

import copy
import os
from typing import Union

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


a_      =     logging.get_logger(__name__)

a_      =     {
    '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 _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bridgetower_vision_model'
			def __init__(      self				:			Tuple   ,   a				:			Any=768   ,   a				:			Optional[Any]=12   ,   a				:			Dict=3   ,   a				:			Dict=16   ,   a				:			Optional[Any]=288   ,   a				:			List[Any]=1   ,   a				:			Tuple=1e-05   ,   a				:			Union[str, Any]=False   ,   a				:			Any=True   ,   a				:			Optional[int]=False   ,   **a				:			Optional[Any]   ,   )				->							Optional[int]:

				"""simple docstring"""

				super().__init__(**a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Dict					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Any					  =							num_channels
				SCREAMING_SNAKE_CASE :   int					  =							patch_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							image_size
				SCREAMING_SNAKE_CASE :   Dict					  =							initializer_factor
				SCREAMING_SNAKE_CASE :   Any					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							stop_gradient
				SCREAMING_SNAKE_CASE :   Tuple					  =							share_layernorm
				SCREAMING_SNAKE_CASE :   Any					  =							remove_last_layer






			@classmethod
			def 				__UpperCamelCase			(      cls				:			str   ,   a				:			Union[str, os.PathLike]   ,   **a				:			Dict						)				->							"PretrainedConfig":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							cls.get_config_dict(a   ,   **a						)

				if config_dict.get("model_type"						) == "bridgetower":
					SCREAMING_SNAKE_CASE :   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 _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bridgetower_text_model'
			def __init__(      self				:			str   ,   a				:			int=5_0265   ,   a				:			List[Any]=768   ,   a				:			int=12   ,   a				:			Optional[Any]=12   ,   a				:			Union[str, Any]=1   ,   a				:			str=3072   ,   a				:			List[Any]="gelu"   ,   a				:			Any=0.1   ,   a				:			List[str]=0.1   ,   a				:			Tuple=514   ,   a				:			Optional[int]=1   ,   a				:			Tuple=1e-05   ,   a				:			Union[str, Any]=1   ,   a				:			Any=0   ,   a				:			str=2   ,   a				:			str="absolute"   ,   a				:			Any=True   ,   **a				:			Dict   ,   )				->							Optional[int]:

				"""simple docstring"""

				super().__init__(**a						)

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






			@classmethod
			def 				__UpperCamelCase			(      cls				:			Tuple   ,   a				:			Union[str, os.PathLike]   ,   **a				:			List[str]						)				->							"PretrainedConfig":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							cls.get_config_dict(a   ,   **a						)

				if config_dict.get("model_type"						) == "bridgetower":
					SCREAMING_SNAKE_CASE :   str					  =							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 _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='bridgetower'
			def __init__(      self				:			List[str]   ,   a				:			List[Any]=True   ,   a				:			List[Any]="gelu"   ,   a				:			Union[str, Any]=768   ,   a				:			Tuple=1   ,   a				:			List[Any]=1e-05   ,   a				:			Dict=False   ,   a				:			Optional[int]="add"   ,   a				:			Any=12   ,   a				:			Optional[int]=6   ,   a				:			int=False   ,   a				:			Optional[int]=False   ,   a				:			Dict=None   ,   a				:			Tuple=None   ,   **a				:			Union[str, Any]   ,   )				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							kwargs.pop("text_config_dict"   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							kwargs.pop("vision_config_dict"   ,   a						)

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

				if text_config is None:
					SCREAMING_SNAKE_CASE :   Any					  =							{}
					logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."						)

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

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							BridgeTowerTextConfig(**a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							BridgeTowerVisionConfig(**a						)
			@classmethod
			def 				__UpperCamelCase			(      cls				:			List[Any]   ,   a				:			BridgeTowerTextConfig   ,   a				:			BridgeTowerVisionConfig   ,   **a				:			List[str]						)				->							Optional[int]:

				"""simple docstring"""

				return cls(text_config=text_config.to_dict()   ,   vision_config=vision_config.to_dict()   ,   **a						)






			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							copy.deepcopy(self.__dict__						)
				SCREAMING_SNAKE_CASE :   str					  =							self.text_config.to_dict()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.vision_config.to_dict()
				SCREAMING_SNAKE_CASE :   Dict					  =							self.__class__.model_type
				return output



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		vocab_size
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					List[str]   =		num_hidden_layers
      _lowerCamelCase	:					int   =		num_attention_heads
      _lowerCamelCase	:					Tuple   =		hidden_act
      _lowerCamelCase	:					str   =		intermediate_size
      _lowerCamelCase	:					Union[str, Any]   =		hidden_dropout_prob
      _lowerCamelCase	:					Tuple   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Any   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		type_vocab_size
      _lowerCamelCase	:					List[Any]   =		initializer_range
      _lowerCamelCase	:					Dict   =		layer_norm_eps
      _lowerCamelCase	:					List[Any]   =		position_embedding_type
      _lowerCamelCase	:					Any   =		quant_mode
      _lowerCamelCase	:					List[str]   =		force_dequant







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         _lowerCamelCase	:					Dict   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         _lowerCamelCase	:					Optional[int]   =		{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ])

