





"""simple docstring"""





import copy

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


__A											=		logging.get_logger(__name__)

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






class 					_lowerCAmelCase   (     lowerCamelCase__       ):
       """simple docstring"""




       __magic_name__     :Union[str, Any]		  =					'''deta'''
       __magic_name__     :int		  =					{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }






       def __init__(    self    , __UpperCAmelCase=None    , __UpperCAmelCase=9_0_0    , __UpperCAmelCase=2_0_4_8    , __UpperCAmelCase=6    , __UpperCAmelCase=2_0_4_8    , __UpperCAmelCase=8    , __UpperCAmelCase=6    , __UpperCAmelCase=1_0_2_4    , __UpperCAmelCase=8    , __UpperCAmelCase=0.0    , __UpperCAmelCase=True    , __UpperCAmelCase="relu"    , __UpperCAmelCase=2_5_6    , __UpperCAmelCase=0.1    , __UpperCAmelCase=0.0    , __UpperCAmelCase=0.0    , __UpperCAmelCase=0.02    , __UpperCAmelCase=1.0    , __UpperCAmelCase=True    , __UpperCAmelCase=False    , __UpperCAmelCase="sine"    , __UpperCAmelCase=5    , __UpperCAmelCase=4    , __UpperCAmelCase=4    , __UpperCAmelCase=True    , __UpperCAmelCase=3_0_0    , __UpperCAmelCase=True    , __UpperCAmelCase=True    , __UpperCAmelCase=1    , __UpperCAmelCase=5    , __UpperCAmelCase=2    , __UpperCAmelCase=1    , __UpperCAmelCase=1    , __UpperCAmelCase=5    , __UpperCAmelCase=2    , __UpperCAmelCase=0.1    , __UpperCAmelCase=0.25    , **__UpperCAmelCase    , ):



         '''simple docstring'''

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

         lowerCAmelCase__			:List[str]											=							backbone_config
         lowerCAmelCase__			:Union[str, Any]											=							num_queries
         lowerCAmelCase__			:Optional[Any]											=							max_position_embeddings
         lowerCAmelCase__			:Union[str, Any]											=							d_model
         lowerCAmelCase__			:Any											=							encoder_ffn_dim
         lowerCAmelCase__			:Union[str, Any]											=							encoder_layers
         lowerCAmelCase__			:Optional[int]											=							encoder_attention_heads
         lowerCAmelCase__			:List[Any]											=							decoder_ffn_dim
         lowerCAmelCase__			:Tuple											=							decoder_layers
         lowerCAmelCase__			:List[Any]											=							decoder_attention_heads
         lowerCAmelCase__			:Optional[int]											=							dropout
         lowerCAmelCase__			:Tuple											=							attention_dropout
         lowerCAmelCase__			:Any											=							activation_dropout
         lowerCAmelCase__			:Optional[Any]											=							activation_function
         lowerCAmelCase__			:int											=							init_std
         lowerCAmelCase__			:int											=							init_xavier_std
         lowerCAmelCase__			:Any											=							encoder_layerdrop
         lowerCAmelCase__			:int											=							auxiliary_loss
         lowerCAmelCase__			:Optional[Any]											=							position_embedding_type
         # deformable attributes
         lowerCAmelCase__			:Tuple											=							num_feature_levels
         lowerCAmelCase__			:str											=							encoder_n_points
         lowerCAmelCase__			:Union[str, Any]											=							decoder_n_points
         lowerCAmelCase__			:List[str]											=							two_stage
         lowerCAmelCase__			:Optional[int]											=							two_stage_num_proposals
         lowerCAmelCase__			:Dict											=							with_box_refine
         lowerCAmelCase__			:Union[str, Any]											=							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
         lowerCAmelCase__			:str											=							class_cost
         lowerCAmelCase__			:int											=							bbox_cost
         lowerCAmelCase__			:Tuple											=							giou_cost
         # Loss coefficients
         lowerCAmelCase__			:Any											=							mask_loss_coefficient
         lowerCAmelCase__			:List[str]											=							dice_loss_coefficient
         lowerCAmelCase__			:Optional[int]											=							bbox_loss_coefficient
         lowerCAmelCase__			:int											=							giou_loss_coefficient
         lowerCAmelCase__			:Any											=							eos_coefficient
         lowerCAmelCase__			:Tuple											=							focal_alpha
         super().__init__(is_encoder_decoder=lowerCAmelCase__    , **lowerCAmelCase__      )






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return self.encoder_attention_heads






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         return self.d_model






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							copy.deepcopy(self.__dict__      )
         lowerCAmelCase__			:List[Any]											=							self.backbone_config.to_dict()
         lowerCAmelCase__			:Union[str, Any]											=							self.__class__.model_type
         return output





'''simple docstring'''




import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
						from PIL import Image
else:






						class 						_UpperCamelCase  :


									'''simple docstring'''

									@staticmethod
									def UpperCamelCase__					(							*lowerCAmelCase__     : Any							,     **lowerCAmelCase__     : Any						):





											"""simple docstring"""






											pass






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


			'''simple docstring'''

			_A      :		Optional[int]			    =		MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

			def UpperCamelCase__					(							self     : str							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		pipeline(
					    """zero-shot-object-detection"""							,     model="""hf-internal-testing/tiny-random-owlvit-object-detection"""						)

					__SCREAMING_SNAKE_CASE       :		Any           =		[
					    {
					        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
					        """candidate_labels""": ["""cat""", """remote""", """couch"""],
					    }
					]
					return object_detector, examples

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		object_detector(examples[0]							,     threshold=0.0						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		len(lowerCAmelCase__						)
					self.assertGreater(lowerCAmelCase__							,     0						)
					self.assertEqual(
					    lowerCAmelCase__							,     [
					        {
					            """score""": ANY(lowerCAmelCase__						),
					            """label""": ANY(lowerCAmelCase__						),
					            """box""": {"""xmin""": ANY(lowerCAmelCase__						), """ymin""": ANY(lowerCAmelCase__						), """xmax""": ANY(lowerCAmelCase__						), """ymax""": ANY(lowerCAmelCase__						)},
					        }
					        for i in range(lowerCAmelCase__						)
					    ]							,     )

			@require_tf
			@unittest.skip("""Zero Shot Object Detection not implemented in TF"""						)
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					pass

			@require_torch
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		pipeline(
					    """zero-shot-object-detection"""							,     model="""hf-internal-testing/tiny-random-owlvit-object-detection"""						)

					__SCREAMING_SNAKE_CASE       :		int           =		object_detector(
					    """./tests/fixtures/tests_samples/COCO/000000039769.png"""							,     candidate_labels=["""cat""", """remote""", """couch"""]							,     threshold=0.64							,     )

					self.assertEqual(
					    nested_simplify(lowerCAmelCase__							,     decimals=4						)							,     [
					        {"""score""": 0.72_35, """label""": """cat""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
					        {"""score""": 0.72_18, """label""": """remote""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
					        {"""score""": 0.71_84, """label""": """couch""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
					        {"""score""": 0.67_48, """label""": """remote""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
					        {"""score""": 0.66_56, """label""": """cat""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
					        {"""score""": 0.66_14, """label""": """couch""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
					        {"""score""": 0.64_56, """label""": """remote""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
					        {"""score""": 0.6_42, """label""": """remote""", """box""": {"""xmin""": 6_7, """ymin""": 2_7_4, """xmax""": 9_3, """ymax""": 2_9_7}},
					        {"""score""": 0.64_19, """label""": """cat""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
					    ]							,     )

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		object_detector(
					    [
					        {
					            """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
					            """candidate_labels""": ["""cat""", """remote""", """couch"""],
					        }
					    ]							,     threshold=0.64							,     )

					self.assertEqual(
					    nested_simplify(lowerCAmelCase__							,     decimals=4						)							,     [
					        [
					            {"""score""": 0.72_35, """label""": """cat""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
					            {"""score""": 0.72_18, """label""": """remote""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
					            {"""score""": 0.71_84, """label""": """couch""", """box""": {"""xmin""": 2_0_4, """ymin""": 1_6_7, """xmax""": 2_3_2, """ymax""": 1_9_0}},
					            {"""score""": 0.67_48, """label""": """remote""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
					            {"""score""": 0.66_56, """label""": """cat""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
					            {"""score""": 0.66_14, """label""": """couch""", """box""": {"""xmin""": 5_7_1, """ymin""": 8_3, """xmax""": 5_9_8, """ymax""": 1_0_3}},
					            {"""score""": 0.64_56, """label""": """remote""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
					            {"""score""": 0.6_42, """label""": """remote""", """box""": {"""xmin""": 6_7, """ymin""": 2_7_4, """xmax""": 9_3, """ymax""": 2_9_7}},
					            {"""score""": 0.64_19, """label""": """cat""", """box""": {"""xmin""": 4_9_4, """ymin""": 1_0_5, """xmax""": 5_2_1, """ymax""": 1_2_7}},
					        ]
					    ]							,     )

			@require_torch
			@slow
			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		pipeline("""zero-shot-object-detection"""						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		object_detector(
					    """http://images.cocodataset.org/val2017/000000039769.jpg"""							,     candidate_labels=["""cat""", """remote""", """couch"""]							,     )
					self.assertEqual(
					    nested_simplify(lowerCAmelCase__							,     decimals=4						)							,     [
					        {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
					        {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
					        {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
					        {"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
					        {"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
					    ]							,     )

					__SCREAMING_SNAKE_CASE       :		Dict           =		object_detector(
					    [
					        {
					            """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
					            """candidate_labels""": ["""cat""", """remote""", """couch"""],
					        },
					        {
					            """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
					            """candidate_labels""": ["""cat""", """remote""", """couch"""],
					        },
					    ]							,     )
					self.assertEqual(
					    nested_simplify(lowerCAmelCase__							,     decimals=4						)							,     [
					        [
					            {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
					            {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
					            {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
					            {"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
					            {"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
					        ],
					        [
					            {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
					            {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
					            {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
					            {"""score""": 0.14_74, """label""": """remote""", """box""": {"""xmin""": 3_3_5, """ymin""": 7_4, """xmax""": 3_7_1, """ymax""": 1_8_7}},
					            {"""score""": 0.12_08, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 6_4_2, """ymax""": 4_7_6}},
					        ],
					    ]							,     )

			@require_tf
			@unittest.skip("""Zero Shot Object Detection not implemented in TF"""						)
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					pass

			@require_torch
			@slow
			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		0.2
					__SCREAMING_SNAKE_CASE       :		Dict           =		pipeline("""zero-shot-object-detection"""						)

					__SCREAMING_SNAKE_CASE       :		Any           =		object_detector(
					    """http://images.cocodataset.org/val2017/000000039769.jpg"""							,     candidate_labels=["""cat""", """remote""", """couch"""]							,     threshold=lowerCAmelCase__							,     )
					self.assertEqual(
					    nested_simplify(lowerCAmelCase__							,     decimals=4						)							,     [
					        {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
					        {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
					        {"""score""": 0.25_37, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 5_5, """xmax""": 3_1_5, """ymax""": 4_7_2}},
					    ]							,     )



			@require_torch
			@slow
			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		2
					__SCREAMING_SNAKE_CASE       :		int           =		pipeline("""zero-shot-object-detection"""						)

					__SCREAMING_SNAKE_CASE       :		int           =		object_detector(
					    """http://images.cocodataset.org/val2017/000000039769.jpg"""							,     candidate_labels=["""cat""", """remote""", """couch"""]							,     top_k=lowerCAmelCase__							,     )
					self.assertEqual(
					    nested_simplify(lowerCAmelCase__							,     decimals=4						)							,     [
					        {"""score""": 0.28_68, """label""": """cat""", """box""": {"""xmin""": 3_2_4, """ymin""": 2_0, """xmax""": 6_4_0, """ymax""": 3_7_3}},
					        {"""score""": 0.2_77, """label""": """remote""", """box""": {"""xmin""": 4_0, """ymin""": 7_2, """xmax""": 1_7_7, """ymax""": 1_1_5}},
					    ]							,     )