





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
						from PIL import Image
else:







						class    __snake_case	:





													'''simple docstring'''







													@staticmethod
													def 							UpperCAmelCase__			(  *A			:   List[Any]							,    **A			:   Union[str, Any]		):
															pass







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   str:
		__snake_case:						str			  =						hashlib.mda(image.tobytes())
		return m.hexdigest()[:10]







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   Dict:
		__snake_case:						Tuple			  =						np.array(SCREAMING_SNAKE_CASE__)
		__snake_case:						Union[str, Any]			  =						npimg.shape
		return {"hash": hashimage(SCREAMING_SNAKE_CASE__), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class    __snake_case	(							unittest.TestCase      ):





							'''simple docstring'''







							lowerCAmelCase__						=					dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
							lowerCAmelCase__						=					dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )




							def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Union[str, Any]							,    A			:   str							,    A			:   Optional[int]		):
									__snake_case:						List[str]			  =						MaskGenerationPipeline(model=A							,    image_processor=A		)
									return image_segmenter, [
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]




							def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Optional[Any]							,    A			:   str		):
									pass




							@require_tf
							@unittest.skip("""Image segmentation not implemented in TF"""		)
							def 							UpperCAmelCase__			(  self			:   str		):
									pass




							@slow
							@require_torch
							def 							UpperCAmelCase__			(  self			:   List[Any]		):
									__snake_case:						Union[str, Any]			  =						pipeline("""mask-generation"""							,    model="""facebook/sam-vit-huge"""		)

									__snake_case:						Any			  =						image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""							,    points_per_batch=256		)

									# Shortening by hashing
									__snake_case:						int			  =						[]
									for i, o in enumerate(outputs["""masks"""]		):
											new_outupt += [{"mask": mask_to_test_readable(A		), "scores": outputs["scores"][i]}]

									# fmt: off
									self.assertEqual(
									    nested_simplify(A							,    decimals=4		)							,    [
									        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
									        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
									        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
									        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
									        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
									        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
									        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
									        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
									        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
									        {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
									        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
									        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
									        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
									        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
									        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
									        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
									        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
									        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
									        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
									        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
									        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
									        {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
									        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
									        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
									        {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
									        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
									        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
									        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
									        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
									        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
									    ]							,    )
									# fmt: on






							@require_torch
							@slow
							def 							UpperCAmelCase__			(  self			:   Dict		):
									__snake_case:						Union[str, Any]			  =						"""facebook/sam-vit-huge"""
									__snake_case:						str			  =						pipeline("""mask-generation"""							,    model=A		)

									__snake_case:						int			  =						image_segmenter(
									    """http://images.cocodataset.org/val2017/000000039769.jpg"""							,    pred_iou_thresh=1							,    points_per_batch=256		)

									# Shortening by hashing
									__snake_case:						str			  =						[]
									for i, o in enumerate(outputs["""masks"""]		):
											new_outupt += [{"mask": mask_to_test_readable(A		), "scores": outputs["scores"][i]}]

									self.assertEqual(
									    nested_simplify(A							,    decimals=4		)							,    [
									        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
									        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
									        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
									        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
									        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
									    ]							,    )













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    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       pass







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





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )




       @property
       def 							UpperCAmelCase__			(  self			:   Dict		):
         __snake_case:						Optional[int]			  =						ort.SessionOptions()
         __snake_case:						List[Any]			  =						False
         return options




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

         __snake_case:						int			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Any			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=10							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[Any]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Any			  =						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

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






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

         __snake_case:						Optional[int]			  =						"""A red cat sitting on a park bench"""

         __snake_case:						Dict			  =						np.random.RandomState(0		)
         __snake_case:						Optional[Any]			  =						pipe(
             prompt=A							,    image=A							,    mask_image=A							,    guidance_scale=7.5							,    num_inference_steps=20							,    generator=A							,    output_type="""np"""							,    )
         __snake_case:						List[str]			  =						output.images
         __snake_case:						str			  =						images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         __snake_case:						Union[str, Any]			  =						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

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






