





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 	UpperCamelCase__				:



           '''simple docstring'''

           @staticmethod
           def        SCREAMING_SNAKE_CASE__						(							*lowerCamelCase__		:					Tuple		,**lowerCamelCase__		:					List[str] )  ->	str:




                 '''simple docstring'''


                 pass



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


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



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


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



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



     '''simple docstring'''

     __snake_case					:			Dict				=    dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])     )
     __snake_case					:			Dict				=    dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()     ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])     )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		MaskGenerationPipeline(model=lowerCamelCase__		,image_processor=lowerCamelCase__ )
           return image_segmenter, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Dict )  ->	Optional[Any]:




           '''simple docstring'''


           pass






     @require_tf
     @unittest.skip("""Image segmentation not implemented in TF""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Tuple:




           '''simple docstring'''


           pass






     @slow
     @require_torch
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Union[str, Any]:




           '''simple docstring'''


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

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

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

           # fmt: off
           self.assertEqual(
               nested_simplify(lowerCamelCase__		,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] )  ->	Dict:




           '''simple docstring'''


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

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

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

           self.assertEqual(
               nested_simplify(lowerCamelCase__		,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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "deta"
     __snake_case					:			int				=    {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					Any=None		,lowerCamelCase__		:					Optional[int]=900		,lowerCamelCase__		:					Optional[Any]=2048		,lowerCamelCase__		:					Optional[Any]=6		,lowerCamelCase__		:					Dict=2048		,lowerCamelCase__		:					Optional[int]=8		,lowerCamelCase__		:					List[str]=6		,lowerCamelCase__		:					Union[str, Any]=1024		,lowerCamelCase__		:					str=8		,lowerCamelCase__		:					Union[str, Any]=0.0		,lowerCamelCase__		:					Dict=True		,lowerCamelCase__		:					Dict="relu"		,lowerCamelCase__		:					int=256		,lowerCamelCase__		:					Optional[Any]=0.1		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Dict=0.02		,lowerCamelCase__		:					List[Any]=1.0		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					int=False		,lowerCamelCase__		:					int="sine"		,lowerCamelCase__		:					Any=5		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					Optional[int]=4		,lowerCamelCase__		:					List[Any]=True		,lowerCamelCase__		:					Optional[int]=300		,lowerCamelCase__		:					Optional[Any]=True		,lowerCamelCase__		:					Optional[Any]=True		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Tuple=5		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Dict=1		,lowerCamelCase__		:					str=1		,lowerCamelCase__		:					List[Any]=5		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Union[str, Any]=0.1		,lowerCamelCase__		:					List[str]=0.25		,**lowerCamelCase__		:					int		,)  ->	List[Any]:




           '''simple docstring'''


           if backbone_config is None:
                 logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
                 SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""] )
           else:
                 if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                       SCREAMING_SNAKE_CASE	=		backbone_config.pop("""model_type""" )
                       SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE	=		config_class.from_dict(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		backbone_config
           SCREAMING_SNAKE_CASE	=		num_queries
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		d_model
           SCREAMING_SNAKE_CASE	=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		encoder_attention_heads
           SCREAMING_SNAKE_CASE	=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		decoder_layers
           SCREAMING_SNAKE_CASE	=		decoder_attention_heads
           SCREAMING_SNAKE_CASE	=		dropout
           SCREAMING_SNAKE_CASE	=		attention_dropout
           SCREAMING_SNAKE_CASE	=		activation_dropout
           SCREAMING_SNAKE_CASE	=		activation_function
           SCREAMING_SNAKE_CASE	=		init_std
           SCREAMING_SNAKE_CASE	=		init_xavier_std
           SCREAMING_SNAKE_CASE	=		encoder_layerdrop
           SCREAMING_SNAKE_CASE	=		auxiliary_loss
           SCREAMING_SNAKE_CASE	=		position_embedding_type
           # deformable attributes
           SCREAMING_SNAKE_CASE	=		num_feature_levels
           SCREAMING_SNAKE_CASE	=		encoder_n_points
           SCREAMING_SNAKE_CASE	=		decoder_n_points
           SCREAMING_SNAKE_CASE	=		two_stage
           SCREAMING_SNAKE_CASE	=		two_stage_num_proposals
           SCREAMING_SNAKE_CASE	=		with_box_refine
           SCREAMING_SNAKE_CASE	=		assign_first_stage
           if two_stage is True and with_box_refine is False:
                 raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
           # Hungarian matcher
           SCREAMING_SNAKE_CASE	=		class_cost
           SCREAMING_SNAKE_CASE	=		bbox_cost
           SCREAMING_SNAKE_CASE	=		giou_cost
           # Loss coefficients
           SCREAMING_SNAKE_CASE	=		mask_loss_coefficient
           SCREAMING_SNAKE_CASE	=		dice_loss_coefficient
           SCREAMING_SNAKE_CASE	=		bbox_loss_coefficient
           SCREAMING_SNAKE_CASE	=		giou_loss_coefficient
           SCREAMING_SNAKE_CASE	=		eos_coefficient
           SCREAMING_SNAKE_CASE	=		focal_alpha
           super().__init__(is_encoder_decoder=lowerCamelCase__		,**lowerCamelCase__ )






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		copy.deepcopy(self.__dict__ )
           SCREAMING_SNAKE_CASE	=		self.backbone_config.to_dict()
           SCREAMING_SNAKE_CASE	=		self.__class__.model_type
           return output


