




'''simple docstring'''





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


UpperCamelCase_						:      Any       =						logging.get_logger(__name__)

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

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						"""table-transformer"""
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						["""past_key_values"""]
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }






     def __init__(			self ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=100 ,_SCREAMING_SNAKE_CASE=6 ,_SCREAMING_SNAKE_CASE=2_048 ,_SCREAMING_SNAKE_CASE=8 ,_SCREAMING_SNAKE_CASE=6 ,_SCREAMING_SNAKE_CASE=2_048 ,_SCREAMING_SNAKE_CASE=8 ,_SCREAMING_SNAKE_CASE=0.0 ,_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=0.0_2 ,_SCREAMING_SNAKE_CASE=1.0 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE="sine" ,_SCREAMING_SNAKE_CASE="resnet50" ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=False ,_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 ,)							->      int:
          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."   )
                    _snake_case							=				CONFIG_MAPPING["resnet"](out_features=["stage4"]   )
               elif isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
                    _snake_case							=				backbone_config.get("model_type"   )
                    _snake_case							=				CONFIG_MAPPING[backbone_model_type]
                    _snake_case							=				config_class.from_dict(_SCREAMING_SNAKE_CASE   )
               # set timm attributes to None
               _snake_case							, _snake_case							, _snake_case							=				None, None, None

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






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






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







class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						version.parse("""1.11"""							)






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






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






     @property
     def     _lowercase     (			self   )							->      int:
          return 12








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:      List[Any]       =						{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[Any]       =						['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						[
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Tuple       =						[
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      Tuple       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


