
"""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


_a														=		logging.get_logger(__name__)

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






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'blip_2_vision_model'



		def __init__( self    ,		__a=14_08    ,		__a=61_44    ,		__a=39    ,		__a=16    ,		__a=2_24    ,		__a=14    ,		__a="gelu"    ,		__a=0.0_0001    ,		__a=0.0    ,		__a=1e-10    ,		__a=True    ,		**__a    ,		)	->	str:
							super().__init__(**__a)

							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  attention_dropout
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  qkv_bias




		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	"PretrainedConfig":
							cls._set_token_in_kwargs(__a)

							_UpperCamelCase			,  _UpperCamelCase										=  cls.get_config_dict(__a    ,		**__a)

							# get the vision config dict if we are loading from Blip2Config
							if config_dict.get('''model_type''') == "blip-2":
												_UpperCamelCase										=  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(__a    ,		**__a)






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'blip_2_qformer'



		def __init__( self    ,		__a=3_05_22    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=0.02    ,		__a=1e-12    ,		__a=0    ,		__a="absolute"    ,		__a=2    ,		__a=14_08    ,		**__a    ,		)	->	Optional[Any]:
							super().__init__(pad_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  cross_attention_frequency
							_UpperCamelCase										=  encoder_hidden_size




		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	"PretrainedConfig":
							cls._set_token_in_kwargs(__a)

							_UpperCamelCase			,  _UpperCamelCase										=  cls.get_config_dict(__a    ,		**__a)

							# get the qformer config dict if we are loading from Blip2Config
							if config_dict.get('''model_type''') == "blip-2":
												_UpperCamelCase										=  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(__a    ,		**__a)






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'blip-2'
		lowercase__      =				True



		def __init__( self    ,		__a=None    ,		__a=None    ,		__a=None    ,		__a=32    ,		**__a)	->	Any:
							super().__init__(**__a)

							if vision_config is None:
												_UpperCamelCase										=  {}
												logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''')

							if qformer_config is None:
												_UpperCamelCase										=  {}
												logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''')

							if text_config is None:
												_UpperCamelCase										=  {}
												logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''')

							_UpperCamelCase										=  BlipaVisionConfig(**__a)
							_UpperCamelCase										=  BlipaQFormerConfig(**__a)
							_UpperCamelCase										=  text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
							_UpperCamelCase										=  CONFIG_MAPPING[text_model_type](**__a)

							_UpperCamelCase										=  self.text_config.tie_word_embeddings
							_UpperCamelCase										=  self.text_config.is_encoder_decoder

							_UpperCamelCase										=  num_query_tokens
							_UpperCamelCase										=  self.vision_config.hidden_size
							_UpperCamelCase										=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							_UpperCamelCase										=  1.0
							_UpperCamelCase										=  0.02



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		__a    ,		__a    ,		**__a    ,		)	->	Tuple:
							return cls(
							    vision_config=vision_config.to_dict()    ,		qformer_config=qformer_config.to_dict()    ,		text_config=text_config.to_dict()    ,		**__a    ,		)




		def 						UpperCAmelCase							( self)	->	Tuple:
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							_UpperCamelCase										=  self.vision_config.to_dict()
							_UpperCamelCase										=  self.qformer_config.to_dict()
							_UpperCamelCase										=  self.text_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output








"""simple docstring"""





class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  name
							_UpperCamelCase										=  value
							_UpperCamelCase										=  weight



		def __repr__( self)	->	List[str]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.value



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							return self.name



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.weight




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.value / self.weight





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for i in range(len(__snake_case     )     ):
										menu.append(Things(name[i],					value[i],					weight[i]     )     )
					return menu



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  sorted(__snake_case,					key=__snake_case,					reverse=__snake_case     )
					_UpperCamelCase										=  []
					_UpperCamelCase			,  _UpperCamelCase										=  0.0, 0.0
					for i in range(len(__snake_case     )     ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]     )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()






