,							revision="""onnx"""    )
            a_    :     Any										=    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_    )

            a_    :     Union[str, Any]										=    """open neural network exchange"""
            a_    :     str										=    np.random.RandomState(0    )
            a_    :     str										=    sd_pipe([prompt]      ,							guidance_scale=7.5      ,							num_inference_steps=1_0      ,							generator=lowerCamelCase_      ,							output_type="""np"""    )
            a_    :     str										=    output.images
            a_    :     Optional[Any]										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     List[str]										=    np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]    )

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

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
            a_    :     Optional[int]										=    LMSDiscreteScheduler.from_pretrained(
                """runwayml/stable-diffusion-v1-5"""      ,							subfolder="""scheduler"""      ,							revision="""onnx"""    )
            a_    :     List[str]										=    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_    )

            a_    :     Optional[int]										=    """open neural network exchange"""
            a_    :     Tuple										=    np.random.RandomState(0    )
            a_    :     Tuple										=    sd_pipe([prompt]      ,							guidance_scale=7.5      ,							num_inference_steps=1_0      ,							generator=lowerCamelCase_      ,							output_type="""np"""    )
            a_    :     Tuple										=    output.images
            a_    :     str										=    image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            a_    :     str										=    np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]    )

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

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     List[str]										=    0

            def test_callback_fn(lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     np.ndarray    ) -> None:
                 a_    :     List[Any]										=    True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 0:
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      a_    :     Union[str, Any]										=    latents[0, -3:, -3:, -1]
                      a_    :     Optional[Any]										=    np.array(
                          [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]    )

                      assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
                 elif step == 5:
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      a_    :     List[str]										=    latents[0, -3:, -3:, -1]
                      a_    :     int										=    np.array(
                          [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]    )

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

            a_    :     Optional[int]										=    False

            a_    :     List[Any]										=    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_    )

            a_    :     List[str]										=    """Andromeda galaxy in a bottle"""

            a_    :     Union[str, Any]										=    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 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Tuple										=    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

            a_    :     Union[str, Any]										=    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_    )
                 a_    :     Tuple										=    OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase_    )

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



