





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_		     = logging.get_logger(__name__)

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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			Any				=    "blip_2_vision_model"






     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Dict=1408		,lowerCamelCase__		:					List[str]=6144		,lowerCamelCase__		:					Tuple=39		,lowerCamelCase__		:					Union[str, Any]=16		,lowerCamelCase__		:					List[Any]=224		,lowerCamelCase__		:					Any=14		,lowerCamelCase__		:					Any="gelu"		,lowerCamelCase__		:					List[str]=0.00001		,lowerCamelCase__		:					Optional[Any]=0.0		,lowerCamelCase__		:					Optional[int]=1e-1_0		,lowerCamelCase__		:					Dict=True		,**lowerCamelCase__		:					List[str]		,)  ->	Dict:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__ )

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






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




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		cls.get_config_dict(lowerCamelCase__		,**lowerCamelCase__ )

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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    "blip_2_qformer"






     def __init__(							self		:					List[str]		,lowerCamelCase__		:					str=30522		,lowerCamelCase__		:					Optional[int]=768		,lowerCamelCase__		:					int=12		,lowerCamelCase__		:					Any=12		,lowerCamelCase__		:					Optional[Any]=3072		,lowerCamelCase__		:					List[Any]="gelu"		,lowerCamelCase__		:					str=0.1		,lowerCamelCase__		:					Tuple=0.1		,lowerCamelCase__		:					List[str]=512		,lowerCamelCase__		:					int=0.02		,lowerCamelCase__		:					Any=1e-1_2		,lowerCamelCase__		:					List[Any]=0		,lowerCamelCase__		:					Union[str, Any]="absolute"		,lowerCamelCase__		:					Union[str, Any]=2		,lowerCamelCase__		:					Tuple=1408		,**lowerCamelCase__		:					Optional[Any]		,)  ->	Any:




           '''simple docstring'''


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

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






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




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		cls.get_config_dict(lowerCamelCase__		,**lowerCamelCase__ )

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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "blip-2"
     __snake_case					:			str				=    True






     def __init__(							self		:					Optional[int]		,lowerCamelCase__		:					Dict=None		,lowerCamelCase__		:					str=None		,lowerCamelCase__		:					int=None		,lowerCamelCase__		:					List[str]=32		,**lowerCamelCase__		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__ )

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

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

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

           SCREAMING_SNAKE_CASE	=		BlipaVisionConfig(**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		BlipaQFormerConfig(**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		text_config["""model_type"""] if """model_type""" in text_config else """opt"""
           SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING[text_model_type](**lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		self.text_config.tie_word_embeddings
           SCREAMING_SNAKE_CASE	=		self.text_config.is_encoder_decoder

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






     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					int		,lowerCamelCase__		:					BlipaVisionConfig		,lowerCamelCase__		:					BlipaQFormerConfig		,lowerCamelCase__		:					PretrainedConfig		,**lowerCamelCase__		:					Union[str, Any]		,)  ->	Tuple:




           '''simple docstring'''


           return cls(
               vision_config=vision_config.to_dict()		,qformer_config=qformer_config.to_dict()		,text_config=text_config.to_dict()		,**lowerCamelCase__		,)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		copy.deepcopy(self.__dict__ )
           SCREAMING_SNAKE_CASE	=		self.vision_config.to_dict()
           SCREAMING_SNAKE_CASE	=		self.qformer_config.to_dict()
           SCREAMING_SNAKE_CASE	=		self.text_config.to_dict()
           SCREAMING_SNAKE_CASE	=		self.__class__.model_type
           return output









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		0
      for ca, ca in zip(stra[:4]				,		stra[:4]			):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))


