
import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}







class      A__							(     A__	):
				lowercase			    =  '''conditional_detr'''
				lowercase			    =  ['''past_key_values''']
				lowercase			    =  {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}






				def __init__(						self					:      str    ,     a					:      Tuple=True    ,     a					:      Optional[int]=None    ,     a					:      Tuple=3    ,     a					:      Any=300    ,     a					:      Tuple=6    ,     a					:      int=2_048    ,     a					:      Union[str, Any]=8    ,     a					:      int=6    ,     a					:      Dict=2_048    ,     a					:      Optional[int]=8    ,     a					:      Dict=0.0    ,     a					:      List[Any]=0.0    ,     a					:      int=True    ,     a					:      int="relu"    ,     a					:      Any=256    ,     a					:      int=0.1    ,     a					:      int=0.0    ,     a					:      Optional[int]=0.0    ,     a					:      List[str]=0.0_2    ,     a					:      Optional[int]=1.0    ,     a					:      List[str]=False    ,     a					:      int="sine"    ,     a					:      str="resnet50"    ,     a					:      int=True    ,     a					:      Tuple=False    ,     a					:      Tuple=2    ,     a					:      Tuple=5    ,     a					:      List[str]=2    ,     a					:      List[str]=1    ,     a					:      Optional[Any]=1    ,     a					:      str=2    ,     a					:      Any=5    ,     a					:      int=2    ,     a					:      List[Any]=0.2_5    ,     **a					:      Union[str, Any]    ,     ):






							'''simple docstring'''







							if backbone_config is not None and use_timm_backbone:
										raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'   )

							if not use_timm_backbone:
										if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'   )
													lowerCAmelCase__      :      Tuple         =		CONFIG_MAPPING["resnet"](out_features=['stage4']   )
										elif isinstance(a    ,     a   ):
													lowerCAmelCase__      :      Union[str, Any]         =		backbone_config.get('model_type'   )
													lowerCAmelCase__      :      Tuple         =		CONFIG_MAPPING[backbone_model_type]
													lowerCAmelCase__      :      Any         =		config_class.from_dict(a   )

							lowerCAmelCase__      :      Optional[Any]         =		use_timm_backbone
							lowerCAmelCase__      :      List[Any]         =		backbone_config
							lowerCAmelCase__      :      Tuple         =		num_channels
							lowerCAmelCase__      :      List[Any]         =		num_queries
							lowerCAmelCase__      :      Dict         =		d_model
							lowerCAmelCase__      :      Any         =		encoder_ffn_dim
							lowerCAmelCase__      :      List[str]         =		encoder_layers
							lowerCAmelCase__      :      List[str]         =		encoder_attention_heads
							lowerCAmelCase__      :      Optional[int]         =		decoder_ffn_dim
							lowerCAmelCase__      :      str         =		decoder_layers
							lowerCAmelCase__      :      Optional[Any]         =		decoder_attention_heads
							lowerCAmelCase__      :      int         =		dropout
							lowerCAmelCase__      :      Optional[int]         =		attention_dropout
							lowerCAmelCase__      :      Any         =		activation_dropout
							lowerCAmelCase__      :      int         =		activation_function
							lowerCAmelCase__      :      int         =		init_std
							lowerCAmelCase__      :      List[Any]         =		init_xavier_std
							lowerCAmelCase__      :      List[str]         =		encoder_layerdrop
							lowerCAmelCase__      :      List[Any]         =		decoder_layerdrop
							lowerCAmelCase__      :      List[Any]         =		encoder_layers
							lowerCAmelCase__      :      Optional[Any]         =		auxiliary_loss
							lowerCAmelCase__      :      List[str]         =		position_embedding_type
							lowerCAmelCase__      :      Optional[Any]         =		backbone
							lowerCAmelCase__      :      Optional[int]         =		use_pretrained_backbone
							lowerCAmelCase__      :      List[Any]         =		dilation
							# Hungarian matcher
							lowerCAmelCase__      :      List[str]         =		class_cost
							lowerCAmelCase__      :      Union[str, Any]         =		bbox_cost
							lowerCAmelCase__      :      int         =		giou_cost
							# Loss coefficients
							lowerCAmelCase__      :      str         =		mask_loss_coefficient
							lowerCAmelCase__      :      List[Any]         =		dice_loss_coefficient
							lowerCAmelCase__      :      int         =		cls_loss_coefficient
							lowerCAmelCase__      :      Tuple         =		bbox_loss_coefficient
							lowerCAmelCase__      :      Any         =		giou_loss_coefficient
							lowerCAmelCase__      :      List[Any]         =		focal_alpha
							super().__init__(is_encoder_decoder=a    ,     **a   )






				@property
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							return self.encoder_attention_heads






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							return self.d_model






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		copy.deepcopy(self.__dict__   )
							if self.backbone_config is not None:
										lowerCAmelCase__      :      List[str]         =		self.backbone_config.to_dict()
							lowerCAmelCase__      :      int         =		self.__class__.model_type
							return output







class      A__							(     A__	):
				lowercase			    =  version.parse('1.11'	)






				@property
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							        ('pixel_mask', {0: 'batch'}),
							    ]   )






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							return 1E-5






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return 12







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)