
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


lowercase_      							=       logging.get_logger(__name__)

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





class   __UpperCamelCase      (				A_    ):






     """simple docstring"""







     lowerCAmelCase_									=       "blip_2_vision_model"



     def __init__(							self				:				Optional[Any]			,		_A				:				Union[str, Any]=1408			,		_A				:				List[str]=6144			,		_A				:				Optional[Any]=39			,		_A				:				Dict=16			,		_A				:				int=224			,		_A				:				str=14			,		_A				:				Tuple="gelu"			,		_A				:				Optional[int]=0.0_00_01			,		_A				:				Any=0.0			,		_A				:				List[str]=1e-10			,		_A				:				Optional[int]=True			,		**_A				:				List[str]			,		):







       """simple docstring"""




       super().__init__(**_snake_case      )

       __SCREAMING_SNAKE_CASE	:     List[Any] =						hidden_size
       __SCREAMING_SNAKE_CASE	:     Tuple =						intermediate_size
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						num_hidden_layers
       __SCREAMING_SNAKE_CASE	:     Any =						num_attention_heads
       __SCREAMING_SNAKE_CASE	:     str =						patch_size
       __SCREAMING_SNAKE_CASE	:     List[str] =						image_size
       __SCREAMING_SNAKE_CASE	:     List[str] =						initializer_range
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						attention_dropout
       __SCREAMING_SNAKE_CASE	:     Any =						layer_norm_eps
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						hidden_act
       __SCREAMING_SNAKE_CASE	:     str =						qkv_bias




     @classmethod
     def       UpperCAmelCase__				(							cls				:				List[str]			,		_A				:				Union[str, os.PathLike]			,		**_A				:				Optional[int]      ):







       """simple docstring"""




       cls._set_token_in_kwargs(_snake_case      )

       __SCREAMING_SNAKE_CASE	:     Dict =						cls.get_config_dict(_snake_case			,		**_snake_case      )

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






class   __UpperCamelCase      (				A_    ):






     """simple docstring"""







     lowerCAmelCase_									=       "blip_2_qformer"



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







       """simple docstring"""




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

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




     @classmethod
     def       UpperCAmelCase__				(							cls				:				Tuple			,		_A				:				Union[str, os.PathLike]			,		**_A				:				Union[str, Any]      ):







       """simple docstring"""




       cls._set_token_in_kwargs(_snake_case      )

       __SCREAMING_SNAKE_CASE	:     Dict =						cls.get_config_dict(_snake_case			,		**_snake_case      )

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






class   __UpperCamelCase      (				A_    ):






     """simple docstring"""







     lowerCAmelCase_									=       "blip-2"
     lowerCAmelCase_									=       True



     def __init__(							self				:				List[Any]			,		_A				:				str=None			,		_A				:				Tuple=None			,		_A				:				Optional[Any]=None			,		_A				:				List[Any]=32			,		**_A				:				List[Any]      ):







       """simple docstring"""




       super().__init__(**_snake_case      )

       if vision_config is None:
         __SCREAMING_SNAKE_CASE	:     Optional[Any] =						{}
         logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.'''      )

       if qformer_config is None:
         __SCREAMING_SNAKE_CASE	:     Optional[Any] =						{}
         logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.'''      )

       if text_config is None:
         __SCREAMING_SNAKE_CASE	:     Optional[Any] =						{}
         logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''      )

       __SCREAMING_SNAKE_CASE	:     List[str] =						BlipaVisionConfig(**_snake_case      )
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						BlipaQFormerConfig(**_snake_case      )
       __SCREAMING_SNAKE_CASE	:     Tuple =						text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
       __SCREAMING_SNAKE_CASE	:     List[str] =						CONFIG_MAPPING[text_model_type](**_snake_case      )

       __SCREAMING_SNAKE_CASE	:     int =						self.text_config.tie_word_embeddings
       __SCREAMING_SNAKE_CASE	:     Any =						self.text_config.is_encoder_decoder

       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						num_query_tokens
       __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						self.vision_config.hidden_size
       __SCREAMING_SNAKE_CASE	:     Optional[Any] =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
       __SCREAMING_SNAKE_CASE	:     List[str] =						1.0
       __SCREAMING_SNAKE_CASE	:     Any =						0.02



     @classmethod
     def       UpperCAmelCase__				(							cls				:				Tuple			,		_A				:				BlipaVisionConfig			,		_A				:				BlipaQFormerConfig			,		_A				:				PretrainedConfig			,		**_A				:				int			,		):







       """simple docstring"""




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




     def       UpperCAmelCase__				(							self				:				int      ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE	:     List[Any] =						copy.deepcopy(self.__dict__      )
       __SCREAMING_SNAKE_CASE	:     int =						self.vision_config.to_dict()
       __SCREAMING_SNAKE_CASE	:     Dict =						self.qformer_config.to_dict()
       __SCREAMING_SNAKE_CASE	:     List[str] =						self.text_config.to_dict()
       __SCREAMING_SNAKE_CASE	:     Optional[int] =						self.__class__.model_type
       return output





"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  Dict			,*_snake_case						:  Any			,**_snake_case						:  str						)					->		None:






				"""simple docstring"""



				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''			,_snake_case			,)
				super().__init__(*_snake_case			,**_snake_case						)
