




"""simple docstring"""






import copy
import os
from typing import Union

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """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 lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """bridgetower_vision_model"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=3						,   lowercase=16						,   lowercase=288						,   lowercase=1						,   lowercase=1E-05						,   lowercase=False						,   lowercase=True						,   lowercase=False						,   **lowercase						,   ):
							super().__init__(**lowercase						)
							_lowerCamelCase  :    Any					=					hidden_size
							_lowerCamelCase  :    Optional[int]					=					num_hidden_layers
							_lowerCamelCase  :    Optional[Any]					=					num_channels
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    str					=					image_size
							_lowerCamelCase  :    int					=					initializer_factor
							_lowerCamelCase  :    List[Any]					=					layer_norm_eps
							_lowerCamelCase  :    List[Any]					=					stop_gradient
							_lowerCamelCase  :    Any					=					share_layernorm
							_lowerCamelCase  :    Union[str, Any]					=					remove_last_layer






			@classmethod
			def       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase,				_lowerCamelCase  :    Dict					=					cls.get_config_dict(lowercase						,   **lowercase						)

							if config_dict.get('model_type'						) == "bridgetower":
											_lowerCamelCase  :    List[Any]					=					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(lowercase						,   **lowercase						)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """bridgetower_text_model"""
			def __init__(						self						,   lowercase=50265						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=1						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=514						,   lowercase=1						,   lowercase=1E-05						,   lowercase=1						,   lowercase=0						,   lowercase=2						,   lowercase="absolute"						,   lowercase=True						,   **lowercase						,   ):
							super().__init__(**lowercase						)

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






			@classmethod
			def       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase,				_lowerCamelCase  :    Dict					=					cls.get_config_dict(lowercase						,   **lowercase						)

							if config_dict.get('model_type'						) == "bridgetower":
											_lowerCamelCase  :    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(lowercase						,   **lowercase						)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """bridgetower"""
			def __init__(						self						,   lowercase=True						,   lowercase="gelu"						,   lowercase=768						,   lowercase=1						,   lowercase=1E-05						,   lowercase=False						,   lowercase="add"						,   lowercase=12						,   lowercase=6						,   lowercase=False						,   lowercase=False						,   lowercase=None						,   lowercase=None						,   **lowercase						,   ):
							# TODO: remove this once the Hub files are updated.
							_lowerCamelCase  :    Optional[Any]					=					kwargs.pop('text_config_dict'						,   lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					kwargs.pop('vision_config_dict'						,   lowercase						)

							super().__init__(**lowercase						)
							_lowerCamelCase  :    List[str]					=					share_cross_modal_transformer_layers
							_lowerCamelCase  :    Optional[int]					=					hidden_act
							_lowerCamelCase  :    Union[str, Any]					=					hidden_size
							_lowerCamelCase  :    Any					=					initializer_factor
							_lowerCamelCase  :    Tuple					=					layer_norm_eps
							_lowerCamelCase  :    str					=					share_link_tower_layers
							_lowerCamelCase  :    Tuple					=					link_tower_type
							_lowerCamelCase  :    Optional[int]					=					num_attention_heads
							_lowerCamelCase  :    str					=					num_hidden_layers
							_lowerCamelCase  :    Optional[int]					=					tie_word_embeddings
							_lowerCamelCase  :    Tuple					=					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  :    Tuple					=					{}
											logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'						)

							_lowerCamelCase  :    Dict					=					BridgeTowerTextConfig(**lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					BridgeTowerVisionConfig(**lowercase						)
			@classmethod
			def       A_					(						cls						,   lowercase						,   lowercase						,   **lowercase						):
							return cls(text_config=text_config.to_dict()						,   vision_config=vision_config.to_dict()						,   **lowercase						)






			def       A_					(						self						):
							_lowerCamelCase  :    Any					=					copy.deepcopy(self.__dict__						)
							_lowerCamelCase  :    Dict					=					self.text_config.to_dict()
							_lowerCamelCase  :    List[Any]					=					self.vision_config.to_dict()
							_lowerCamelCase  :    Optional[int]					=					self.__class__.model_type
							return output





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				return "".join(chr(ord(lowercase__	) - 32	) if 'a' <= char <= 'z' else char for char in word	)


if __name__ == "__main__":
			from doctest import testmod

			testmod()