





'''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


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

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

class      A_			(	__snake_case	):

			'''simple docstring'''





			_lowerCAmelCase							=     "table-transformer"
			_lowerCAmelCase							=     ["past_key_values"]
			_lowerCAmelCase							=     {
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}




			def __init__(       self      ,  A_=True      ,  A_=None      ,  A_=3      ,  A_=1_00      ,  A_=6      ,  A_=20_48      ,  A_=8      ,  A_=6      ,  A_=20_48      ,  A_=8      ,  A_=0.0      ,  A_=0.0      ,  A_=True      ,  A_="relu"      ,  A_=2_56      ,  A_=0.1      ,  A_=0.0      ,  A_=0.0      ,  A_=0.02      ,  A_=1.0      ,  A_=False      ,  A_="sine"      ,  A_="resnet50"      ,  A_=True      ,  A_=False      ,  A_=1      ,  A_=5      ,  A_=2      ,  A_=1      ,  A_=1      ,  A_=5      ,  A_=2      ,  A_=0.1      ,  **A_      ,  ):
								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." )
																		_UpperCamelCase									= CONFIG_MAPPING['''resnet'''](out_features=["stage4"] )
													elif isinstance(A__      ,  A__ ):
																		_UpperCamelCase									= backbone_config.get("model_type" )
																		_UpperCamelCase									= CONFIG_MAPPING[backbone_model_type]
																		_UpperCamelCase									= config_class.from_dict(A__ )
													# set timm attributes to None
													_UpperCamelCase									= None, None, None

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




			@property
			def    a						(       self ):
								return self.encoder_attention_heads




			@property
			def    a						(       self ):
								return self.d_model




class      A_			(	__snake_case	):

			'''simple docstring'''





			_lowerCAmelCase							=     version.parse("""1.11"""	)




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




			@property
			def    a						(       self ):
								return 1e-5




			@property
			def    a						(       self ):
								return 12











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



