
'''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	):

						@property
						def  A_				(			self				)					->      str:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







						def  A_				(			self				)					->      str:


								'''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=a       ,							feature_extractor=a       ,							provider=self.gpu_provider       ,							sess_options=self.gpu_options       ,							)
								pipe.set_progress_bar_config(disable=a				)

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

								_UpperCamelCase						    =	np.random.RandomState(0				)
								_UpperCamelCase						    =	pipe(
								    prompt=a       ,							image=a       ,							mask_image=a       ,							strength=0.75       ,							guidance_scale=7.5       ,							num_inference_steps=15       ,							generator=a       ,							output_type="""np"""       ,							)

								_UpperCamelCase						    =	output.images[0]

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





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

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


								'''simple docstring'''

								_UpperCamelCase						    =	huggingface_hub.__version__

								_UpperCamelCase						    =	"""not installed"""
								_UpperCamelCase						    =	"""NA"""
								if is_torch_available():
										import torch

										_UpperCamelCase						    =	torch.__version__
										_UpperCamelCase						    =	torch.cuda.is_available()

								_UpperCamelCase						    =	"""not installed"""
								if is_transformers_available():
										import transformers

										_UpperCamelCase						    =	transformers.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_accelerate_available():
										import accelerate

										_UpperCamelCase						    =	accelerate.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_xformers_available():
										import xformers

										_UpperCamelCase						    =	xformers.__version__

								_UpperCamelCase						    =	{
								    """`diffusers` version""": version,
								    """Platform""": platform.platform(),
								    """Python version""": platform.python_version(),
								    """PyTorch version (GPU?)""": F'{pt_version} ({pt_cuda_available})',
								    """Huggingface_hub version""": hub_version,
								    """Transformers version""": transformers_version,
								    """Accelerate version""": accelerate_version,
								    """xFormers version""": xformers_version,
								    """Using GPU in script?""": """<fill in>""",
								    """Using distributed or parallel set-up in script?""": """<fill in>""",
								}

								print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""				)
								print(self.format_dict(a				)				)

								return info







						@staticmethod
						def  A_				(			a				)					->      Dict:


								'''simple docstring'''

								return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]				) + "\n"



