




"""simple docstring"""







import copy
import os
from typing import Union

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}




class 				A__ (			__lowercase):
		A_		: List[Any]            =			'bridgetower_vision_model'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2_88 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1E-05 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**__A				)
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		str		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   num_channels
			__lowerCAmelCase    :		int		    =   patch_size
			__lowerCAmelCase    :		str		    =   image_size
			__lowerCAmelCase    :		List[str]		    =   initializer_factor
			__lowerCAmelCase    :		Tuple		    =   layer_norm_eps
			__lowerCAmelCase    :		Union[str, Any]		    =   stop_gradient
			__lowerCAmelCase    :		Union[str, Any]		    =   share_layernorm
			__lowerCAmelCase    :		List[Any]		    =   remove_last_layer







		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   cls.get_config_dict(__A ,	**__A				)

			if config_dict.get('model_type'				) == "bridgetower":
				__lowerCAmelCase    :		int		    =   config_dict['text_config']

			if "model_type" in config_dict and hasattr(cls ,	'model_type'				) and config_dict["model_type"] != cls.model_type:
				logger.warning(
				    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
				    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

			return cls.from_dict(__A ,	**__A				)




class 				A__ (			__lowercase):
		A_		: Optional[int]            =			'bridgetower_text_model'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=5_02_65 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_14 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1E-05 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE="absolute" ,	_SCREAMING_SNAKE_CASE=True ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**__A				)

			__lowerCAmelCase    :		List[Any]		    =   vocab_size
			__lowerCAmelCase    :		Dict		    =   hidden_size
			__lowerCAmelCase    :		Union[str, Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		Dict		    =   num_attention_heads
			__lowerCAmelCase    :		Any		    =   hidden_act
			__lowerCAmelCase    :		Optional[int]		    =   initializer_factor
			__lowerCAmelCase    :		int		    =   intermediate_size
			__lowerCAmelCase    :		Tuple		    =   hidden_dropout_prob
			__lowerCAmelCase    :		List[Any]		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		int		    =   max_position_embeddings
			__lowerCAmelCase    :		List[Any]		    =   type_vocab_size
			__lowerCAmelCase    :		Any		    =   layer_norm_eps
			__lowerCAmelCase    :		Dict		    =   position_embedding_type
			__lowerCAmelCase    :		Tuple		    =   use_cache
			__lowerCAmelCase    :		int		    =   pad_token_id
			__lowerCAmelCase    :		Optional[Any]		    =   bos_token_id
			__lowerCAmelCase    :		Tuple		    =   eos_token_id







		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Tuple		    =   cls.get_config_dict(__A ,	**__A				)

			if config_dict.get('model_type'				) == "bridgetower":
				__lowerCAmelCase    :		Dict		    =   config_dict['text_config']

			if "model_type" in config_dict and hasattr(cls ,	'model_type'				) and config_dict["model_type"] != cls.model_type:
				logger.warning(
				    f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
				    f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

			return cls.from_dict(__A ,	**__A				)




class 				A__ (			__lowercase):
		A_		: Dict            =			'bridgetower'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1E-05 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="add" ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE ,	):
			# TODO: remove this once the Hub files are updated.
			__lowerCAmelCase    :		str		    =   kwargs.pop('text_config_dict' ,	__A				)
			__lowerCAmelCase    :		Tuple		    =   kwargs.pop('vision_config_dict' ,	__A				)

			super().__init__(**__A				)
			__lowerCAmelCase    :		Dict		    =   share_cross_modal_transformer_layers
			__lowerCAmelCase    :		str		    =   hidden_act
			__lowerCAmelCase    :		str		    =   hidden_size
			__lowerCAmelCase    :		Optional[int]		    =   initializer_factor
			__lowerCAmelCase    :		str		    =   layer_norm_eps
			__lowerCAmelCase    :		int		    =   share_link_tower_layers
			__lowerCAmelCase    :		Tuple		    =   link_tower_type
			__lowerCAmelCase    :		int		    =   num_attention_heads
			__lowerCAmelCase    :		Any		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   tie_word_embeddings
			__lowerCAmelCase    :		Any		    =   init_layernorm_from_vision_encoder

			if text_config is None:
				__lowerCAmelCase    :		Tuple		    =   {}
				logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'				)

			if vision_config is None:
				__lowerCAmelCase    :		Optional[Any]		    =   {}
				logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'				)

			__lowerCAmelCase    :		Optional[Any]		    =   BridgeTowerTextConfig(**__A				)
			__lowerCAmelCase    :		Tuple		    =   BridgeTowerVisionConfig(**__A				)

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return cls(text_config=text_config.to_dict() ,	vision_config=vision_config.to_dict() ,	**__A				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   copy.deepcopy(self.__dict__				)
			__lowerCAmelCase    :		List[Any]		    =   self.text_config.to_dict()
			__lowerCAmelCase    :		List[Any]		    =   self.vision_config.to_dict()
			__lowerCAmelCase    :		Dict		    =   self.__class__.model_type
			return output

"""simple docstring"""







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'detr'
		A_		: Union[str, Any]            =			['past_key_values']
		A_		: 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=1_00 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_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=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_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 ,	):
			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.'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING['resnet'](out_features=['stage4']				)
				elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		str		    =   backbone_config.get('model_type'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING[backbone_model_type]
					__lowerCAmelCase    :		str		    =   config_class.from_dict(_SCREAMING_SNAKE_CASE				)
				# set timm attributes to None
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   None, None, None

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

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

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

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return cls(backbone_config=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   copy.deepcopy(self.__dict__				)
			if output["backbone_config"] is not None:
				__lowerCAmelCase    :		Dict		    =   self.backbone_config.to_dict()
			__lowerCAmelCase    :		List[str]		    =   self.__class__.model_type
			return output




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

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

		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-5







		@property
		def 							__lowerCamelCase							(   self				):
			return 12