



"""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


_lowerCAmelCase    : Any							=							logging.get_logger(__name__)

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






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Any								=			'instructblip_vision_model'



      def __init__(					self					:     Optional[int]					,     A					:     str=1_4_0_8					,     A					:     Dict=6_1_4_4					,     A					:     Tuple=3_9					,     A					:     Optional[int]=1_6					,     A					:     int=2_2_4					,     A					:     Optional[Any]=1_4					,     A					:     Union[str, Any]="gelu"					,     A					:     Optional[int]=1e-6					,     A					:     Union[str, Any]=0.0					,     A					:     Tuple=1e-10					,     A					:     Union[str, Any]=True					,     **A					:     int					,     ):
           super().__init__(**A						)

           _UpperCAmelCase      :     int        =   hidden_size
           _UpperCAmelCase      :     List[str]        =   intermediate_size
           _UpperCAmelCase      :     List[Any]        =   num_hidden_layers
           _UpperCAmelCase      :     Optional[int]        =   num_attention_heads
           _UpperCAmelCase      :     Optional[int]        =   patch_size
           _UpperCAmelCase      :     List[Any]        =   image_size
           _UpperCAmelCase      :     Dict        =   initializer_range
           _UpperCAmelCase      :     int        =   attention_dropout
           _UpperCAmelCase      :     Optional[Any]        =   layer_norm_eps
           _UpperCAmelCase      :     Union[str, Any]        =   hidden_act
           _UpperCAmelCase      :     Union[str, Any]        =   qkv_bias



      @classmethod
      def 						snake_case_ (					cls					:     Any					,     A					:     Union[str, os.PathLike]					,     **A					:     List[Any]						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   cls.get_config_dict(A					,     **A						)

           # get the vision config dict if we are loading from InstructBlipConfig
           if config_dict.get("model_type"						) == "instructblip":
                _UpperCAmelCase      :     Optional[Any]        =   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_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			'instructblip_qformer'



      def __init__(					self					:     Any					,     A					:     Union[str, Any]=3_0_5_2_2					,     A					:     List[str]=7_6_8					,     A					:     Dict=1_2					,     A					:     List[str]=1_2					,     A					:     List[str]=3_0_7_2					,     A					:     Dict="gelu"					,     A					:     str=0.1					,     A					:     Union[str, Any]=0.1					,     A					:     Dict=5_1_2					,     A					:     Union[str, Any]=0.02					,     A					:     str=1e-12					,     A					:     str=0					,     A					:     Optional[int]="absolute"					,     A					:     List[str]=2					,     A					:     List[Any]=1_4_0_8					,     **A					:     Union[str, Any]					,     ):
           super().__init__(pad_token_id=A					,     **A						)

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



      @classmethod
      def 						snake_case_ (					cls					:     List[Any]					,     A					:     Union[str, os.PathLike]					,     **A					:     Optional[int]						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   cls.get_config_dict(A					,     **A						)

           # get the qformer config dict if we are loading from InstructBlipConfig
           if config_dict.get("model_type"						) == "instructblip":
                _UpperCAmelCase      :     Any        =   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_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			'instructblip'
      __SCREAMING_SNAKE_CASE						:       Tuple								=			True



      def __init__(					self					:     Optional[int]					,     A					:     List[str]=None					,     A					:     Any=None					,     A					:     Tuple=None					,     A					:     int=3_2					,     **A					:     Optional[int]						):
           super().__init__(**A						)

           if vision_config is None:
                _UpperCAmelCase      :     Union[str, Any]        =   {}
                logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."						)

           if qformer_config is None:
                _UpperCAmelCase      :     Union[str, Any]        =   {}
                logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."						)

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

           _UpperCAmelCase      :     Dict        =   InstructBlipVisionConfig(**A						)
           _UpperCAmelCase      :     List[Any]        =   InstructBlipQFormerConfig(**A						)
           _UpperCAmelCase      :     int        =   text_config["model_type"] if "model_type" in text_config else "opt"
           _UpperCAmelCase      :     Tuple        =   CONFIG_MAPPING[text_model_type](**A						)

           _UpperCAmelCase      :     int        =   self.text_config.tie_word_embeddings
           _UpperCAmelCase      :     List[str]        =   self.text_config.is_encoder_decoder

           _UpperCAmelCase      :     Optional[int]        =   num_query_tokens
           _UpperCAmelCase      :     int        =   self.vision_config.hidden_size
           _UpperCAmelCase      :     str        =   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           _UpperCAmelCase      :     Tuple        =   1.0
           _UpperCAmelCase      :     Any        =   0.02



      @classmethod
      def 						snake_case_ (					cls					:     int					,     A					:     InstructBlipVisionConfig					,     A					:     InstructBlipQFormerConfig					,     A					:     PretrainedConfig					,     **A					:     int					,     ):
           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					:     str						):
           _UpperCAmelCase      :     Any        =   copy.deepcopy(self.__dict__						)
           _UpperCAmelCase      :     Tuple        =   self.vision_config.to_dict()
           _UpperCAmelCase      :     str        =   self.qformer_config.to_dict()
           _UpperCAmelCase      :     Any        =   self.text_config.to_dict()
           _UpperCAmelCase      :     Optional[int]        =   self.__class__.model_type
           return output





"""simple docstring"""




from __future__ import annotations

from typing import Any

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]    )  ->       None:





     '''simple docstring'''

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  []						,  0    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						list[Any]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       None:





     '''simple docstring'''

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
 _lowerCAmelCase    : list[Any]							=							[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(["A", "B", "C"])
 generate_all_subsequences(seq)
