






"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"blip_2_vision_model"

					def __init__(			self  :      Dict     ,		__snake_case  :      Any=1_4_0_8     ,		__snake_case  :      Union[str, Any]=6_1_4_4     ,		__snake_case  :      List[str]=3_9     ,		__snake_case  :      int=1_6     ,		__snake_case  :      Tuple=2_2_4     ,		__snake_case  :      Any=1_4     ,		__snake_case  :      List[Any]="gelu"     ,		__snake_case  :      Optional[Any]=0.00001     ,		__snake_case  :      Union[str, Any]=0.0     ,		__snake_case  :      Dict=1E-10     ,		__snake_case  :      List[str]=True     ,		**__snake_case  :      Optional[int]     ,		)			->							Tuple:
											super().__init__(**__snake_case					)

											__magic_name__:	Any							=       hidden_size
											__magic_name__:	Any							=       intermediate_size
											__magic_name__:	Union[str, Any]							=       num_hidden_layers
											__magic_name__:	List[Any]							=       num_attention_heads
											__magic_name__:	Any							=       patch_size
											__magic_name__:	int							=       image_size
											__magic_name__:	Tuple							=       initializer_range
											__magic_name__:	List[Any]							=       attention_dropout
											__magic_name__:	Union[str, Any]							=       layer_norm_eps
											__magic_name__:	Dict							=       hidden_act
											__magic_name__:	str							=       qkv_bias






					@classmethod
					def   lowerCamelCase__   (			cls  :      List[str]     ,		__snake_case  :      Union[str, os.PathLike]     ,		**__snake_case  :      Optional[Any]					)			->							"PretrainedConfig":
											cls._set_token_in_kwargs(__snake_case					)

											__magic_name__,			__magic_name__:	Union[str, Any]							=       cls.get_config_dict(__snake_case     ,		**__snake_case					)

											# get the vision config dict if we are loading from Blip2Config
											if config_dict.get("""model_type"""					) == "blip-2":
																	__magic_name__:	int							=       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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"blip_2_qformer"

					def __init__(			self  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=3_0_5_2_2     ,		__snake_case  :      List[Any]=7_6_8     ,		__snake_case  :      int=1_2     ,		__snake_case  :      str=1_2     ,		__snake_case  :      Union[str, Any]=3_0_7_2     ,		__snake_case  :      Union[str, Any]="gelu"     ,		__snake_case  :      Any=0.1     ,		__snake_case  :      Optional[Any]=0.1     ,		__snake_case  :      List[Any]=5_1_2     ,		__snake_case  :      str=0.02     ,		__snake_case  :      Any=1E-12     ,		__snake_case  :      Tuple=0     ,		__snake_case  :      Tuple="absolute"     ,		__snake_case  :      Dict=2     ,		__snake_case  :      List[Any]=1_4_0_8     ,		**__snake_case  :      Tuple     ,		)			->							Dict:
											super().__init__(pad_token_id=__snake_case     ,		**__snake_case					)

											__magic_name__:	Optional[Any]							=       vocab_size
											__magic_name__:	Optional[int]							=       hidden_size
											__magic_name__:	Dict							=       num_hidden_layers
											__magic_name__:	str							=       num_attention_heads
											__magic_name__:	List[str]							=       hidden_act
											__magic_name__:	Any							=       intermediate_size
											__magic_name__:	int							=       hidden_dropout_prob
											__magic_name__:	str							=       attention_probs_dropout_prob
											__magic_name__:	int							=       max_position_embeddings
											__magic_name__:	List[str]							=       initializer_range
											__magic_name__:	Optional[int]							=       layer_norm_eps
											__magic_name__:	Dict							=       position_embedding_type
											__magic_name__:	List[Any]							=       cross_attention_frequency
											__magic_name__:	Tuple							=       encoder_hidden_size






					@classmethod
					def   lowerCamelCase__   (			cls  :      Tuple     ,		__snake_case  :      Union[str, os.PathLike]     ,		**__snake_case  :      Optional[int]					)			->							"PretrainedConfig":
											cls._set_token_in_kwargs(__snake_case					)

											__magic_name__,			__magic_name__:	Optional[Any]							=       cls.get_config_dict(__snake_case     ,		**__snake_case					)

											# get the qformer config dict if we are loading from Blip2Config
											if config_dict.get("""model_type"""					) == "blip-2":
																	__magic_name__:	List[str]							=       config_dict["""qformer_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       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"blip-2"
					UpperCAmelCase__								=				True

					def __init__(			self  :      str     ,		__snake_case  :      Dict=None     ,		__snake_case  :      int=None     ,		__snake_case  :      Tuple=None     ,		__snake_case  :      int=3_2     ,		**__snake_case  :      Tuple					)			->							List[Any]:
											super().__init__(**__snake_case					)

											if vision_config is None:
																	__magic_name__:	int							=       {}
																	logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""					)

											if qformer_config is None:
																	__magic_name__:	str							=       {}
																	logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""					)

											if text_config is None:
																	__magic_name__:	Optional[Any]							=       {}
																	logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""					)

											__magic_name__:	Dict							=       BlipaVisionConfig(**__snake_case					)
											__magic_name__:	Optional[int]							=       BlipaQFormerConfig(**__snake_case					)
											__magic_name__:	str							=       text_config["""model_type"""] if """model_type""" in text_config else """opt"""
											__magic_name__:	Union[str, Any]							=       CONFIG_MAPPING[text_model_type](**__snake_case					)

											__magic_name__:	Tuple							=       self.text_config.tie_word_embeddings
											__magic_name__:	Optional[int]							=       self.text_config.is_encoder_decoder

											__magic_name__:	Optional[Any]							=       num_query_tokens
											__magic_name__:	int							=       self.vision_config.hidden_size
											__magic_name__:	List[Any]							=       self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
											__magic_name__:	Tuple							=       1.0
											__magic_name__:	Any							=       0.02

					@classmethod
					def   lowerCamelCase__   (			cls  :      List[str]     ,		__snake_case  :      BlipaVisionConfig     ,		__snake_case  :      BlipaQFormerConfig     ,		__snake_case  :      PretrainedConfig     ,		**__snake_case  :      Optional[int]     ,		)			->							str:
											return cls(
											    vision_config=vision_config.to_dict()     ,		qformer_config=qformer_config.to_dict()     ,		text_config=text_config.to_dict()     ,		**__snake_case     ,		)






					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[int]:
											__magic_name__:	Optional[int]							=       copy.deepcopy(self.__dict__					)
											__magic_name__:	List[Any]							=       self.vision_config.to_dict()
											__magic_name__:	int							=       self.qformer_config.to_dict()
											__magic_name__:	Dict							=       self.text_config.to_dict()
											__magic_name__:	Optional[int]							=       self.__class__.model_type
											return output











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int = 1_0_0  )    ->							int:
						__magic_name__:	str							=       0
						__magic_name__:	Any							=       0
						for i in range(1      ,   n + 1  ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
							print(f'''{solution() = }''')



