"""simple docstring"""





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
       import onnxruntime as ort


class      UpperCAmelCase_	(    _a ,							unittest.TestCase):
      # FIXME: add fast tests
      pass





@nightly
@require_onnxruntime
@require_torch_gpu
class      UpperCAmelCase_	(    unittest.TestCase):


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


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Union[str, Any]       =	ort.SessionOptions()
             lowercase__			:		List[Any]       =	False
             return options


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		List[str]       =	load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
             lowercase__			:		Dict       =	load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
             lowercase__			:		List[str]       =	OnnxStableDiffusionInpaintPipeline.from_pretrained(
                 'runwayml/stable-diffusion-inpainting'		,			revision='onnx'		,			safety_checker=_snake_case		,			feature_extractor=_snake_case		,			provider=self.gpu_provider		,			sess_options=self.gpu_options		,			)
             pipe.set_progress_bar_config(disable=_snake_case							)

             lowercase__			:		Dict       =	'A red cat sitting on a park bench'

             lowercase__			:		Any       =	np.random.RandomState(0							)
             lowercase__			:		List[str]       =	pipe(
                 prompt=_snake_case		,			image=_snake_case		,			mask_image=_snake_case		,			guidance_scale=7.5		,			num_inference_steps=1_0		,			generator=_snake_case		,			output_type='np'		,			)
             lowercase__			:		List[str]       =	output.images
             lowercase__			:		List[str]       =	images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             lowercase__			:		Optional[Any]       =	np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
             lowercase__			:		Dict       =	load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
             lowercase__			:		List[str]       =	LMSDiscreteScheduler.from_pretrained(
                 'runwayml/stable-diffusion-inpainting'		,			subfolder='scheduler'		,			revision='onnx'							)
             lowercase__			:		int       =	OnnxStableDiffusionInpaintPipeline.from_pretrained(
                 'runwayml/stable-diffusion-inpainting'		,			revision='onnx'		,			scheduler=_snake_case		,			safety_checker=_snake_case		,			feature_extractor=_snake_case		,			provider=self.gpu_provider		,			sess_options=self.gpu_options		,			)
             pipe.set_progress_bar_config(disable=_snake_case							)

             lowercase__			:		Tuple       =	'A red cat sitting on a park bench'

             lowercase__			:		Any       =	np.random.RandomState(0							)
             lowercase__			:		Optional[int]       =	pipe(
                 prompt=_snake_case		,			image=_snake_case		,			mask_image=_snake_case		,			guidance_scale=7.5		,			num_inference_steps=2_0		,			generator=_snake_case		,			output_type='np'		,			)
             lowercase__			:		Dict       =	output.images
             lowercase__			:		List[Any]       =	images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

             assert images.shape == (1, 5_1_2, 5_1_2, 3)
             lowercase__			:		int       =	np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3



from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)