

"""simple docstring"""


import copy
import os
from typing import Union

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


UpperCAmelCase													=logging.get_logger(__name__)

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





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''git_vision_model'''




    def __init__( self ,lowerCamelCase_=7_6_8 ,lowerCamelCase_=3_0_7_2 ,lowerCamelCase_=1_2 ,lowerCamelCase_=1_2 ,lowerCamelCase_=3 ,lowerCamelCase_=2_2_4 ,lowerCamelCase_=1_6 ,lowerCamelCase_="quick_gelu" ,lowerCamelCase_=1E-5 ,lowerCamelCase_=0.0 ,lowerCamelCase_=0.02 ,**lowerCamelCase_ ,)    ->      Tuple:
          super().__init__(**lowerCamelCase_    )

          A				  =		hidden_size
          A				  =		intermediate_size
          A				  =		num_hidden_layers
          A				  =		num_attention_heads
          A				  =		num_channels
          A				  =		patch_size
          A				  =		image_size
          A				  =		initializer_range
          A				  =		attention_dropout
          A				  =		layer_norm_eps
          A				  =		hidden_act




    @classmethod
    def UpperCamelCase__		( cls ,lowerCamelCase_ ,**lowerCamelCase_    )    ->      "PretrainedConfig":
          cls._set_token_in_kwargs(lowerCamelCase_    )

          A  ,			A				  =		cls.get_config_dict(lowerCamelCase_ ,**lowerCamelCase_    )

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





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''git'''




    def __init__( self ,lowerCamelCase_=None ,lowerCamelCase_=3_0_5_2_2 ,lowerCamelCase_=7_6_8 ,lowerCamelCase_=6 ,lowerCamelCase_=1_2 ,lowerCamelCase_=3_0_7_2 ,lowerCamelCase_="gelu" ,lowerCamelCase_=0.1 ,lowerCamelCase_=0.1 ,lowerCamelCase_=1_0_2_4 ,lowerCamelCase_=0.02 ,lowerCamelCase_=1E-12 ,lowerCamelCase_=0 ,lowerCamelCase_="absolute" ,lowerCamelCase_=True ,lowerCamelCase_=False ,lowerCamelCase_=1_0_1 ,lowerCamelCase_=1_0_2 ,lowerCamelCase_=None ,**lowerCamelCase_ ,)    ->      List[str]:
          super().__init__(bos_token_id=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ ,pad_token_id=lowerCamelCase_ ,**lowerCamelCase_    )

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

          A				  =		GitVisionConfig(**lowerCamelCase_    )
          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				  =		use_cache
          A				  =		tie_word_embeddings
          A				  =		num_image_with_embedding

          A				  =		bos_token_id
          A				  =		eos_token_id




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		copy.deepcopy(self.__dict__    )
          A				  =		self.vision_config.to_dict()
          A				  =		self.__class__.model_type
          return output







"""simple docstring"""


import random





class lowerCamelCase__   :



    '''simple docstring'''




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_    )    ->      tuple[list[int], list[int]]:
          A				  =		[ord(lowerCamelCase_    ) for i in text]
          A				  =		[]
          A				  =		[]
          for i in plain:
                A				  =		random.randint(1 ,3_0_0    )
                A				  =		(i + k) * k
                cipher.append(lowerCamelCase_    )
                key.append(lowerCamelCase_    )
          return cipher, key




    @staticmethod
    def UpperCamelCase__		( lowerCamelCase_ ,lowerCamelCase_    )    ->      str:
          A				  =		[]
          for i in range(len(lowerCamelCase_    )    ):
                A				  =		int((cipher[i] - (key[i]) ** 2) / key[i]    )
                plain.append(chr(lowerCamelCase_    )    )
          return "".join(lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase							,			UpperCAmelCase													=Onepad().encrypt("Hello")
  print(c, k)
  print(Onepad().decrypt(c, k))




