
"""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        _SCREAMING_SNAKE_CASE  :






					'''simple docstring'''



					@staticmethod
					def 				__lowerCamelCase	(					*UpperCAmelCase_	:			Optional[int]				,     **UpperCAmelCase_	:			Tuple  )		->  str:







												"""simple docstring"""




												pass






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Image						):






							"""simple docstring"""



							_lowerCAmelCase  	=			hashlib.mda(image.tobytes()						)
							return m.hexdigest()[:10]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Image						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.array(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			npimg.shape
							return {"hash": hashimage(SCREAMING_SNAKE_CASE						), "shape": shape}

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






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
				SCREAMING_SNAKE_CASE_:							Any							=					dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			Optional[int]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			Optional[int]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			MaskGenerationPipeline(model=UpperCAmelCase_				,     image_processor=UpperCAmelCase_  )
											return image_segmenter, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]


				def 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Tuple  )		->  Optional[int]:







											"""simple docstring"""




											pass


				@require_tf
				@unittest.skip('Image segmentation not implemented in TF'  )
				def 				__lowerCamelCase	(					self	:			str  )		->  str:







											"""simple docstring"""




											pass


				@slow
				@require_torch
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			pipeline('mask-generation'				,     model='facebook/sam-vit-huge'  )

											_lowerCAmelCase  	=			image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'				,     points_per_batch=256  )

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

											# fmt: off
											self.assertEqual(
											    nested_simplify(UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			str  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			'facebook/sam-vit-huge'
											_lowerCAmelCase  	=			pipeline('mask-generation'				,     model=UpperCAmelCase_  )

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

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

											self.assertEqual(
											    nested_simplify(UpperCAmelCase_				,     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},
											    ]				,     )








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]						):
														for y in range(image.shape[0]						):
																					_lowerCAmelCase  	=			(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					_lowerCAmelCase  	=			int(summation > 0						)
							return output


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






