



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	:





           @staticmethod
           def 	__snake_case    (       *UpperCamelCase :							Tuple     ,  **UpperCamelCase :							Union[str, Any]				)->str:
            pass






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Image     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hashlib.mda(image.tobytes()     )
 return m.hexdigest()[:10]






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Image     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	np.array(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Any       =	npimg.shape
 return {"hash": hashimage(__lowerCamelCase     ), "shape": shape}






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





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							int				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    int       =	MaskGenerationPipeline(model=UpperCamelCase     ,  image_processor=UpperCamelCase				)
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]





     def 	__snake_case    (       self :							Optional[int]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict				)->List[Any]:
      pass





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





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

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

      # Shortening by hashing
      __SCREAMING_SNAKE_CASE       :    Dict       =	[]
      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 	__snake_case    (       self :							Any				)->Any:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"facebook/sam-vit-huge"
      __SCREAMING_SNAKE_CASE       :    Any       =	pipeline("mask-generation"     ,  model=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_segmenter(
          "http://images.cocodataset.org/val2017/000000039769.jpg"     ,  pred_iou_thresh=1     ,  points_per_batch=2_5_6				)

      # Shortening by hashing
      __SCREAMING_SNAKE_CASE       :    Dict       =	[]
      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},
          ]     ,  )











def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if height >= 1:
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
  move_disk(__lowerCamelCase		,	__lowerCamelCase     )
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 print("moving disk from"		,	__lowerCamelCase		,	"to"		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	int(input("Height of hanoi: "     ).strip()     )
 move_tower(__lowerCamelCase		,	"A"		,	"B"		,	"C"     )


if __name__ == "__main__":
      main()






