



"""simple docstring"""
import copy

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


A:     Optional[Any]										=	logging.get_logger(__name__)

A:     Optional[int]										=	{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     int		=		'deta'
   __lowerCAmelCase						:     List[Any]		=		{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=900   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=6   ,  _SCREAMING_SNAKE_CASE=2048   ,  _SCREAMING_SNAKE_CASE=8   ,  _SCREAMING_SNAKE_CASE=6   ,  _SCREAMING_SNAKE_CASE=1024   ,  _SCREAMING_SNAKE_CASE=8   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE="relu"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.0   ,  _SCREAMING_SNAKE_CASE=0.02   ,  _SCREAMING_SNAKE_CASE=1.0   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE="sine"   ,  _SCREAMING_SNAKE_CASE=5   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=300   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=5   ,  _SCREAMING_SNAKE_CASE=2   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=1   ,  _SCREAMING_SNAKE_CASE=5   ,  _SCREAMING_SNAKE_CASE=2   ,  _SCREAMING_SNAKE_CASE=0.1   ,  _SCREAMING_SNAKE_CASE=0.25   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Any:





     '''simple docstring'''




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

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

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





     '''simple docstring'''




     return self.encoder_attention_heads

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





     '''simple docstring'''




     return self.d_model




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





     '''simple docstring'''




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





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,	[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]		):
 UpperCAmelCase			:      List[Any]			=			tmp_path_factory.mktemp("""dset_infos_dir"""		)
 if "full:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""		)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write(""""""		)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / """dataset_infos.json"""    ,	"""w"""		) as f:
   f.write("""{\"default\": {\"dataset_size\": 42}}"""		)
 UpperCAmelCase			:      str			=			DatasetInfosDict.from_directory(UpperCamelCase		)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,	[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	DatasetInfo		):
 UpperCAmelCase			:      List[Any]			=			str(UpperCamelCase		)
 dataset_info.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			DatasetInfo.from_directory(UpperCamelCase		)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(UpperCamelCase    ,	"""dataset_info.json"""		)		)

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo(
     description="""foo"""    ,	citation="""bar"""    ,	homepage="""https://foo.bar"""    ,	license="""CC0"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	post_processed={}    ,	supervised_keys=()    ,	task_templates=[]    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train""", """num_examples""": 42}]    ,	download_checksums={}    ,	download_size=1337    ,	post_processing_size=442    ,	dataset_size=1234    ,	size_in_bytes=1337 + 442 + 1234    ,	)
 UpperCAmelCase			:      str			=			dataset_info._to_yaml_dict()
 assert sorted(UpperCamelCase		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]    ,	(list, dict, int, str)		)
 UpperCAmelCase			:      Union[str, Any]			=			yaml.safe_dump(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			yaml.safe_load(UpperCamelCase		)
 assert dataset_info_yaml_dict == reloaded

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo()
 UpperCAmelCase			:      Union[str, Any]			=			dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,	[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}		),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}		),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	)
            }		),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42		),
                """v2""": DatasetInfo(dataset_size=1337		),
            }		),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	DatasetInfosDict		):
 UpperCAmelCase			:      List[str]			=			str(UpperCamelCase		)
 dataset_infos_dict.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			DatasetInfosDict.from_directory(UpperCamelCase		)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  UpperCAmelCase			:      Union[str, Any]			=			config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  UpperCAmelCase			:      Optional[Any]			=			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(UpperCamelCase    ,	"""README.md"""		)		)
