

'''simple docstring'''



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 lowerCamelCase					(    unittest.TestCase		):



			'''simple docstring'''






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





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


			@property
			def lowercase__		( self			:		str					)     ->						Optional[int]:





									'''simple docstring'''
									A__	:  Optional[Any]           =ort.SessionOptions()
									A__	:  Optional[Any]           =False
									return options



			def lowercase__		( self			:		str					)     ->						List[str]:





									'''simple docstring'''
									A__	:  List[Any]           =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo.png"""					)
									A__	:  Any           =load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""					)
									A__	:  Union[str, Any]           =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
									A__	:  Tuple           =OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""						,     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__	:  Optional[Any]           ='A red cat sitting on a park bench'

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

									A__	:  int           =output.images[0]

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






"""simple docstring"""







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_							=    {
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





