

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	def wrapper(*lowerCamelCase__     ,			**lowerCamelCase__      ):
		__lowerCamelCase    :    str 	=			timeit.default_timer()
		__lowerCamelCase    :    Optional[Any] 	=			func(*lowerCamelCase__     ,			**lowerCamelCase__      )
		__lowerCamelCase    :    Any 	=			timeit.default_timer() - starttime
		return delta

	__lowerCamelCase    :    str 	=			func.__name__

	return wrapper






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=1_0_0     ,			lowerCamelCase__=None      )  ->					Tuple:
	__lowerCamelCase    :    int 	=			[]
	__lowerCamelCase    :    str 	=			seq_shapes or {}
	for i in range(lowerCamelCase__      ):
		__lowerCamelCase    :    Any 	=			{}
		for col_id, (k, v) in enumerate(features.items()      ):
			if isinstance(lowerCamelCase__     ,			_ArrayXD      ):
				__lowerCamelCase    :    Optional[Any] 	=			np.random.rand(*v.shape      ).astype(v.dtype      )
			elif isinstance(lowerCamelCase__     ,			datasets.Value      ):
				if v.dtype == "string":
					__lowerCamelCase    :    Optional[int] 	=			'The small grey turtle was surprisingly fast when challenged.'
				else:
					__lowerCamelCase    :    List[Any] 	=			np.random.randint(1_0     ,			size=1      ).astype(v.dtype      ).item()
			elif isinstance(lowerCamelCase__     ,			datasets.Sequence      ):
				while isinstance(lowerCamelCase__     ,			datasets.Sequence      ):
					__lowerCamelCase    :    Union[str, Any] 	=			v.feature
				__lowerCamelCase    :    Tuple 	=			seq_shapes[k]
				__lowerCamelCase    :    List[Any] 	=			np.random.rand(*lowerCamelCase__      ).astype(v.dtype      )
			__lowerCamelCase    :    Dict 	=			data

		dummy_data.append((i, example)      )

	return dummy_data






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=1_0_0     ,			lowerCamelCase__=None      )  ->					int:
	__lowerCamelCase    :    Optional[Any] 	=			generate_examples(lowerCamelCase__     ,			num_examples=lowerCamelCase__     ,			seq_shapes=lowerCamelCase__      )

	with ArrowWriter(features=lowerCamelCase__     ,			path=lowerCamelCase__      ) as writer:
		for key, record in dummy_data:
			__lowerCamelCase    :    Any 	=			features.encode_example(lowerCamelCase__      )
			writer.write(lowerCamelCase__      )

		__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."      )

	__lowerCamelCase    :    int 	=			datasets.Dataset.from_file(filename=lowerCamelCase__     ,			info=datasets.DatasetInfo(features=lowerCamelCase__      )      )

	return dataset







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 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


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

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




