


import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


A      = pytest.mark.integration





@pytest.mark.parametrize("path" ,						["paws", "csv"]			)
def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       inspect_dataset(__A ,						__A			)
       UpperCAmelCase__										=     path + ".py"
       assert script_name in os.listdir(__A			)
       assert "__pycache__" not in os.listdir(__A			)





@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path" ,						["accuracy"]			)
def __UpperCAmelCase       (		__A ,						__A			)						->					Any:



       '''simple docstring'''





       inspect_metric(__A ,						__A			)
       UpperCAmelCase__										=     path + ".py"
       assert script_name in os.listdir(__A			)
       assert "__pycache__" not in os.listdir(__A			)





@pytest.mark.parametrize(
    "path, config_name, expected_splits" ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ] ,						)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     get_dataset_config_info(__A ,						config_name=__A			)
       assert info.config_name == config_name
       assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception" ,						[
        ("paws", None, ValueError),
    ] ,						)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Dict:



       '''simple docstring'''





       with pytest.raises(__A			):
              get_dataset_config_info(__A ,						config_name=__A			)





@pytest.mark.parametrize(
    "path, expected" ,						[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ] ,						)
def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       UpperCAmelCase__										=     get_dataset_config_names(__A			)
       assert expected in config_names





@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config" ,						[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ] ,						)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     get_dataset_infos(__A			)
       assert list(infos.keys()			) == expected_configs
       UpperCAmelCase__										=     expected_configs[0]
       assert expected_config in infos
       UpperCAmelCase__										=     infos[expected_config]
       assert info.config_name == expected_config
       assert list(info.splits.keys()			) == expected_splits_in_first_config





@pytest.mark.parametrize(
    "path, expected_config, expected_splits" ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ] ,						)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     get_dataset_infos(__A			)
       assert expected_config in infos
       UpperCAmelCase__										=     infos[expected_config]
       assert info.config_name == expected_config
       assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception" ,						[
        ("paws", None, ValueError),
    ] ,						)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





       with pytest.raises(__A			):
              get_dataset_split_names(__A ,						config_name=__A			)









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     lowercase__  (					unittest.TestCase		):






  @property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







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






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







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

         UpperCAmelCase__										=     "A red cat sitting on a park bench"

         UpperCAmelCase__										=     np.random.RandomState(0 )
         UpperCAmelCase__										=     pipe(
             prompt=_lowercase		,						image=_lowercase		,						mask_image=_lowercase		,						strength=0.7_5		,						guidance_scale=7.5		,						num_inference_steps=15		,						generator=_lowercase		,						output_type="np"		,						)

         UpperCAmelCase__										=     output.images[0]

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





