





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase						:   List[str]			  =     logging.get_logger(__name__)





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  List[Any]					    =	'''timm_backbone'''



				def __init__(			self     ,					_A=None     ,					_A=3     ,					_A=True     ,					_A=True     ,					_A=None     ,					**_A     ,					):
									super().__init__(**_A     )
									__A	:						Any       =				backbone
									__A	:						Optional[int]       =				num_channels
									__A	:						List[Any]       =				features_only
									__A	:						List[str]       =				use_pretrained_backbone
									__A	:						int       =				True
									__A	:						List[Any]       =				out_indices if out_indices is not None else (-1,)











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 						_A:


								"""simple docstring"""


								@staticmethod
								def 					UpperCAmelCase_		(			*_A     ,					**_A     ):
													pass







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					__A	:						str       =				hashlib.mda(image.tobytes()  )
					return m.hexdigest()[:10]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Dict:
					__A	:						Dict       =				np.array(a  )
					__A	:						List[Any]       =				npimg.shape
					return {"hash": hashimage(a  ), "shape": shape}





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


				"""simple docstring"""


				UpperCamelCase    :  str					    =	dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
				UpperCamelCase    :  int					    =	dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Dict       =				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     ,					_A     ,					_A     ):
									pass



				@require_tf
				@unittest.skip('Image segmentation not implemented in TF'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				@slow
				@require_torch
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				pipeline('mask-generation'     ,					model='facebook/sam-vit-huge'     )

									__A	:						List[str]       =				image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[Any]       =				[]
									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': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
									        {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
									        {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
									        {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
									        {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
									        {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
									        {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
									        {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
									        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
									        {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
									        {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
									        {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
									        {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
									        {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
									        {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
									        {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
									        {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
									        {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
									        {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
									        {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
									        {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
									        {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
									        {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
									        {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
									    ]     ,					)
									# fmt: on





				@require_torch
				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				'facebook/sam-vit-huge'
									__A	:						List[str]       =				pipeline('mask-generation'     ,					model=_A     )

									__A	:						Tuple       =				image_segmenter(
									    'http://images.cocodataset.org/val2017/000000039769.jpg'     ,					pred_iou_thresh=1     ,					points_per_batch=256     )

									# Shortening by hashing
									__A	:						List[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': 1.0_4_4_4},
									        {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
									        {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
									        {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
									        {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
									    ]     ,					)




