





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


snake_case								=							logging.get_logger(__name__)

snake_case								=							{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			int  			=	'''instructblip_vision_model'''







       def __init__(     self						:						Tuple    ,__A						:						Union[str, Any]=1408    ,__A						:						Optional[Any]=6144    ,__A						:						Tuple=39    ,__A						:						Any=16    ,__A						:						Any=224    ,__A						:						List[Any]=14    ,__A						:						Dict="gelu"    ,__A						:						str=1e-6    ,__A						:						Optional[int]=0.0    ,__A						:						int=1e-10    ,__A						:						Tuple=True    ,**__A						:						Any    ,)    ->       str:
         super().__init__(**__A	)

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







       @classmethod
       def  __UpperCAmelCase			(     cls						:						List[str]    ,__A						:						Union[str, os.PathLike]    ,**__A						:						Tuple	)    ->       "PretrainedConfig":
         cls._set_token_in_kwargs(__A	)

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

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'	) == "instructblip":
           _lowercase						    =			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 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			int  			=	'''instructblip_qformer'''







       def __init__(     self						:						Optional[int]    ,__A						:						str=3_0522    ,__A						:						str=768    ,__A						:						Tuple=12    ,__A						:						Tuple=12    ,__A						:						Any=3072    ,__A						:						List[str]="gelu"    ,__A						:						List[str]=0.1    ,__A						:						int=0.1    ,__A						:						List[Any]=512    ,__A						:						Optional[int]=0.02    ,__A						:						Optional[Any]=1e-12    ,__A						:						Optional[int]=0    ,__A						:						Optional[int]="absolute"    ,__A						:						Optional[Any]=2    ,__A						:						str=1408    ,**__A						:						str    ,)    ->       Optional[int]:
         super().__init__(pad_token_id=__A    ,**__A	)

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







       @classmethod
       def  __UpperCAmelCase			(     cls						:						Dict    ,__A						:						Union[str, os.PathLike]    ,**__A						:						int	)    ->       "PretrainedConfig":
         cls._set_token_in_kwargs(__A	)

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

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'	) == "instructblip":
           _lowercase						    =			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 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Any  			=	'''instructblip'''
       SCREAMING_SNAKE_CASE_   :			Tuple  			=	True







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

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

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

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

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

         _lowercase						    =			self.text_config.tie_word_embeddings
         _lowercase						    =			self.text_config.is_encoder_decoder

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







       @classmethod
       def  __UpperCAmelCase			(     cls						:						Union[str, Any]    ,__A						:						InstructBlipVisionConfig    ,__A						:						InstructBlipQFormerConfig    ,__A						:						PretrainedConfig    ,**__A						:						Tuple    ,)    ->       List[Any]:
         return cls(
             vision_config=vision_config.to_dict()    ,qformer_config=qformer_config.to_dict()    ,text_config=text_config.to_dict()    ,**__A    ,)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]	)    ->       int:
         _lowercase						    =			copy.deepcopy(self.__dict__	)
         _lowercase						    =			self.vision_config.to_dict()
         _lowercase						    =			self.qformer_config.to_dict()
         _lowercase						    =			self.text_config.to_dict()
         _lowercase						    =			self.__class__.model_type
         return output






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       warnings.warn(
           "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use BeitImageProcessor instead."			,				A_			,				)
       super().__init__(*A_			,				**A_  )
