





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        __magic_name__	:





      """simple docstring"""



      @staticmethod
      def     SCREAMING_SNAKE_CASE		(				*snake_case							:Optional[Any]      ,   **snake_case							:Any	):





         '''simple docstring'''




         pass

def   __snake_case    (       _lowerCAmelCase			:			Image						)			->    str:
   A_       :					str        =      hashlib.mda(image.tobytes()						)
   return m.hexdigest()[:10]

def   __snake_case    (       _lowerCAmelCase			:			Image						)			->    Dict:
   A_       :					List[Any]        =      np.array(_lowerCAmelCase						)
   A_       :					Union[str, Any]        =      npimg.shape
   return {"hash": hashimage(_lowerCAmelCase						), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
     __UpperCamelCase            =  dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:str      ,   snake_case							:Optional[Any]      ,   snake_case							:Tuple	):





        '''simple docstring'''




        A_       :					List[Any]        =      MaskGenerationPipeline(model=snake_case      ,   image_processor=snake_case	)
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Optional[int]      ,   snake_case							:List[str]	):





        '''simple docstring'''




        pass



     @require_tf
     @unittest.skip("Image segmentation not implemented in TF"	)
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        pass



     @slow
     @require_torch
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					Dict        =      pipeline("mask-generation"      ,   model="facebook/sam-vit-huge"	)

        A_       :					List[str]        =      image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"      ,   points_per_batch=256	)

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

        # fmt: off
        self.assertEqual(
            nested_simplify(snake_case      ,   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     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      "facebook/sam-vit-huge"
        A_       :					List[Any]        =      pipeline("mask-generation"      ,   model=snake_case	)

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

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

        self.assertEqual(
            nested_simplify(snake_case      ,   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},
            ]      ,   )







import logging

from transformers import PretrainedConfig


_lowerCAmelCase				:   str						       =						logging.getLogger(__name__)


_lowerCAmelCase				:   Dict						       =						{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''bertabs'''



     def __init__(				self							:Optional[int]      ,   snake_case							:Any=30_522      ,   snake_case							:List[str]=512      ,   snake_case							:str=6      ,   snake_case							:int=512      ,   snake_case							:Optional[Any]=8      ,   snake_case							:Tuple=512      ,   snake_case							:str=0.2      ,   snake_case							:Any=6      ,   snake_case							:Optional[Any]=768      ,   snake_case							:Optional[Any]=8      ,   snake_case							:List[Any]=2_048      ,   snake_case							:Dict=0.2      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					List[str]        =      vocab_size
        A_       :					int        =      max_pos

        A_       :					Tuple        =      enc_layers
        A_       :					Tuple        =      enc_hidden_size
        A_       :					str        =      enc_heads
        A_       :					Optional[Any]        =      enc_ff_size
        A_       :					Optional[Any]        =      enc_dropout

        A_       :					List[str]        =      dec_layers
        A_       :					List[Any]        =      dec_hidden_size
        A_       :					Optional[int]        =      dec_heads
        A_       :					Any        =      dec_ff_size
        A_       :					Optional[int]        =      dec_dropout
