


'''simple docstring'''


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 		lowerCAmelCase__	:




									@staticmethod
									def 							lowerCAmelCase__			(						*lowerCamelCase__     :   Optional[int]		,     **lowerCamelCase__     :   List[Any]       )      ->Dict:



											'''simple docstring'''







											pass






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Optional[Any]	    =					hashlib.mda(image.tobytes()      )
		return m.hexdigest()[:10]






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Optional[int]	    =					np.array(__lowerCAmelCase      )
		_UpperCAmelCase						:				Tuple	    =					npimg.shape
		return {"hash": hashimage(__lowerCAmelCase      ), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class 		lowerCAmelCase__	(	unittest.TestCase		):
			lowerCAmelCase       :		List[str]				      =   dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
			lowerCAmelCase       :		int				      =   dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   Dict		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   Union[str, Any]       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					MaskGenerationPipeline(model=lowerCamelCase__		,     image_processor=lowerCamelCase__       )
					return image_segmenter, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   List[str]		,     lowerCamelCase__     :   Any       )      ->List[Any]:



					'''simple docstring'''







					pass




			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"       )
			def 							lowerCAmelCase__			(						self     :   Any       )      ->Any:



					'''simple docstring'''







					pass




			@slow
			@require_torch
			def 							lowerCAmelCase__			(						self     :   str       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					pipeline("mask-generation"		,     model="facebook/sam-vit-huge"       )

					_UpperCAmelCase						:				List[str]	    =					image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"		,     points_per_batch=2_56       )

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

					# fmt: off
					self.assertEqual(
					    nested_simplify(lowerCamelCase__		,     decimals=4       )		,     [
					        {"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0_4_4_4},
					        {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1},
					        {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
					        {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
					        {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
					        {"mask": {"hash": "e2d0b7a0b7", "shape": (4_80, 6_40)}, "scores": 0.9_9_6_7},
					        {"mask": {"hash": "453c7844bd", "shape": (4_80, 6_40)}, "scores": 0.9_9_3},
					        {"mask": {"hash": "3d44f2926d", "shape": (4_80, 6_40)}, "scores": 0.9_9_0_9},
					        {"mask": {"hash": "64033ddc3f", "shape": (4_80, 6_40)}, "scores": 0.9_8_7_9},
					        {"mask": {"hash": "801064ff79", "shape": (4_80, 6_40)}, "scores": 0.9_8_3_4},
					        {"mask": {"hash": "6172f276ef", "shape": (4_80, 6_40)}, "scores": 0.9_7_1_6},
					        {"mask": {"hash": "b49e60e084", "shape": (4_80, 6_40)}, "scores": 0.9_6_1_2},
					        {"mask": {"hash": "a811e775fd", "shape": (4_80, 6_40)}, "scores": 0.9_5_9_9},
					        {"mask": {"hash": "a6a8ebcf4b", "shape": (4_80, 6_40)}, "scores": 0.9_5_5_2},
					        {"mask": {"hash": "9d8257e080", "shape": (4_80, 6_40)}, "scores": 0.9_5_3_2},
					        {"mask": {"hash": "32de6454a8", "shape": (4_80, 6_40)}, "scores": 0.9_5_1_6},
					        {"mask": {"hash": "af3d4af2c8", "shape": (4_80, 6_40)}, "scores": 0.9_4_9_9},
					        {"mask": {"hash": "3c6db475fb", "shape": (4_80, 6_40)}, "scores": 0.9_4_8_3},
					        {"mask": {"hash": "c290813fb9", "shape": (4_80, 6_40)}, "scores": 0.9_4_6_4},
					        {"mask": {"hash": "b6f0b8f606", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
					        {"mask": {"hash": "92ce16bfdf", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
					        {"mask": {"hash": "c749b25868", "shape": (4_80, 6_40)}, "scores": 0.9_4_0_8},
					        {"mask": {"hash": "efb6cab859", "shape": (4_80, 6_40)}, "scores": 0.9_3_3_5},
					        {"mask": {"hash": "1ff2eafb30", "shape": (4_80, 6_40)}, "scores": 0.9_3_2_6},
					        {"mask": {"hash": "788b798e24", "shape": (4_80, 6_40)}, "scores": 0.9_2_6_2},
					        {"mask": {"hash": "abea804f0e", "shape": (4_80, 6_40)}, "scores": 0.8_9_9_9},
					        {"mask": {"hash": "7b9e8ddb73", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_6},
					        {"mask": {"hash": "cd24047c8a", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_4},
					        {"mask": {"hash": "6943e6bcbd", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_3},
					        {"mask": {"hash": "b5f47c9191", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_1}
					    ]		,     )
					# fmt: on






			@require_torch
			@slow
			def 							lowerCAmelCase__			(						self     :   Any       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					"facebook/sam-vit-huge"
					_UpperCAmelCase						:				int	    =					pipeline("mask-generation"		,     model=lowerCamelCase__       )

					_UpperCAmelCase						:				Optional[Any]	    =					image_segmenter(
					    "http://images.cocodataset.org/val2017/000000039769.jpg"		,     pred_iou_thresh=1		,     points_per_batch=2_56       )

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

					self.assertEqual(
					    nested_simplify(lowerCamelCase__		,     decimals=4       )		,     [
					        {"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0_4_4_4},
					        {"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1_0},
					        {"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
					        {"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
					        {"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
					    ]		,     )







'''simple docstring'''


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)



