





'''simple docstring'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )






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


SCREAMING_SNAKE_CASE__       :      Optional[int]  =   logging.get_logger(__name__)

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




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Union[str, Any]	     =      """blip_2_vision_model"""







    def __init__(    self     ,  UpperCamelCase__=1408     ,  UpperCamelCase__=6144     ,  UpperCamelCase__=39     ,  UpperCamelCase__=16     ,  UpperCamelCase__=224     ,  UpperCamelCase__=14     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.00001     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=1e-10     ,  UpperCamelCase__=True     ,  **UpperCamelCase__     ,  )     ->      Optional[Any]:
           super().__init__(**UpperCamelCase__     )

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      "PretrainedConfig":
           cls._set_token_in_kwargs(UpperCamelCase__     )

           lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					cls.get_config_dict(UpperCamelCase__     ,  **UpperCamelCase__     )

           # get the vision config dict if we are loading from Blip2Config
           if config_dict.get("model_type"     ) == "blip-2":
                  lowerCamelCase   :      Optional[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(UpperCamelCase__     ,  **UpperCamelCase__     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      """blip_2_qformer"""







    def __init__(    self     ,  UpperCamelCase__=3_0522     ,  UpperCamelCase__=768     ,  UpperCamelCase__=12     ,  UpperCamelCase__=12     ,  UpperCamelCase__=3072     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=512     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-12     ,  UpperCamelCase__=0     ,  UpperCamelCase__="absolute"     ,  UpperCamelCase__=2     ,  UpperCamelCase__=1408     ,  **UpperCamelCase__     ,  )     ->      int:
           super().__init__(pad_token_id=UpperCamelCase__     ,  **UpperCamelCase__     )

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      "PretrainedConfig":
           cls._set_token_in_kwargs(UpperCamelCase__     )

           lowerCamelCase ,      lowerCamelCase   :      str			 =					cls.get_config_dict(UpperCamelCase__     ,  **UpperCamelCase__     )

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




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[str]	     =      """blip-2"""
    lowerCamelCase_      :	int	     =      True







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=32     ,  **UpperCamelCase__     )     ->      str:
           super().__init__(**UpperCamelCase__     )

           if vision_config is None:
                  lowerCamelCase   :      List[Any]			 =					{}
                  logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."     )

           if qformer_config is None:
                  lowerCamelCase   :      List[Any]			 =					{}
                  logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."     )

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

           lowerCamelCase   :      Optional[int]			 =					BlipaVisionConfig(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					BlipaQFormerConfig(**UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					text_config["model_type"] if "model_type" in text_config else "opt"
           lowerCamelCase   :      str			 =					CONFIG_MAPPING[text_model_type](**UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					self.text_config.tie_word_embeddings
           lowerCamelCase   :      int			 =					self.text_config.is_encoder_decoder

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ,  )     ->      str:
           return cls(
               vision_config=vision_config.to_dict()     ,  qformer_config=qformer_config.to_dict()     ,  text_config=text_config.to_dict()     ,  **UpperCamelCase__     ,  )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Tuple			 =					copy.deepcopy(self.__dict__     )
           lowerCamelCase   :      Tuple			 =					self.vision_config.to_dict()
           lowerCamelCase   :      int			 =					self.qformer_config.to_dict()
           lowerCamelCase   :      Optional[Any]			 =					self.text_config.to_dict()
           lowerCamelCase   :      int			 =					self.__class__.model_type
           return output






