



"""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


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

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




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""table-transformer"""
 _lowerCAmelCase     :    Optional[Any]	      =	["""past_key_values"""]
 _lowerCAmelCase     :    List[Any]	      =	{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }

 def __init__(  self	: Any						,  lowercase_	: Any=True						,  lowercase_	: Optional[int]=None						,  lowercase_	: Dict=3						,  lowercase_	: Any=100						,  lowercase_	: int=6						,  lowercase_	: Union[str, Any]=2048						,  lowercase_	: Union[str, Any]=8						,  lowercase_	: Any=6						,  lowercase_	: List[str]=2048						,  lowercase_	: Tuple=8						,  lowercase_	: str=0.0						,  lowercase_	: str=0.0						,  lowercase_	: Any=True						,  lowercase_	: Union[str, Any]="relu"						,  lowercase_	: List[Any]=256						,  lowercase_	: Dict=0.1						,  lowercase_	: Optional[int]=0.0						,  lowercase_	: Union[str, Any]=0.0						,  lowercase_	: int=0.02						,  lowercase_	: Union[str, Any]=1.0						,  lowercase_	: Union[str, Any]=False						,  lowercase_	: Optional[Any]="sine"						,  lowercase_	: Union[str, Any]="resnet50"						,  lowercase_	: Tuple=True						,  lowercase_	: Any=False						,  lowercase_	: int=1						,  lowercase_	: Tuple=5						,  lowercase_	: int=2						,  lowercase_	: Optional[Any]=1						,  lowercase_	: Dict=1						,  lowercase_	: Optional[Any]=5						,  lowercase_	: int=2						,  lowercase_	: Any=0.1						,  **lowercase_	: List[str]						,  ):
     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_				:  Dict       =			CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
         elif isinstance(lowercase_						,  lowercase_       ):
             snake_case_				:  Optional[int]       =			backbone_config.get('''model_type'''       )
             snake_case_				:  Dict       =			CONFIG_MAPPING[backbone_model_type]
             snake_case_				:  List[str]       =			config_class.from_dict(lowercase_       )
         # set timm attributes to None
         snake_case_,			snake_case_,			snake_case_				:  List[Any]       =			None, None, None

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

 @property
 def 		_snake_case      (  self	: Dict       ):
     return self.encoder_attention_heads






 @property
 def 		_snake_case      (  self	: Optional[int]       ):
     return self.d_model





class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[str, Any]	      =	version.parse("""1.11""")

 @property
 def 		_snake_case      (  self	: Dict       ):
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ]       )

 @property
 def 		_snake_case      (  self	: Union[str, Any]       ):
     return 1E-5






 @property
 def 		_snake_case      (  self	: Dict       ):
     return 12










"""simple docstring"""

lowercase__					:						Union[str, Any]												=			{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}





