




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     :		List[str]								=       logging.get_logger(__name__)

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


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''instructblip_vision_model'''


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Dict=1_408 ,			UpperCAmelCase__				:     List[Any]=6_144 ,			UpperCAmelCase__				:     Tuple=39 ,			UpperCAmelCase__				:     Dict=16 ,			UpperCAmelCase__				:     List[Any]=224 ,			UpperCAmelCase__				:     Tuple=14 ,			UpperCAmelCase__				:     Any="gelu" ,			UpperCAmelCase__				:     int=1e-6 ,			UpperCAmelCase__				:     Dict=0.0 ,			UpperCAmelCase__				:     Optional[int]=1e-10 ,			UpperCAmelCase__				:     int=True ,			**UpperCAmelCase__				:     Union[str, Any] ,			)						->Tuple:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           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 	SCREAMING_SNAKE_CASE							(      cls				:     int ,			UpperCAmelCase__				:     Union[str, os.PathLike] ,			**UpperCAmelCase__				:     Any)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__			,							A__							=   cls.get_config_dict(UpperCAmelCase__ ,			**UpperCAmelCase__)

           # 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(UpperCAmelCase__ ,			**UpperCAmelCase__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''instructblip_qformer'''


    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     Optional[Any]=30_522 ,			UpperCAmelCase__				:     int=768 ,			UpperCAmelCase__				:     List[str]=12 ,			UpperCAmelCase__				:     Optional[int]=12 ,			UpperCAmelCase__				:     Any=3_072 ,			UpperCAmelCase__				:     Union[str, Any]="gelu" ,			UpperCAmelCase__				:     Any=0.1 ,			UpperCAmelCase__				:     Dict=0.1 ,			UpperCAmelCase__				:     List[Any]=512 ,			UpperCAmelCase__				:     List[str]=0.02 ,			UpperCAmelCase__				:     Any=1e-12 ,			UpperCAmelCase__				:     List[Any]=0 ,			UpperCAmelCase__				:     Tuple="absolute" ,			UpperCAmelCase__				:     Dict=2 ,			UpperCAmelCase__				:     Tuple=1_408 ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->str:





           '''simple docstring'''

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

           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 	SCREAMING_SNAKE_CASE							(      cls				:     Optional[int] ,			UpperCAmelCase__				:     Union[str, os.PathLike] ,			**UpperCAmelCase__				:     List[Any])						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__			,							A__							=   cls.get_config_dict(UpperCAmelCase__ ,			**UpperCAmelCase__)

           # 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(UpperCAmelCase__ ,			**UpperCAmelCase__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''instructblip'''
    UpperCAmelCase__							  =				True


    def __init__(      self				:     Any ,			UpperCAmelCase__				:     Tuple=None ,			UpperCAmelCase__				:     List[str]=None ,			UpperCAmelCase__				:     Tuple=None ,			UpperCAmelCase__				:     List[str]=32 ,			**UpperCAmelCase__				:     int)						->Any:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           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(**UpperCAmelCase__)
           A__							=   InstructBlipQFormerConfig(**UpperCAmelCase__)
           A__							=   text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
           A__							=   CONFIG_MAPPING[text_model_type](**UpperCAmelCase__)

           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 	SCREAMING_SNAKE_CASE							(      cls				:     List[Any] ,			UpperCAmelCase__				:     InstructBlipVisionConfig ,			UpperCAmelCase__				:     InstructBlipQFormerConfig ,			UpperCAmelCase__				:     PretrainedConfig ,			**UpperCAmelCase__				:     Any ,			)						->int:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Dict:





           '''simple docstring'''

           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








def SCREAMING_SNAKE_CASE    (	lowercase_ = 50						)       ->	int:



       """simple docstring"""

       A__							=   [1] * (length + 1)

       for row_length in range(3			,    length + 1						):
              for block_length in range(3			,    row_length + 1						):
                     for block_start in range(row_length - block_length						):
                            ways_number[row_length] += ways_number[
                                row_length - block_start - block_length - 1
                            ]

                     ways_number[row_length] += 1

       return ways_number[length]


if __name__ == "__main__":
  print(F'''{solution() = }''')


