
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 		UpperCAmelCase(*_A				:   Tuple    ,   **_A				:   Any       )						->						int:
            pass



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

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



def 						lowercase_      (       A__		)							->							Dict:
    """simple docstring"""

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







@is_pipeline_test
@require_vision
@require_torch
class 					lowerCamelCase		(  unittest.TestCase  ):
     UpperCAmelCase__    :							int									=		dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])  )
     UpperCAmelCase__    :							Optional[int]									=		dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()  ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])  )





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





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   str    ,   _A				:   Tuple       )						->						str:
         pass





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





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

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

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

         # fmt: off
         self.assertEqual(
             nested_simplify(_A    ,   decimals=4       )    ,   [
                 {"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.04_44},
                 {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.0_21},
                 {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.01_67},
                 {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.01_32},
                 {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.00_53},
                 {"mask": {"hash": "e2d0b7a0b7", "shape": (4_8_0, 6_4_0)}, "scores": 0.99_67},
                 {"mask": {"hash": "453c7844bd", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_93},
                 {"mask": {"hash": "3d44f2926d", "shape": (4_8_0, 6_4_0)}, "scores": 0.99_09},
                 {"mask": {"hash": "64033ddc3f", "shape": (4_8_0, 6_4_0)}, "scores": 0.98_79},
                 {"mask": {"hash": "801064ff79", "shape": (4_8_0, 6_4_0)}, "scores": 0.98_34},
                 {"mask": {"hash": "6172f276ef", "shape": (4_8_0, 6_4_0)}, "scores": 0.97_16},
                 {"mask": {"hash": "b49e60e084", "shape": (4_8_0, 6_4_0)}, "scores": 0.96_12},
                 {"mask": {"hash": "a811e775fd", "shape": (4_8_0, 6_4_0)}, "scores": 0.95_99},
                 {"mask": {"hash": "a6a8ebcf4b", "shape": (4_8_0, 6_4_0)}, "scores": 0.95_52},
                 {"mask": {"hash": "9d8257e080", "shape": (4_8_0, 6_4_0)}, "scores": 0.95_32},
                 {"mask": {"hash": "32de6454a8", "shape": (4_8_0, 6_4_0)}, "scores": 0.95_16},
                 {"mask": {"hash": "af3d4af2c8", "shape": (4_8_0, 6_4_0)}, "scores": 0.94_99},
                 {"mask": {"hash": "3c6db475fb", "shape": (4_8_0, 6_4_0)}, "scores": 0.94_83},
                 {"mask": {"hash": "c290813fb9", "shape": (4_8_0, 6_4_0)}, "scores": 0.94_64},
                 {"mask": {"hash": "b6f0b8f606", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_43},
                 {"mask": {"hash": "92ce16bfdf", "shape": (4_8_0, 6_4_0)}, "scores": 0.9_43},
                 {"mask": {"hash": "c749b25868", "shape": (4_8_0, 6_4_0)}, "scores": 0.94_08},
                 {"mask": {"hash": "efb6cab859", "shape": (4_8_0, 6_4_0)}, "scores": 0.93_35},
                 {"mask": {"hash": "1ff2eafb30", "shape": (4_8_0, 6_4_0)}, "scores": 0.93_26},
                 {"mask": {"hash": "788b798e24", "shape": (4_8_0, 6_4_0)}, "scores": 0.92_62},
                 {"mask": {"hash": "abea804f0e", "shape": (4_8_0, 6_4_0)}, "scores": 0.89_99},
                 {"mask": {"hash": "7b9e8ddb73", "shape": (4_8_0, 6_4_0)}, "scores": 0.89_86},
                 {"mask": {"hash": "cd24047c8a", "shape": (4_8_0, 6_4_0)}, "scores": 0.89_84},
                 {"mask": {"hash": "6943e6bcbd", "shape": (4_8_0, 6_4_0)}, "scores": 0.88_73},
                 {"mask": {"hash": "b5f47c9191", "shape": (4_8_0, 6_4_0)}, "scores": 0.88_71}
             ]    ,   )
         # fmt: on





     @require_torch
     @slow
     def 		UpperCAmelCase(self				:   int       )						->						List[Any]:
         snake_case								= "facebook/sam-vit-huge"
         snake_case								= pipeline("mask-generation"    ,   model=_A       )

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

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

         self.assertEqual(
             nested_simplify(_A    ,   decimals=4       )    ,   [
                 {"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.04_44},
                 {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.02_10},
                 {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.01_67},
                 {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.01_32},
                 {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.00_53},
             ]    ,   )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

   _A   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

