




"""simple docstring"""

import copy
import os
from typing import Union

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


a      =      logging.get_logger(__name__)

a      =      {
    '''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
    '''Salesforce/blip-vqa-capfit-large''': (
        '''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-base''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
    ),
    '''Salesforce/blip-image-captioning-large''': (
        '''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
    ),
    '''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
    '''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
    '''Salesforce/blip-itm-large-flikr''': (
        '''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
    ),
}





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[str]				  =     '''blip_text_model'''

 def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	int=30_524   ,							_UpperCAmelCase	:	Any=768   ,							_UpperCAmelCase	:	str=768   ,							_UpperCAmelCase	:	List[Any]=3_072   ,							_UpperCAmelCase	:	int=768   ,							_UpperCAmelCase	:	Dict=12   ,							_UpperCAmelCase	:	Any=8   ,							_UpperCAmelCase	:	Optional[int]=512   ,							_UpperCAmelCase	:	Tuple="gelu"   ,							_UpperCAmelCase	:	Union[str, Any]=1E-1_2   ,							_UpperCAmelCase	:	List[Any]=0.0   ,							_UpperCAmelCase	:	Dict=0.0   ,							_UpperCAmelCase	:	List[str]=0.02   ,							_UpperCAmelCase	:	str=30_522   ,							_UpperCAmelCase	:	List[Any]=2   ,							_UpperCAmelCase	:	int=0   ,							_UpperCAmelCase	:	Optional[int]=102   ,							_UpperCAmelCase	:	int=True   ,							_UpperCAmelCase	:	Dict=True   ,							**_UpperCAmelCase	:	str   ,							):
       super().__init__(
           pad_token_id=_UpperCAmelCase   ,							bos_token_id=_UpperCAmelCase   ,							eos_token_id=_UpperCAmelCase   ,							sep_token_id=_UpperCAmelCase   ,							**_UpperCAmelCase   ,							)

       _A        =       vocab_size
       _A        =       hidden_size
       _A        =       encoder_hidden_size
       _A        =       intermediate_size
       _A        =       projection_dim
       _A        =       hidden_dropout_prob
       _A        =       num_hidden_layers
       _A        =       num_attention_heads
       _A        =       max_position_embeddings
       _A        =       layer_norm_eps
       _A        =       hidden_act
       _A        =       initializer_range
       _A        =       attention_probs_dropout_prob
       _A        =       is_decoder
       _A        =       use_cache



 @classmethod
 def       lowerCAmelCase_							(			cls	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, os.PathLike]   ,							**_UpperCAmelCase	:	Optional[int]		):
       cls._set_token_in_kwargs(_UpperCAmelCase		)

       _A       ,   _A        =       cls.get_config_dict(_UpperCAmelCase   ,							**_UpperCAmelCase		)

       # get the text config dict if we are loading from BlipConfig
       if config_dict.get('model_type'		) == "blip":
             _A        =       config_dict['text_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_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Tuple				  =     '''blip_vision_model'''

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	int=768   ,							_UpperCAmelCase	:	int=3_072   ,							_UpperCAmelCase	:	Optional[int]=512   ,							_UpperCAmelCase	:	Any=12   ,							_UpperCAmelCase	:	Optional[Any]=12   ,							_UpperCAmelCase	:	str=384   ,							_UpperCAmelCase	:	Union[str, Any]=16   ,							_UpperCAmelCase	:	int="gelu"   ,							_UpperCAmelCase	:	List[str]=1E-5   ,							_UpperCAmelCase	:	Optional[int]=0.0   ,							_UpperCAmelCase	:	Union[str, Any]=1E-1_0   ,							**_UpperCAmelCase	:	List[Any]   ,							):
       super().__init__(**_UpperCAmelCase		)

       _A        =       hidden_size
       _A        =       intermediate_size
       _A        =       projection_dim
       _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



 @classmethod
 def       lowerCAmelCase_							(			cls	:	Optional[int]   ,							_UpperCAmelCase	:	Union[str, os.PathLike]   ,							**_UpperCAmelCase	:	Tuple		):
       cls._set_token_in_kwargs(_UpperCAmelCase		)

       _A       ,   _A        =       cls.get_config_dict(_UpperCAmelCase   ,							**_UpperCAmelCase		)

       # get the vision config dict if we are loading from BlipConfig
       if config_dict.get('model_type'		) == "blip":
             _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_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[Any]				  =     '''blip'''
 UpperCAmelCase   :					Tuple				  =     True

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	str=None   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	List[str]=512   ,							_UpperCAmelCase	:	List[str]=2.6592   ,							_UpperCAmelCase	:	Optional[int]=256   ,							**_UpperCAmelCase	:	Tuple   ,							):
       super().__init__(**_UpperCAmelCase		)

       if text_config is None:
             _A        =       {}
             logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'		)

       if vision_config is None:
             _A        =       {}
             logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'		)

       _A        =       BlipTextConfig(**_UpperCAmelCase		)
       _A        =       BlipVisionConfig(**_UpperCAmelCase		)

       _A        =       self.vision_config.hidden_size

       _A        =       projection_dim
       _A        =       logit_scale_init_value
       _A        =       1.0
       _A        =       0.02
       _A        =       image_text_hidden_size

 @classmethod
 def       lowerCAmelCase_							(			cls	:	Union[str, Any]   ,							_UpperCAmelCase	:	BlipTextConfig   ,							_UpperCAmelCase	:	BlipVisionConfig   ,							**_UpperCAmelCase	:	List[Any]		):
       return cls(text_config=text_config.to_dict()   ,							vision_config=vision_config.to_dict()   ,							**_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       copy.deepcopy(self.__dict__		)
       _A        =       self.text_config.to_dict()
       _A        =       self.vision_config.to_dict()
       _A        =       self.__class__.model_type
       return output







"""simple docstring"""

def 						_snake_case					( _snake_case							: str							)   -> str:






      '''simple docstring'''


      _A        =       ''
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 						_snake_case					( _snake_case							: str							)   -> dict[str, str]:






      '''simple docstring'''


      _A        =       [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      _A        =       remove_duplicates(key.upper()							)
      _A        =       len(_snake_case							)
      # First fill cipher with key characters
      _A        =       {alphabet[i]: char for i, char in enumerate(_snake_case							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_snake_case							)    ,  26							):
            _A        =       alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  _A        =       alphabet[i - offset]
            _A        =       char
      return cipher_alphabet






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      return "".join(cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      _A        =       {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( )   -> None:






      '''simple docstring'''


      _A        =       input('Enter message to encode or decode: '							).strip()
      _A        =       input('Enter keyword: '							).strip()
      _A        =       input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            _A        =       {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      _A        =       create_cipher_map(_snake_case							)
      print(func(_snake_case    ,  _snake_case							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

