




'''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_			=						logging.get_logger(__name__)

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







class 		lowercase_   (			SCREAMING_SNAKE_CASE__       ):


   """simple docstring"""

   lowerCamelCase_      =						'''instructblip_vision_model'''



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

      """simple docstring"""
      super().__init__(**A__   )

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



   @classmethod
   def 							lowerCAmelCase_      (   cls :     str  ,				__lowerCamelCase :     Tuple  ,				**__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      cls._set_token_in_kwargs(A__   )

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

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get("model_type"   ) == "instructblip":
         _SCREAMING_SNAKE_CASE	=				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 		lowercase_   (			SCREAMING_SNAKE_CASE__       ):


   """simple docstring"""

   lowerCamelCase_      =						'''instructblip_qformer'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Optional[Any]=3_0_5_2_2  ,				__lowerCamelCase :     List[Any]=7_6_8  ,				__lowerCamelCase :     Optional[Any]=1_2  ,				__lowerCamelCase :     Tuple=1_2  ,				__lowerCamelCase :     Tuple=3_0_7_2  ,				__lowerCamelCase :     Dict="gelu"  ,				__lowerCamelCase :     List[Any]=0.1  ,				__lowerCamelCase :     Dict=0.1  ,				__lowerCamelCase :     int=5_1_2  ,				__lowerCamelCase :     Union[str, Any]=0.0_2  ,				__lowerCamelCase :     str=1e-12  ,				__lowerCamelCase :     Dict=0  ,				__lowerCamelCase :     int="absolute"  ,				__lowerCamelCase :     Tuple=2  ,				__lowerCamelCase :     Union[str, Any]=1_4_0_8  ,				**__lowerCamelCase :     Any  ,				):

      """simple docstring"""
      super().__init__(pad_token_id=A__  ,				**A__   )

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



   @classmethod
   def 							lowerCAmelCase_      (   cls :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      cls._set_token_in_kwargs(A__   )

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

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get("model_type"   ) == "instructblip":
         _SCREAMING_SNAKE_CASE	=				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 		lowercase_   (			SCREAMING_SNAKE_CASE__       ):


   """simple docstring"""

   lowerCamelCase_      =						'''instructblip'''
   lowerCamelCase_      =						True



   def __init__(   self :     int  ,				__lowerCamelCase :     Optional[int]=None  ,				__lowerCamelCase :     List[str]=None  ,				__lowerCamelCase :     List[str]=None  ,				__lowerCamelCase :     int=3_2  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      super().__init__(**A__   )

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

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

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

      _SCREAMING_SNAKE_CASE	=				InstructBlipVisionConfig(**A__   )
      _SCREAMING_SNAKE_CASE	=				InstructBlipQFormerConfig(**A__   )
      _SCREAMING_SNAKE_CASE	=				text_config["model_type"] if "model_type" in text_config else "opt"
      _SCREAMING_SNAKE_CASE	=				CONFIG_MAPPING[text_model_type](**A__   )

      _SCREAMING_SNAKE_CASE	=				self.text_config.tie_word_embeddings
      _SCREAMING_SNAKE_CASE	=				self.text_config.is_encoder_decoder

      _SCREAMING_SNAKE_CASE	=				num_query_tokens
      _SCREAMING_SNAKE_CASE	=				self.vision_config.hidden_size
      _SCREAMING_SNAKE_CASE	=				self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      _SCREAMING_SNAKE_CASE	=				1.0
      _SCREAMING_SNAKE_CASE	=				0.0_2



   @classmethod
   def 							lowerCAmelCase_      (   cls :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Any  ,				**__lowerCamelCase :     Any  ,				):

      """simple docstring"""
      return cls(
          vision_config=vision_config.to_dict()  ,				qformer_config=qformer_config.to_dict()  ,				text_config=text_config.to_dict()  ,				**A__  ,				)



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				copy.deepcopy(self.__dict__   )
      _SCREAMING_SNAKE_CASE	=				self.vision_config.to_dict()
      _SCREAMING_SNAKE_CASE	=				self.qformer_config.to_dict()
      _SCREAMING_SNAKE_CASE	=				self.text_config.to_dict()
      _SCREAMING_SNAKE_CASE	=				self.__class__.model_type
      return output







'''simple docstring'''



from collections.abc import Sequence
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Sequence[int] | None = None  )			->			int:
   if nums is None or not nums:
      raise ValueError("Input sequence should not be empty"  )

   _SCREAMING_SNAKE_CASE	=				nums[0]
   for i in range(1	,					len(__A  )  ):
      _SCREAMING_SNAKE_CASE	=				nums[i]
      _SCREAMING_SNAKE_CASE	=				max(__A	,					ans + num	,					__A  )

   return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 lowerCamelCase_			=						int(input('Enter number of elements : ').strip())
 lowerCamelCase_			=						list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
 print(max_subsequence_sum(array))

