


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_      : List[str]  					=       logging.get_logger(__name__)

UpperCAmelCase_      : Optional[Any]  					=       {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     int        =						'''table-transformer'''
      snake_case__				:     Union[str, Any]        =						['''past_key_values''']
      snake_case__				:     Optional[Any]        =						{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
      }
      def __init__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       str=None     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=3     ,							SCREAMING_SNAKE_CASE__				:       str=1_0_0     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=6     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       List[str]=8     ,							SCREAMING_SNAKE_CASE__				:       Dict=6     ,							SCREAMING_SNAKE_CASE__				:       Tuple=2_0_4_8     ,							SCREAMING_SNAKE_CASE__				:       List[str]=8     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.0     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.0     ,							SCREAMING_SNAKE_CASE__				:       Any=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]="relu"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2_5_6     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0.0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0.0     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.02     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1.0     ,							SCREAMING_SNAKE_CASE__				:       Any=False     ,							SCREAMING_SNAKE_CASE__				:       List[Any]="sine"     ,							SCREAMING_SNAKE_CASE__				:       Dict="resnet50"     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Dict=1     ,							SCREAMING_SNAKE_CASE__				:       int=5     ,							SCREAMING_SNAKE_CASE__				:       Dict=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1     ,							SCREAMING_SNAKE_CASE__				:       int=1     ,							SCREAMING_SNAKE_CASE__				:       Any=5     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=0.1     ,							**SCREAMING_SNAKE_CASE__				:       int     ,							)		->			Tuple:
             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_					:		Dict							=  CONFIG_MAPPING['resnet'](out_features=['stage4']     )
                    elif isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                           a_					:		int							=  backbone_config.get('model_type'     )
                           a_					:		Any							=  CONFIG_MAPPING[backbone_model_type]
                           a_					:		Optional[int]							=  config_class.from_dict(SCREAMING_SNAKE_CASE__     )
                    # set timm attributes to None
                    a_							,		a_							,		a_					:		Optional[int]							=  None, None, None

             a_					:		Union[str, Any]							=  use_timm_backbone
             a_					:		str							=  backbone_config
             a_					:		str							=  num_channels
             a_					:		Tuple							=  num_queries
             a_					:		int							=  d_model
             a_					:		Optional[int]							=  encoder_ffn_dim
             a_					:		str							=  encoder_layers
             a_					:		Optional[Any]							=  encoder_attention_heads
             a_					:		Any							=  decoder_ffn_dim
             a_					:		int							=  decoder_layers
             a_					:		Dict							=  decoder_attention_heads
             a_					:		Union[str, Any]							=  dropout
             a_					:		Dict							=  attention_dropout
             a_					:		Dict							=  activation_dropout
             a_					:		Optional[Any]							=  activation_function
             a_					:		List[str]							=  init_std
             a_					:		Any							=  init_xavier_std
             a_					:		Union[str, Any]							=  encoder_layerdrop
             a_					:		Optional[int]							=  decoder_layerdrop
             a_					:		Union[str, Any]							=  encoder_layers
             a_					:		Dict							=  auxiliary_loss
             a_					:		str							=  position_embedding_type
             a_					:		Union[str, Any]							=  backbone
             a_					:		Any							=  use_pretrained_backbone
             a_					:		List[str]							=  dilation
             # Hungarian matcher
             a_					:		Tuple							=  class_cost
             a_					:		Optional[Any]							=  bbox_cost
             a_					:		Any							=  giou_cost
             # Loss coefficients
             a_					:		Optional[Any]							=  mask_loss_coefficient
             a_					:		Union[str, Any]							=  dice_loss_coefficient
             a_					:		Dict							=  bbox_loss_coefficient
             a_					:		List[Any]							=  giou_loss_coefficient
             a_					:		str							=  eos_coefficient
             super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             return self.encoder_attention_heads

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             return self.d_model
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     int        =						version.parse('''1.11'''			)
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[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        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			float:
             return 1E-5

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			int:
             return 1_2






from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : List[str]  					=       {
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Optional[int]        =						'''convbert'''
      def __init__(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=3_0_5_2_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       str=3_0_7_2     ,							SCREAMING_SNAKE_CASE__				:       Dict="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.02     ,							SCREAMING_SNAKE_CASE__				:       Any=1E-12     ,							SCREAMING_SNAKE_CASE__				:       int=1     ,							SCREAMING_SNAKE_CASE__				:       int=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=7_6_8     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=2     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=9     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							**SCREAMING_SNAKE_CASE__				:       List[Any]     ,							)		->			Any:
             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							bos_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)

             a_					:		Tuple							=  vocab_size
             a_					:		List[str]							=  hidden_size
             a_					:		List[str]							=  num_hidden_layers
             a_					:		Dict							=  num_attention_heads
             a_					:		Optional[int]							=  intermediate_size
             a_					:		int							=  hidden_act
             a_					:		Dict							=  hidden_dropout_prob
             a_					:		int							=  attention_probs_dropout_prob
             a_					:		str							=  max_position_embeddings
             a_					:		List[str]							=  type_vocab_size
             a_					:		List[str]							=  initializer_range
             a_					:		Tuple							=  layer_norm_eps
             a_					:		Optional[int]							=  embedding_size
             a_					:		List[Any]							=  head_ratio
             a_					:		List[Any]							=  conv_kernel_size
             a_					:		Tuple							=  num_groups
             a_					:		Tuple							=  classifier_dropout
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Mapping[str, Mapping[int, str]]:
             if self.task == "multiple-choice":
                    a_					:		Tuple							=  {0: 'batch', 1: 'choice', 2: 'sequence'}
             else:
                    a_					:		List[str]							=  {0: 'batch', 1: 'sequence'}
             return OrderedDict(
                 [
                     ('input_ids', dynamic_axis),
                     ('attention_mask', dynamic_axis),
                     ('token_type_ids', dynamic_axis),
                 ]     )


