



"""simple docstring"""






import copy

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


__snake_case							:   List[Any]      =    logging.get_logger(__name__)

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



class 			A__   (  lowerCAmelCase_							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	"""deta"""
	SCREAMING_SNAKE_CASE          =	{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}

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

				"""simple docstring"""

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

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

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				return self.encoder_attention_heads

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[int]:

				"""simple docstring"""

				return self.d_model







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[Any]:

				"""simple docstring"""

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






'''simple docstring'''




import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    "kwargs, expected"			,     [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10       )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(__lowercase			,     i + 1       ) for i in range(10       )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1       )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0			,     4       ), range(4			,     7       ), range(7			,     10       )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0			,     1       ), range(1			,     2       ), range(2			,     3       )]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   _distribute_shards(**__lowercase       )
     assert out == expected
@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"			,     [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Optional[Any]			,     __lowercase							:      int       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   _split_gen_kwargs(__lowercase			,     __lowercase       )
     assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"			,     [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]       )		->       List[Any]:


     '''simple docstring'''



     if expected is RuntimeError:
          with pytest.raises(__lowercase       ):
               _number_of_shards_in_gen_kwargs(__lowercase       )
     else:
          _UpperCAmelCase     							=   _number_of_shards_in_gen_kwargs(__lowercase       )
          assert out == expected




