

"""simple docstring"""




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


class 		_A       (			_A					):
      __a								=	'blip_2_vision_model'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=1408    ,						SCREAMING_SNAKE_CASE__=6144    ,						SCREAMING_SNAKE_CASE__=39    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__=224    ,						SCREAMING_SNAKE_CASE__=14    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.0_00_01    ,						SCREAMING_SNAKE_CASE__=0.0    ,						SCREAMING_SNAKE_CASE__=1e-10    ,						SCREAMING_SNAKE_CASE__=True    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					List[Any]:
         super().__init__(**__lowerCamelCase		)

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





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					"PretrainedConfig":
         cls._set_token_in_kwargs(__lowerCamelCase		)

         lowerCamelCase__						=			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":
            lowerCamelCase__						=			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 		_A       (			_A					):
      __a								=	'blip_2_qformer'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=30522    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=3072    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=512    ,						SCREAMING_SNAKE_CASE__=0.02    ,						SCREAMING_SNAKE_CASE__=1e-12    ,						SCREAMING_SNAKE_CASE__=0    ,						SCREAMING_SNAKE_CASE__="absolute"    ,						SCREAMING_SNAKE_CASE__=2    ,						SCREAMING_SNAKE_CASE__=1408    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Optional[Any]:
         super().__init__(pad_token_id=__lowerCamelCase    ,						**__lowerCamelCase		)

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





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					"PretrainedConfig":
         cls._set_token_in_kwargs(__lowerCamelCase		)

         lowerCamelCase__						=			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":
            lowerCamelCase__						=			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 		_A       (			_A					):
      __a								=	'blip-2'
      __a								=	True





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=32    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         super().__init__(**__lowerCamelCase		)

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

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

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

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

         lowerCamelCase__						=			self.text_config.tie_word_embeddings
         lowerCamelCase__						=			self.text_config.is_encoder_decoder

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





      @classmethod
      def 					_lowerCamelCase  (  cls    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Tuple:
         return cls(
             vision_config=vision_config.to_dict()    ,						qformer_config=qformer_config.to_dict()    ,						text_config=text_config.to_dict()    ,						**__lowerCamelCase    ,						)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			copy.deepcopy(self.__dict__		)
         lowerCamelCase__						=			self.vision_config.to_dict()
         lowerCamelCase__						=			self.qformer_config.to_dict()
         lowerCamelCase__						=			self.text_config.to_dict()
         lowerCamelCase__						=			self.__class__.model_type
         return output







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      str:




   """simple docstring"""
   return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




