






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


UpperCAmelCase__							  =						logging.get_logger(__name__)

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






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''blip_2_vision_model'''







      def __init__( self :		Union[str, Any]					,   __UpperCAmelCase :		Optional[Any]=1_408					,   __UpperCAmelCase :		Optional[Any]=6_144					,   __UpperCAmelCase :		int=39					,   __UpperCAmelCase :		List[str]=16					,   __UpperCAmelCase :		Optional[int]=224					,   __UpperCAmelCase :		Union[str, Any]=14					,   __UpperCAmelCase :		Optional[int]="gelu"					,   __UpperCAmelCase :		Optional[int]=0.00001					,   __UpperCAmelCase :		List[str]=0.0					,   __UpperCAmelCase :		str=1e-1_0					,   __UpperCAmelCase :		List[str]=True					,   **__UpperCAmelCase :		List[str]					,   )       ->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  __lowerCAmelCase   ( cls :		Union[str, Any]					,   __UpperCAmelCase :		Union[str, os.PathLike]					,   **__UpperCAmelCase :		Tuple    )       ->"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 Blip2Config
       if config_dict.get('''model_type'''    ) == "blip-2":
        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 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''blip_2_qformer'''







      def __init__( self :		Any					,   __UpperCAmelCase :		Dict=30_522					,   __UpperCAmelCase :		Union[str, Any]=768					,   __UpperCAmelCase :		Any=12					,   __UpperCAmelCase :		Dict=12					,   __UpperCAmelCase :		Union[str, Any]=3_072					,   __UpperCAmelCase :		Optional[int]="gelu"					,   __UpperCAmelCase :		Optional[int]=0.1					,   __UpperCAmelCase :		str=0.1					,   __UpperCAmelCase :		Optional[Any]=512					,   __UpperCAmelCase :		str=0.02					,   __UpperCAmelCase :		Tuple=1e-1_2					,   __UpperCAmelCase :		List[str]=0					,   __UpperCAmelCase :		List[str]="absolute"					,   __UpperCAmelCase :		Optional[int]=2					,   __UpperCAmelCase :		List[Any]=1_408					,   **__UpperCAmelCase :		Dict					,   )       ->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  __lowerCAmelCase   ( cls :		Any					,   __UpperCAmelCase :		Union[str, os.PathLike]					,   **__UpperCAmelCase :		Optional[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 Blip2Config
       if config_dict.get('''model_type'''    ) == "blip-2":
        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 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''blip-2'''
      __snake_case	    =    True







      def __init__( self :		Dict					,   __UpperCAmelCase :		Optional[Any]=None					,   __UpperCAmelCase :		List[str]=None					,   __UpperCAmelCase :		str=None					,   __UpperCAmelCase :		Union[str, Any]=32					,   **__UpperCAmelCase :		List[Any]    )       ->List[Any]:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase    )

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

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

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

       a						    = BlipaVisionConfig(**__UpperCAmelCase    )
       a						    = BlipaQFormerConfig(**__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  __lowerCAmelCase   ( cls :		Optional[Any]					,   __UpperCAmelCase :		BlipaVisionConfig					,   __UpperCAmelCase :		BlipaQFormerConfig					,   __UpperCAmelCase :		PretrainedConfig					,   **__UpperCAmelCase :		Optional[Any]					,   )       ->Any:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Any    )       ->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 		_a       ( a					:list					)				->     list:

 if len(a					) < 2:
  return collection

 def circle_sort_util(a					:list      ,				a					:int      ,				a					:int					) -> bool:
  a						    = False

  if low == high:
   return swapped

  a						    = low
  a						    = high

  while left < right:
   if collection[left] > collection[right]:
    a    ,	a						    = (
        collection[right],
        collection[left],
    )
    a						    = True

   left += 1
   right -= 1

  if left == right and collection[left] > collection[right + 1]:
   a    ,	a						    = (
       collection[right + 1],
       collection[left],
   )

   a						    = True

  a						    = low + int((high - low) / 2					)
  a						    = circle_sort_util(a      ,				a      ,				a					)
  a						    = circle_sort_util(a      ,				mid + 1      ,				a					)

  return swapped or left_swap or right_swap

 a						    = True

 while is_not_sorted is True:
  a						    = circle_sort_util(a      ,				0      ,				len(a					) - 1					)

 return collection


if __name__ == "__main__":
     UpperCAmelCase__							  =						input("Enter numbers separated by a comma:\n").strip()
     UpperCAmelCase__							  =						[int(item) for item in user_input.split(",")]
     print(circle_sort(unsorted))


