






"""simple docstring"""







import copy

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


_UpperCamelCase					      =  logging.get_logger(__name__)

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







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      '''deta'''
    SCREAMING_SNAKE_CASE		=      {
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
    }






    def __init__(			self     ,A=None     ,A=900     ,A=2_048     ,A=6     ,A=2_048     ,A=8     ,A=6     ,A=1_024     ,A=8     ,A=0.0     ,A=True     ,A="relu"     ,A=256     ,A=0.1     ,A=0.0     ,A=0.0     ,A=0.02     ,A=1.0     ,A=True     ,A=False     ,A="sine"     ,A=5     ,A=4     ,A=4     ,A=True     ,A=300     ,A=True     ,A=True     ,A=1     ,A=5     ,A=2     ,A=1     ,A=1     ,A=5     ,A=2     ,A=0.1     ,A=0.25     ,**A     ,):
          if backbone_config is None:
                logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
                UpperCAmelCase          =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""] )
          else:
                if isinstance(A     ,A ):
                      UpperCAmelCase          =	backbone_config.pop("""model_type""" )
                      UpperCAmelCase          =	CONFIG_MAPPING[backbone_model_type]
                      UpperCAmelCase          =	config_class.from_dict(A )

          UpperCAmelCase          =	backbone_config
          UpperCAmelCase          =	num_queries
          UpperCAmelCase          =	max_position_embeddings
          UpperCAmelCase          =	d_model
          UpperCAmelCase          =	encoder_ffn_dim
          UpperCAmelCase          =	encoder_layers
          UpperCAmelCase          =	encoder_attention_heads
          UpperCAmelCase          =	decoder_ffn_dim
          UpperCAmelCase          =	decoder_layers
          UpperCAmelCase          =	decoder_attention_heads
          UpperCAmelCase          =	dropout
          UpperCAmelCase          =	attention_dropout
          UpperCAmelCase          =	activation_dropout
          UpperCAmelCase          =	activation_function
          UpperCAmelCase          =	init_std
          UpperCAmelCase          =	init_xavier_std
          UpperCAmelCase          =	encoder_layerdrop
          UpperCAmelCase          =	auxiliary_loss
          UpperCAmelCase          =	position_embedding_type
          # deformable attributes
          UpperCAmelCase          =	num_feature_levels
          UpperCAmelCase          =	encoder_n_points
          UpperCAmelCase          =	decoder_n_points
          UpperCAmelCase          =	two_stage
          UpperCAmelCase          =	two_stage_num_proposals
          UpperCAmelCase          =	with_box_refine
          UpperCAmelCase          =	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
          UpperCAmelCase          =	class_cost
          UpperCAmelCase          =	bbox_cost
          UpperCAmelCase          =	giou_cost
          # Loss coefficients
          UpperCAmelCase          =	mask_loss_coefficient
          UpperCAmelCase          =	dice_loss_coefficient
          UpperCAmelCase          =	bbox_loss_coefficient
          UpperCAmelCase          =	giou_loss_coefficient
          UpperCAmelCase          =	eos_coefficient
          UpperCAmelCase          =	focal_alpha
          super().__init__(is_encoder_decoder=A     ,**A )






    @property
    def     _UpperCamelCase							(			self ):
          return self.encoder_attention_heads






    @property
    def     _UpperCamelCase							(			self ):
          return self.d_model






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	copy.deepcopy(self.__dict__ )
          UpperCAmelCase          =	self.backbone_config.to_dict()
          UpperCAmelCase          =	self.__class__.model_type
          return output











"""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  lowerCamelCase__ :






      @staticmethod
      def     _UpperCamelCase							(			*A     ,**A ):
            pass

def _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """simple docstring"""






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







@is_pipeline_test
@require_vision
@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      dict(
        (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
    SCREAMING_SNAKE_CASE		=      dict(
        (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	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 ):
          pass






    @require_tf
    @unittest.skip("""Image segmentation not implemented in TF""" )
    def     _UpperCamelCase							(			self ):
          pass






    @slow
    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model="""facebook/sam-vit-huge""" )

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

          # Shortening by hashing
          UpperCAmelCase          =	[]
          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""": 1.0444},
                  {"""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     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""facebook/sam-vit-huge"""
          UpperCAmelCase          =	pipeline("""mask-generation"""     ,model=A )

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

          # Shortening by hashing
          UpperCAmelCase          =	[]
          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""": 1.0444},
                  {"""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},
              ]     ,)



