

"""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_					:	Tuple							  =						logging.get_logger(__name__)

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



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"instructblip_vision_model"

    def __init__(     self: int    ,						UpperCamelCase: Any=14_08    ,						UpperCamelCase: Optional[Any]=61_44    ,						UpperCamelCase: Optional[int]=39    ,						UpperCamelCase: Optional[int]=16    ,						UpperCamelCase: Tuple=2_24    ,						UpperCamelCase: List[Any]=14    ,						UpperCamelCase: Optional[int]="gelu"    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=0.0    ,						UpperCamelCase: Optional[Any]=1e-1_0    ,						UpperCamelCase: List[Any]=True    ,						**UpperCamelCase: Optional[Any]    ,						):






         """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 				UpperCamelCase	(     cls: int    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: str       ):






         """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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"instructblip_qformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Any=3_05_22    ,						UpperCamelCase: Optional[int]=7_68    ,						UpperCamelCase: int=12    ,						UpperCamelCase: Optional[Any]=12    ,						UpperCamelCase: Optional[Any]=30_72    ,						UpperCamelCase: List[str]="gelu"    ,						UpperCamelCase: List[str]=0.1    ,						UpperCamelCase: List[Any]=0.1    ,						UpperCamelCase: Optional[Any]=5_12    ,						UpperCamelCase: Optional[Any]=0.02    ,						UpperCamelCase: List[Any]=1e-1_2    ,						UpperCamelCase: Dict=0    ,						UpperCamelCase: Tuple="absolute"    ,						UpperCamelCase: str=2    ,						UpperCamelCase: List[str]=14_08    ,						**UpperCamelCase: Tuple    ,						):






         """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 				UpperCamelCase	(     cls: List[str]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: List[Any]       ):






         """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 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"instructblip"
    UpperCAmelCase						 =					True

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






         """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 				UpperCamelCase	(     cls: Union[str, Any]    ,						UpperCamelCase: InstructBlipVisionConfig    ,						UpperCamelCase: InstructBlipQFormerConfig    ,						UpperCamelCase: PretrainedConfig    ,						**UpperCamelCase: 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 				UpperCamelCase	(     self: List[str]       ):






         """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









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					list			):
     if len(UpperCAmelCase_			) <= 1:
          return lst

     A__										=  1

     while i < len(UpperCAmelCase_			):
          if lst[i - 1] <= lst[i]:
               i += 1
          else:
               A__			,				A__										=  lst[i], lst[i - 1]
               i -= 1
               if i == 0:
                    A__										=  1

     return lst


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						input('Enter numbers separated by a comma:\n').strip()
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[int(item) for item in user_input.split(',')]
 print(gnome_sort(unsorted))






