





def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	)
      while cur > 1:
            # Find the maximum number in arr
            SCREAMING_SNAKE_CASE		=						arr.index(max(arr[0:cur]	)	)
            # Reverse from 0 to mi
            SCREAMING_SNAKE_CASE		=						arr[mi::-1] + arr[mi + 1 : len(_SCREAMING_SNAKE_CASE	)]
            # Reverse whole list
            SCREAMING_SNAKE_CASE		=						arr[cur - 1 :: -1] + arr[cur : len(_SCREAMING_SNAKE_CASE	)]
            cur -= 1
      return arr


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









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			"git_vision_model"






     def __init__(							self						:    List[Any]						,lowerCamelCase__						:    Dict=768						,lowerCamelCase__						:    Union[str, Any]=3072						,lowerCamelCase__						:    Optional[int]=12						,lowerCamelCase__						:    Tuple=12						,lowerCamelCase__						:    Tuple=3						,lowerCamelCase__						:    Optional[Any]=224						,lowerCamelCase__						:    Union[str, Any]=16						,lowerCamelCase__						:    List[Any]="quick_gelu"						,lowerCamelCase__						:    Optional[Any]=1e-5						,lowerCamelCase__						:    str=0.0						,lowerCamelCase__						:    Optional[int]=0.02						,**lowerCamelCase__						:    Union[str, Any]						,)     ->		Optional[int]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

           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		=						num_channels
           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






     @classmethod
     def 				SCREAMING_SNAKE_CASE__  (							cls						:    Tuple						,lowerCamelCase__						:    Union[str, os.PathLike]						,**lowerCamelCase__						:    int       )     ->		"PretrainedConfig":




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						cls.get_config_dict(lowerCamelCase__						,**lowerCamelCase__       )

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get("""model_type"""       ) == "git":
                 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(lowerCamelCase__						,**lowerCamelCase__       )

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			"git"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    int=None						,lowerCamelCase__						:    str=30522						,lowerCamelCase__						:    Tuple=768						,lowerCamelCase__						:    Union[str, Any]=6						,lowerCamelCase__						:    str=12						,lowerCamelCase__						:    List[str]=3072						,lowerCamelCase__						:    Dict="gelu"						,lowerCamelCase__						:    Tuple=0.1						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    List[str]=1024						,lowerCamelCase__						:    List[str]=0.02						,lowerCamelCase__						:    str=1e-1_2						,lowerCamelCase__						:    Optional[int]=0						,lowerCamelCase__						:    Optional[int]="absolute"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    str=False						,lowerCamelCase__						:    int=101						,lowerCamelCase__						:    int=102						,lowerCamelCase__						:    Dict=None						,**lowerCamelCase__						:    List[Any]						,)     ->		Optional[Any]:




           '''simple docstring'''


           super().__init__(bos_token_id=lowerCamelCase__						,eos_token_id=lowerCamelCase__						,pad_token_id=lowerCamelCase__						,**lowerCamelCase__       )

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

           SCREAMING_SNAKE_CASE		=						GitVisionConfig(**lowerCamelCase__       )
           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		=						use_cache
           SCREAMING_SNAKE_CASE		=						tie_word_embeddings
           SCREAMING_SNAKE_CASE		=						num_image_with_embedding

           SCREAMING_SNAKE_CASE		=						bos_token_id
           SCREAMING_SNAKE_CASE		=						eos_token_id






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						copy.deepcopy(self.__dict__       )
           SCREAMING_SNAKE_CASE		=						self.vision_config.to_dict()
           SCREAMING_SNAKE_CASE		=						self.__class__.model_type
           return output


