




"""simple docstring"""



import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''git_vision_model'''




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Dict=3_072     ,			snake_case_						:	List[str]=12     ,			snake_case_						:	List[Any]=12     ,			snake_case_						:	Union[str, Any]=3     ,			snake_case_						:	Union[str, Any]=224     ,			snake_case_						:	Union[str, Any]=16     ,			snake_case_						:	Any="quick_gelu"     ,			snake_case_						:	Optional[Any]=1e-5     ,			snake_case_						:	List[str]=0.0     ,			snake_case_						:	List[Any]=0.02     ,			**snake_case_						:	int     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     super().__init__(**snake_case_				)

     A__	=    hidden_size
     A__	=    intermediate_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    num_channels
     A__	=    patch_size
     A__	=    image_size
     A__	=    initializer_range
     A__	=    attention_dropout
     A__	=    layer_norm_eps
     A__	=    hidden_act




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




     '''simple docstring'''

     cls._set_token_in_kwargs(snake_case_				)

     A__,				A__	=    cls.get_config_dict(snake_case_     ,			**snake_case_				)

     # get the vision config dict if we are loading from GITConfig
     if config_dict.get("model_type"				) == "git":
      A__	=    config_dict["vision_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(snake_case_     ,			**snake_case_				)






class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''git'''




    def __init__(   self						:	Any     ,			snake_case_						:	List[str]=None     ,			snake_case_						:	Optional[int]=30_522     ,			snake_case_						:	Optional[int]=768     ,			snake_case_						:	Optional[Any]=6     ,			snake_case_						:	Optional[Any]=12     ,			snake_case_						:	Union[str, Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Any=0.1     ,			snake_case_						:	Dict=1_024     ,			snake_case_						:	Optional[Any]=0.02     ,			snake_case_						:	Dict=1e-12     ,			snake_case_						:	Optional[Any]=0     ,			snake_case_						:	List[Any]="absolute"     ,			snake_case_						:	int=True     ,			snake_case_						:	int=False     ,			snake_case_						:	int=101     ,			snake_case_						:	Union[str, Any]=102     ,			snake_case_						:	List[Any]=None     ,			**snake_case_						:	str     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     super().__init__(bos_token_id=snake_case_     ,			eos_token_id=snake_case_     ,			pad_token_id=snake_case_     ,			**snake_case_				)

     if vision_config is None:
      A__	=    {}
      logger.info("vision_config is None. initializing the GitVisionConfig with default values."				)

     A__	=    GitVisionConfig(**snake_case_				)
     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    use_cache
     A__	=    tie_word_embeddings
     A__	=    num_image_with_embedding

     A__	=    bos_token_id
     A__	=    eos_token_id




    def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




     '''simple docstring'''

     A__	=    copy.deepcopy(self.__dict__				)
     A__	=    self.vision_config.to_dict()
     A__	=    self.__class__.model_type
     return output











"""simple docstring"""



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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





