

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 ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							str:

				"""simple docstring"""

				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)
			@property
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ort.SessionOptions()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							False
				return options






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
				SCREAMING_SNAKE_CASE :   str					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							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
				SCREAMING_SNAKE_CASE :   Dict					  =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
				    "CompVis/stable-diffusion-v1-4"   ,   revision="onnx"   ,   safety_checker=a   ,   feature_extractor=a   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							"A red cat sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							np.random.RandomState(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   strength=0.75   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   generator=a   ,   output_type="np"   ,   )

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							output.images[0]

				assert image.shape == (512, 512, 3)
				assert np.abs(expected_image - image						).max() < 1e-2




import pytest


lowercase_													=      '''__dummy_dataset1__'''

lowercase_													=      '''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Any:
					return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Union[str, Any]:
					return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase						 =dataset_loading_script_name
					__lowerCAmelCase						 =tmp_path / """datasets""" / script_name
					script_dir.mkdir(parents=__lowerCamelCase						)
					__lowerCAmelCase						 =script_dir / f"""{script_name}.py"""
					with open(__lowerCamelCase					, """w"""						) as f:
										f.write(__lowerCamelCase						)
					return str(__lowerCamelCase						)




