
"""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


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			str					=			'''levit'''


						def __init__(       self   :					Any     ,A_   :					Union[str, Any]=224     ,A_   :					Dict=3     ,A_   :					Tuple=3     ,A_   :					str=2     ,A_   :					int=1     ,A_   :					int=16     ,A_   :					Tuple=[128, 256, 384]     ,A_   :					Any=[4, 8, 12]     ,A_   :					int=[4, 4, 4]     ,A_   :					Optional[Any]=[16, 16, 16]     ,A_   :					Union[str, Any]=0     ,A_   :					List[Any]=[2, 2, 2]     ,A_   :					Tuple=[2, 2, 2]     ,A_   :					str=0.02     ,**A_   :					Tuple     ,)   ->      Dict:
							super().__init__(**A_					)
							A        =      image_size
							A        =      num_channels
							A        =      kernel_size
							A        =      stride
							A        =      padding
							A        =      hidden_sizes
							A        =      num_attention_heads
							A        =      depths
							A        =      key_dim
							A        =      drop_path_rate
							A        =      patch_size
							A        =      attention_ratio
							A        =      mlp_ratio
							A        =      initializer_range
							A        =      [
							    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Union[str, Any]					=			version.parse('''1.11'''			)


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




						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      float:
							return 1e-4






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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

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

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			"table-transformer"
     __snake_case    :	Union[str, Any]			=			["past_key_values"]
     __snake_case    :	List[Any]			=			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    Optional[Any]=None						,lowerCamelCase__						:    List[Any]=3						,lowerCamelCase__						:    Optional[int]=100						,lowerCamelCase__						:    List[Any]=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    List[Any]=8						,lowerCamelCase__						:    Dict=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    Any=8						,lowerCamelCase__						:    Optional[int]=0.0						,lowerCamelCase__						:    int=0.0						,lowerCamelCase__						:    List[Any]=True						,lowerCamelCase__						:    Optional[int]="relu"						,lowerCamelCase__						:    Tuple=256						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    Optional[Any]=0.0						,lowerCamelCase__						:    Tuple=0.0						,lowerCamelCase__						:    List[Any]=0.02						,lowerCamelCase__						:    int=1.0						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    Optional[Any]="sine"						,lowerCamelCase__						:    List[str]="resnet50"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    int=1						,lowerCamelCase__						:    Dict=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    Union[str, Any]=1						,lowerCamelCase__						:    str=1						,lowerCamelCase__						:    Any=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    str=0.1						,**lowerCamelCase__						:    List[str]						,)     ->		Optional[int]:




           '''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."""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__       )
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						None, None, None

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






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




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			version.parse("1.11"   )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


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






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		int:




           '''simple docstring'''


           return 12


