"""simple docstring"""


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 	snake_case__	(     *_UpperCAmelCase,				**_UpperCAmelCase  ):
              '''simple docstring'''



              pass







def 					__a   (		A      ):






     '''simple docstring'''



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







def 					__a   (		A      ):






     '''simple docstring'''



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






@is_pipeline_test
@require_vision
@require_torch
class    a__    (   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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							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,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        pass


   @require_tf
   @unittest.skip("Image segmentation not implemented in TF"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @slow
   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							pipeline("mask-generation",				model="facebook/sam-vit-huge"  )

        lowercase__				 =							image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg",				points_per_batch=256  )

        # Shortening by hashing
        lowercase__				 =							[]
        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": 1.0_444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
                {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_967},
                {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
                {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_909},
                {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_879},
                {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_834},
                {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_716},
                {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_612},
                {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_599},
                {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_552},
                {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_532},
                {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_516},
                {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_499},
                {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_483},
                {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_464},
                {"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": 0.9_408},
                {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_335},
                {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_326},
                {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_262},
                {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_999},
                {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_986},
                {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_984},
                {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_873},
                {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_871}
            ],				)
        # fmt: on


   @require_torch
   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"facebook/sam-vit-huge"
        lowercase__				 =							pipeline("mask-generation",				model=_UpperCAmelCase  )

        lowercase__				 =							image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg",				pred_iou_thresh=1,				points_per_batch=256  )

        # Shortening by hashing
        lowercase__				 =							[]
        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": 1.0_444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_210},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
            ],				)





"""simple docstring"""


import os
import sys

lowerCAmelCase_:					Any     =					os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCAmelCase_:					Union[str, Any]     =					[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]







@add_start_docstrings(AutoConfig.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoConfig.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoTokenizer.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoTokenizer.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModel.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModel.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForCausalLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForCausalLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForMaskedLM.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForMaskedLM.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForSequenceClassification.from_pretrained(*A    ,				**A      )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__      )
def 					__a   (		*A    ,				**A      ):






     '''simple docstring'''



     return AutoModelForQuestionAnswering.from_pretrained(*A    ,				**A      )




