
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},
							    ]       ,       )





from __future__ import annotations

_A     =  list[tuple[int, int]]

_A     =  [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_A     =  ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   Tuple:
							lowerCAmelCase_      			=			pos_x
							lowerCAmelCase_      			=			pos_y
							lowerCAmelCase_      			=			(pos_y, pos_x)
							lowerCAmelCase_      			=			goal_x
							lowerCAmelCase_      			=			goal_y
							lowerCAmelCase_      			=			g_cost
							lowerCAmelCase_      			=			parent
							lowerCAmelCase_      			=			self.calculate_heuristic()




				def   __a		(      self  )       ->   float:
							lowerCAmelCase_      			=			abs(self.pos_x - self.goal_x  )
							lowerCAmelCase_      			=			abs(self.pos_y - self.goal_y  )
							return dx + dy




				def __lt__(      self       ,       _UpperCamelCase  )       ->   bool:
							return self.f_cost < other.f_cost





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			Node(start[1]       ,       start[0]       ,       goal[1]       ,       goal[0]       ,       0       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			Node(goal[1]       ,       goal[0]       ,       goal[1]       ,       goal[0]       ,       99_999       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			[self.start]
							lowerCAmelCase_      			=			[]

							lowerCAmelCase_      			=			False




				def   __a		(      self  )       ->   Path | None:
							while self.open_nodes:
										# Open Nodes are sorted using __lt__
										self.open_nodes.sort()
										lowerCAmelCase_      			=			self.open_nodes.pop(0  )

										if current_node.pos == self.target.pos:
													lowerCAmelCase_      			=			True
													return self.retrace_path(_UpperCamelCase  )

										self.closed_nodes.append(_UpperCamelCase  )
										lowerCAmelCase_      			=			self.get_successors(_UpperCamelCase  )

										for child_node in successors:
													if child_node in self.closed_nodes:
																continue

													if child_node not in self.open_nodes:
																self.open_nodes.append(_UpperCamelCase  )
													else:
																# retrieve the best current path
																lowerCAmelCase_      			=			self.open_nodes.pop(self.open_nodes.index(_UpperCamelCase  )  )

																if child_node.g_cost < better_node.g_cost:
																			self.open_nodes.append(_UpperCamelCase  )
																else:
																			self.open_nodes.append(_UpperCamelCase  )

							if not self.reached:
										return [self.start.pos]
							return None




				def   __a		(      self       ,       _UpperCamelCase  )       ->   list[Node]:
							lowerCAmelCase_      			=			[]
							for action in delta:
										lowerCAmelCase_      			=			parent.pos_x + action[1]
										lowerCAmelCase_      			=			parent.pos_y + action[0]

										if not (0 <= pos_x <= len(grid[0]  ) - 1 and 0 <= pos_y <= len(_UpperCamelCase  ) - 1):
													continue

										if grid[pos_y][pos_x] != 0:
													continue

										successors.append(
										    Node(
										        _UpperCamelCase       ,       _UpperCamelCase       ,       self.target.pos_y       ,       self.target.pos_x       ,       parent.g_cost + 1       ,       _UpperCamelCase       ,       )  )
							return successors




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Path:
							lowerCAmelCase_      			=			node
							lowerCAmelCase_      			=			[]
							while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)  )
										lowerCAmelCase_      			=			current_node.parent
							path.reverse()
							return path


if __name__ == "__main__":
			_A     =  (0, 0)
			_A     =  (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			print("------")

			_A     =  GreedyBestFirst(init, goal)
			_A     =  greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									_A     =  2

						for elem in grid:
									print(elem)



