





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


snake_case__					:							Optional[int]													=     logging.get_logger(__name__)

snake_case__					:							Union[str, Any]													=     {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """table-transformer"""
   lowerCAmelCase__        = ["""past_key_values"""]
   lowerCAmelCase__        = {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }




   def __init__(self	:int    ,   _UpperCamelCase	:Tuple=True    ,   _UpperCamelCase	:int=None    ,   _UpperCamelCase	:Tuple=3    ,   _UpperCamelCase	:int=100    ,   _UpperCamelCase	:int=6    ,   _UpperCamelCase	:List[str]=2048    ,   _UpperCamelCase	:Tuple=8    ,   _UpperCamelCase	:int=6    ,   _UpperCamelCase	:Dict=2048    ,   _UpperCamelCase	:List[str]=8    ,   _UpperCamelCase	:str=0.0    ,   _UpperCamelCase	:Optional[int]=0.0    ,   _UpperCamelCase	:Union[str, Any]=True    ,   _UpperCamelCase	:Dict="relu"    ,   _UpperCamelCase	:Dict=256    ,   _UpperCamelCase	:Optional[Any]=0.1    ,   _UpperCamelCase	:Union[str, Any]=0.0    ,   _UpperCamelCase	:int=0.0    ,   _UpperCamelCase	:List[Any]=0.0_2    ,   _UpperCamelCase	:Optional[Any]=1.0    ,   _UpperCamelCase	:Any=False    ,   _UpperCamelCase	:Tuple="sine"    ,   _UpperCamelCase	:Optional[int]="resnet50"    ,   _UpperCamelCase	:Optional[Any]=True    ,   _UpperCamelCase	:Optional[int]=False    ,   _UpperCamelCase	:Dict=1    ,   _UpperCamelCase	:Any=5    ,   _UpperCamelCase	:Optional[Any]=2    ,   _UpperCamelCase	:Dict=1    ,   _UpperCamelCase	:Tuple=1    ,   _UpperCamelCase	:int=5    ,   _UpperCamelCase	:Optional[int]=2    ,   _UpperCamelCase	:List[str]=0.1    ,   **_UpperCamelCase	:Union[str, Any]    ,   )->					Any:
         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.''' )
                     __A          =				CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
               elif isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
                     __A          =				backbone_config.get('''model_type''' )
                     __A          =				CONFIG_MAPPING[backbone_model_type]
                     __A          =				config_class.from_dict(_UpperCamelCase )
               # set timm attributes to None
               __A   ,						__A   ,						__A          =				None, None, None

         __A          =				use_timm_backbone
         __A          =				backbone_config
         __A          =				num_channels
         __A          =				num_queries
         __A          =				d_model
         __A          =				encoder_ffn_dim
         __A          =				encoder_layers
         __A          =				encoder_attention_heads
         __A          =				decoder_ffn_dim
         __A          =				decoder_layers
         __A          =				decoder_attention_heads
         __A          =				dropout
         __A          =				attention_dropout
         __A          =				activation_dropout
         __A          =				activation_function
         __A          =				init_std
         __A          =				init_xavier_std
         __A          =				encoder_layerdrop
         __A          =				decoder_layerdrop
         __A          =				encoder_layers
         __A          =				auxiliary_loss
         __A          =				position_embedding_type
         __A          =				backbone
         __A          =				use_pretrained_backbone
         __A          =				dilation
         # Hungarian matcher
         __A          =				class_cost
         __A          =				bbox_cost
         __A          =				giou_cost
         # Loss coefficients
         __A          =				mask_loss_coefficient
         __A          =				dice_loss_coefficient
         __A          =				bbox_loss_coefficient
         __A          =				giou_loss_coefficient
         __A          =				eos_coefficient
         super().__init__(is_encoder_decoder=_UpperCamelCase    ,   **_UpperCamelCase )




   @property
   def _lowerCAmelCase      (self	:str )->					int:
         return self.encoder_attention_heads






   @property
   def _lowerCAmelCase      (self	:Dict )->					int:
         return self.d_model





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = version.parse("""1.11"""   )




   @property
   def _lowerCAmelCase      (self	:List[Any] )->					Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ] )




   @property
   def _lowerCAmelCase      (self	:int )->					float:
         return 1e-5






   @property
   def _lowerCAmelCase      (self	:Union[str, Any] )->					int:
         return 12












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:							Optional[Any]													=     {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__					:							List[str]													=     [
           'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'IBertForMaskedLM',
           'IBertForMultipleChoice',
           'IBertForQuestionAnswering',
           'IBertForSequenceClassification',
           'IBertForTokenClassification',
           'IBertModel',
           'IBertPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ibert import (
                  IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  IBertForMaskedLM,
                  IBertForMultipleChoice,
                  IBertForQuestionAnswering,
                  IBertForSequenceClassification,
                  IBertForTokenClassification,
                  IBertModel,
                  IBertPreTrainedModel,
              )

else:
       import sys

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





