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__							:



					@staticmethod
					def __SCREAMING_SNAKE_CASE							(						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
										pass
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       hashlib.mda(image.tobytes()						)
					return m.hexdigest()[:10]
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       np.array(__lowerCAmelCase						)
					__a		 =       npimg.shape
					return {"hash": hashimage(__lowerCAmelCase						), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 						a__							(       unittest.TestCase		):
			A__    :       str        =      dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if MODEL_FOR_MASK_GENERATION_MAPPING else [])		)
			A__    :       List[Any]        =      dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()		) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])		)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[Any]:
								__a		 =       MaskGenerationPipeline(model=__lowercase				,						image_processor=__lowercase   )
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[int]:
								pass



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



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       pipeline('mask-generation'				,						model='facebook/sam-vit-huge'   )

								__a		 =       image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'				,						points_per_batch=2_5_6   )

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

								# fmt: off
								self.assertEqual(
								    nested_simplify(__lowercase				,						decimals=4   )				,						[
								        {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_444},
								        {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
								        {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_167},
								        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_132},
								        {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_053},
								        {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_967},
								        {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
								        {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_909},
								        {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_879},
								        {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_834},
								        {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_716},
								        {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_612},
								        {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_599},
								        {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_552},
								        {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_532},
								        {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_516},
								        {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_499},
								        {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_483},
								        {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_464},
								        {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
								        {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
								        {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_408},
								        {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_335},
								        {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_326},
								        {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_262},
								        {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_999},
								        {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_986},
								        {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_984},
								        {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_873},
								        {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8_871}
								    ]				,						)
								# fmt: on







			@require_torch
			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       '''facebook/sam-vit-huge'''
								__a		 =       pipeline('mask-generation'				,						model=__lowercase   )

								__a		 =       image_segmenter(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'				,						pred_iou_thresh=1				,						points_per_batch=2_5_6   )

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

								self.assertEqual(
								    nested_simplify(__lowercase				,						decimals=4   )				,						[
								        {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_444},
								        {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_210},
								        {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_167},
								        {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_132},
								        {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_053},
								    ]				,						)





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# Return True if there is node that has not iterated.
					__a		 =       [False] * len(__lowerCamelCase						)
					__a		 =       []
					queue.append(__lowerCamelCase						)
					__a		 =       True

					while queue:
										__a		 =       queue.pop(0						)
										for ind in range(len(graph[u]						)						):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(__lowerCamelCase						)
																				__a		 =       True
																				__a		 =       u

					return visited[t]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# This array is filled by BFS and to store path
					__a		 =       [-1] * (len(__lowerCamelCase						))
					__a		 =       0
					while bfs(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
										__a		 =       float('Inf'						)
										__a		 =       sink

										while s != source:
															# Find the minimum value in select path
															__a		 =       min(__lowerCamelCase				,    graph[parent[s]][s]						)
															__a		 =       parent[s]

										max_flow += path_flow
										__a		 =       sink

										while v != source:
															__a		 =       parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															__a		 =       parent[v]
					return max_flow


lowerCamelCase_    :		str							=	[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

lowerCamelCase_						,  lowerCamelCase_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




