





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        __UpperCamelCase( *A_       ,							**A_   ):
           '''simple docstring'''




           pass



def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   hashlib.mda(image.tobytes()     )
 return m.hexdigest()[:10]



def 		A_				(     _lowerCAmelCase     )						-> Dict:
 UpperCamelCase	:					Dict       	=   np.array(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[int]       	=   npimg.shape
 return {"hash": hashimage(_lowerCAmelCase     ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 					A__   (       unittest.TestCase			):
      _UpperCAmelCase	:Tuple						    =   dict(
          (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
      _UpperCAmelCase	:List[str]						    =   dict(
          (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   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       ,							A_       ,							A_   ):
       '''simple docstring'''




       pass





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




       pass





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




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

       UpperCamelCase	:					Optional[Any]       	=   image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"       ,							points_per_batch=256   )

       # Shortening by hashing
       UpperCamelCase	:					Optional[Any]       	=   []
       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": (480, 640)}, "scores": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
               {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
               {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
               {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
               {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
               {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
               {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
               {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
               {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
               {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
               {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
               {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
               {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
               {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
               {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
               {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
               {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
               {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
               {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
               {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
               {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
               {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
               {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
               {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
               {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
           ]       ,							)
       # fmt: on





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




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

       UpperCamelCase	:					Optional[Any]       	=   image_segmenter(
           "http://images.cocodataset.org/val2017/000000039769.jpg"       ,							pred_iou_thresh=1       ,							points_per_batch=256   )

       # Shortening by hashing
       UpperCamelCase	:					Dict       	=   []
       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": (480, 640)}, "scores": 1.04_44},
               {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
               {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
               {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
               {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
           ]       ,							)







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase       :     str   =     logging.get_logger(__name__)

__lowerCamelCase       :     str   =     {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}



class 					A__   (       __snake_case       ,   __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   'convnextv2'





      def __init__( self       ,							A_=3       ,							A_=4       ,							A_=4       ,							A_=None       ,							A_=None       ,							A_="gelu"       ,							A_=0.02       ,							A_=1e-12       ,							A_=0.0       ,							A_=224       ,							A_=None       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )

       UpperCamelCase	:					Dict       	=   num_channels
       UpperCamelCase	:					Union[str, Any]       	=   patch_size
       UpperCamelCase	:					Union[str, Any]       	=   num_stages
       UpperCamelCase	:					List[Any]       	=   [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       UpperCamelCase	:					List[str]       	=   [3, 3, 9, 3] if depths is None else depths
       UpperCamelCase	:					Dict       	=   hidden_act
       UpperCamelCase	:					Union[str, Any]       	=   initializer_range
       UpperCamelCase	:					Tuple       	=   layer_norm_eps
       UpperCamelCase	:					str       	=   drop_path_rate
       UpperCamelCase	:					List[str]       	=   image_size
       UpperCamelCase	:					List[str]       	=   ["stem"] + [F"""stage{idx}""" for idx in range(1       ,							len(self.depths   ) + 1   )]
       UpperCamelCase	,    UpperCamelCase	:					str       	=   get_aligned_output_features_output_indices(
           out_features=A_       ,							out_indices=A_       ,							stage_names=self.stage_names   )
