






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 				_UpperCamelCase      :


       @staticmethod
       def 			__UpperCAmelCase		(						*__UpperCamelCase       ,    **__UpperCamelCase						)->						Union[str, Any]:
            pass





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       hashlib.mda(image.tobytes()							)
     return m.hexdigest()[:10]





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



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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						List[str]:
           __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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						List[str]:
           pass


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


      @slow
      @require_torch
      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __lowerCAmelCase             =       pipeline("mask-generation"       ,    model="facebook/sam-vit-huge"						)

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

           # 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": (4_8_0, 6_4_0)}, "scores": 1.0_4_4_4},
                   {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_2_1},
                   {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_1_6_7},
                   {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_1_3_2},
                   {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_0_5_3},
                   {"mask": {"hash": "e2d0b7a0b7", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_9_6_7},
                   {"mask": {"hash": "453c7844bd", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_9_3},
                   {"mask": {"hash": "3d44f2926d", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_9_0_9},
                   {"mask": {"hash": "64033ddc3f", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_8_7_9},
                   {"mask": {"hash": "801064ff79", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_8_3_4},
                   {"mask": {"hash": "6172f276ef", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_7_1_6},
                   {"mask": {"hash": "b49e60e084", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_6_1_2},
                   {"mask": {"hash": "a811e775fd", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_5_9_9},
                   {"mask": {"hash": "a6a8ebcf4b", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_5_5_2},
                   {"mask": {"hash": "9d8257e080", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_5_3_2},
                   {"mask": {"hash": "32de6454a8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_5_1_6},
                   {"mask": {"hash": "af3d4af2c8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_4_9_9},
                   {"mask": {"hash": "3c6db475fb", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_4_8_3},
                   {"mask": {"hash": "c290813fb9", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_4_6_4},
                   {"mask": {"hash": "b6f0b8f606", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_4_3},
                   {"mask": {"hash": "92ce16bfdf", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_4_3},
                   {"mask": {"hash": "c749b25868", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_4_0_8},
                   {"mask": {"hash": "efb6cab859", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_3_3_5},
                   {"mask": {"hash": "1ff2eafb30", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_3_2_6},
                   {"mask": {"hash": "788b798e24", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_2_6_2},
                   {"mask": {"hash": "abea804f0e", "shape": (4_8_0, 6_4_0)}, "scores": 0.8_9_9_9},
                   {"mask": {"hash": "7b9e8ddb73", "shape": (4_8_0, 6_4_0)}, "scores": 0.8_9_8_6},
                   {"mask": {"hash": "cd24047c8a", "shape": (4_8_0, 6_4_0)}, "scores": 0.8_9_8_4},
                   {"mask": {"hash": "6943e6bcbd", "shape": (4_8_0, 6_4_0)}, "scores": 0.8_8_7_3},
                   {"mask": {"hash": "b5f47c9191", "shape": (4_8_0, 6_4_0)}, "scores": 0.8_8_7_1}
               ]       ,    )
           # fmt: on


      @require_torch
      @slow
      def 			__UpperCAmelCase		(						self						)->						str:
           __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=2_5_6						)

           # 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": (4_8_0, 6_4_0)}, "scores": 1.0_4_4_4},
                   {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_2_1_0},
                   {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_1_6_7},
                   {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_1_3_2},
                   {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_0_5_3},
               ]       ,    )














from __future__ import annotations





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # We need to create solution object to save path.
     __lowerCAmelCase             =       [[0 for _ in range(__snake_case							)] for _ in range(__snake_case							)]
     __lowerCAmelCase             =       run_maze(__snake_case       ,			0       ,			0       ,			__snake_case							)
     if solved:
          print("\n".join(str(__snake_case							) for row in solutions							)							)
     else:
          print("No solution exists!"							)
     return solved





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # Final check point.
     if i == j == (size - 1):
          __lowerCAmelCase             =       1
          return True

     __lowerCAmelCase             =       (not i < 0) and (not j < 0)  # Check lower bounds
     __lowerCAmelCase             =       (i < size) and (j < size)  # Check upper bounds

     if lower_flag and upper_flag:
          # check for already visited and block points.
          __lowerCAmelCase             =       (not solutions[i][j]) and (not maze[i][j])
          if block_flag:
               # check visited
               __lowerCAmelCase             =       1

               # check for directions
               if (
                   run_maze(__snake_case       ,			i + 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j + 1       ,			__snake_case							)
                   or run_maze(__snake_case       ,			i - 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j - 1       ,			__snake_case							)
               ):
                    return True

               __lowerCAmelCase             =       0
               return False
     return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()






