


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/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip_vision_model"""


     def __init__(	self  ,       __A=1408  ,       __A=6144  ,       __A=39  ,       __A=16  ,       __A=224  ,       __A=14  ,       __A="gelu"  ,       __A=1E-6  ,       __A=0.0  ,       __A=1E-10  ,       __A=True  ,       **__A  ,       ):
        super().__init__(**__A    )

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


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       **__A    ):
        cls._set_token_in_kwargs(__A    )

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

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""    ) == "instructblip":
           __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(__A  ,       **__A    )







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip_qformer"""


     def __init__(	self  ,       __A=30522  ,       __A=768  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=0.02  ,       __A=1E-12  ,       __A=0  ,       __A="absolute"  ,       __A=2  ,       __A=1408  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       **__A    )

        __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					=  cross_attention_frequency
        __a					=  encoder_hidden_size


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       **__A    ):
        cls._set_token_in_kwargs(__A    )

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

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("""model_type"""    ) == "instructblip":
           __a					=  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							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """instructblip"""
     _lowerCamelCase   =      True


     def __init__(	self  ,       __A=None  ,       __A=None  ,       __A=None  ,       __A=32  ,       **__A    ):
        super().__init__(**__A    )

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

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

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

        __a					=  InstructBlipVisionConfig(**__A    )
        __a					=  InstructBlipQFormerConfig(**__A    )
        __a					=  text_config["""model_type"""] if """model_type""" in text_config else """opt"""
        __a					=  CONFIG_MAPPING[text_model_type](**__A    )

        __a					=  self.text_config.tie_word_embeddings
        __a					=  self.text_config.is_encoder_decoder

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


     @classmethod
     def 	snake_case_  (	cls  ,       __A  ,       __A  ,       __A  ,       **__A  ,       ):
        return cls(
            vision_config=vision_config.to_dict()  ,       qformer_config=qformer_config.to_dict()  ,       text_config=text_config.to_dict()  ,       **__A  ,       )


     def 	snake_case_  (	self    ):
        __a					=  copy.deepcopy(self.__dict__    )
        __a					=  self.vision_config.to_dict()
        __a					=  self.qformer_config.to_dict()
        __a					=  self.text_config.to_dict()
        __a					=  self.__class__.model_type
        return output










"""simple docstring"""



def 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







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

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	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"""))




