
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   __a		(      *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
														pass






def      lowerCamelCase__       (       __lowerCAmelCase						:						Image       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						Image       ):




			"""simple docstring"""


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





@is_pipeline_test
@require_vision
@require_torch
class 			_lowerCAmelCase      (			unittest.TestCase ):
				_lowercase    	=dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
				_lowercase    	=dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							pass




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




				@slow
				@require_torch
				def   __a		(      self  )       ->   Any:
							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   __a		(      self  )       ->   List[Any]:
							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},
							    ]       ,       )





def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						set       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			len(__lowerCAmelCase       ), len(grid[0]       )
			if (
			    min(__lowerCAmelCase					,							__lowerCAmelCase       ) < 0
			    or row == row_length
			    or col == col_length
			    or (row, col) in visit
			    or grid[row][col] == 1
			):
						return 0
			if row == row_length - 1 and col == col_length - 1:
						return 1

			visit.add((row, col)       )

			lowerCAmelCase_      			=			0
			count += depth_first_search(__lowerCAmelCase					,							row + 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							row - 1					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col + 1					,							__lowerCAmelCase       )
			count += depth_first_search(__lowerCAmelCase					,							__lowerCAmelCase					,							col - 1					,							__lowerCAmelCase       )

			visit.remove((row, col)       )
			return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()



