import copy
import os
from typing import Union

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


_lowerCamelCase       : Union[str, Any]      					=		logging.get_logger(__name__)

_lowerCamelCase       : Tuple      					=		{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}


class     lowercase     (  snake_case_  ):
							lowercase__    :							Optional[Any]										=				'''pix2struct_text_model'''
							lowercase__    :							List[Any]										=				['''past_key_values''']
							lowercase__    :							Dict										=				{
							    '''hidden_size''': '''hidden_size''',
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(    self :       Tuple						,				_UpperCamelCase :       str=50_244						,				_UpperCamelCase :       Dict=768						,				_UpperCamelCase :       Union[str, Any]=64						,				_UpperCamelCase :       int=2_048						,				_UpperCamelCase :       List[Any]=12						,				_UpperCamelCase :       List[str]=12						,				_UpperCamelCase :       str=32						,				_UpperCamelCase :       Optional[Any]=128						,				_UpperCamelCase :       Tuple=0.1						,				_UpperCamelCase :       Optional[Any]=1e-6						,				_UpperCamelCase :       Union[str, Any]=1.0						,				_UpperCamelCase :       Optional[int]="gelu_new"						,				_UpperCamelCase :       Optional[Any]=0						,				_UpperCamelCase :       Any=False						,				_UpperCamelCase :       Union[str, Any]=0						,				_UpperCamelCase :       int=1						,				_UpperCamelCase :       str=False						,				_UpperCamelCase :       List[Any]=True						,				**_UpperCamelCase :       int						,				)       ->					List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							vocab_size
												SCREAMING_SNAKE_CASE           =							hidden_size
												SCREAMING_SNAKE_CASE           =							d_kv
												SCREAMING_SNAKE_CASE           =							d_ff
												SCREAMING_SNAKE_CASE           =							num_layers
												SCREAMING_SNAKE_CASE           =							num_heads
												SCREAMING_SNAKE_CASE           =							relative_attention_num_buckets
												SCREAMING_SNAKE_CASE           =							relative_attention_max_distance
												SCREAMING_SNAKE_CASE           =							dropout_rate
												SCREAMING_SNAKE_CASE           =							layer_norm_epsilon
												SCREAMING_SNAKE_CASE           =							initializer_factor
												SCREAMING_SNAKE_CASE           =							use_cache

												SCREAMING_SNAKE_CASE           =							eos_token_id
												SCREAMING_SNAKE_CASE           =							decoder_start_token_id

												# for backwards compatibility
												SCREAMING_SNAKE_CASE           =							dense_act_fn

												super().__init__(
												    pad_token_id=UpperCamelCase__						,				eos_token_id=UpperCamelCase__						,				decoder_start_token_id=UpperCamelCase__						,				tie_word_embeddings=UpperCamelCase__						,				is_decoder=UpperCamelCase__						,				**UpperCamelCase__						,				)





							@classmethod
							def 						__snake_case(    cls :       Dict						,				_UpperCamelCase :       List[str]						,				**_UpperCamelCase :       List[str]   )       ->					"PretrainedConfig":



												'''simple docstring'''

												cls._set_token_in_kwargs(UpperCamelCase__   )

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

												# get the text config dict if we are loading from Pix2StructConfig
												if config_dict.get("model_type"   ) == "pix2struct":
																	SCREAMING_SNAKE_CASE           =							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     (  snake_case_  ):
							lowercase__    :							Union[str, Any]										=				'''pix2struct_vision_model'''




							def __init__(    self :       Dict						,				_UpperCamelCase :       List[Any]=768						,				_UpperCamelCase :       str=768						,				_UpperCamelCase :       Optional[int]=2_048						,				_UpperCamelCase :       Dict=64						,				_UpperCamelCase :       int=12						,				_UpperCamelCase :       Union[str, Any]=12						,				_UpperCamelCase :       Optional[Any]="gelu_new"						,				_UpperCamelCase :       Tuple=1e-6						,				_UpperCamelCase :       Any=0.0						,				_UpperCamelCase :       List[Any]=0.0						,				_UpperCamelCase :       List[str]=1e-10						,				_UpperCamelCase :       Tuple=1.0						,				_UpperCamelCase :       Any=4_096						,				_UpperCamelCase :       int=32						,				_UpperCamelCase :       int=128						,				**_UpperCamelCase :       str						,				)       ->					Dict:



												'''simple docstring'''

												super().__init__(**UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							hidden_size
												SCREAMING_SNAKE_CASE           =							patch_embed_hidden_size
												SCREAMING_SNAKE_CASE           =							d_ff
												SCREAMING_SNAKE_CASE           =							dropout_rate
												SCREAMING_SNAKE_CASE           =							num_hidden_layers
												SCREAMING_SNAKE_CASE           =							num_attention_heads
												SCREAMING_SNAKE_CASE           =							initializer_range
												SCREAMING_SNAKE_CASE           =							initializer_factor
												SCREAMING_SNAKE_CASE           =							attention_dropout
												SCREAMING_SNAKE_CASE           =							layer_norm_eps
												SCREAMING_SNAKE_CASE           =							dense_act_fn
												SCREAMING_SNAKE_CASE           =							seq_len
												SCREAMING_SNAKE_CASE           =							relative_attention_num_buckets
												SCREAMING_SNAKE_CASE           =							relative_attention_max_distance
												SCREAMING_SNAKE_CASE           =							d_kv





							@classmethod
							def 						__snake_case(    cls :       int						,				_UpperCamelCase :       List[Any]						,				**_UpperCamelCase :       str   )       ->					"PretrainedConfig":



												'''simple docstring'''

												cls._set_token_in_kwargs(UpperCamelCase__   )

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

												# get the vision config dict if we are loading from Pix2StructConfig
												if config_dict.get("model_type"   ) == "pix2struct":
																	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(UpperCamelCase__						,				**UpperCamelCase__   )



class     lowercase     (  snake_case_  ):
							lowercase__    :							Optional[Any]										=				'''pix2struct'''
							lowercase__    :							Dict										=				True




							def __init__(    self :       str						,				_UpperCamelCase :       List[Any]=None						,				_UpperCamelCase :       Any=None						,				_UpperCamelCase :       int=1.0						,				_UpperCamelCase :       Tuple=0.0_2						,				_UpperCamelCase :       Optional[int]=False						,				_UpperCamelCase :       Any=False						,				_UpperCamelCase :       str=True						,				**_UpperCamelCase :       Dict						,				)       ->					Any:



												'''simple docstring'''

												super().__init__(tie_word_embeddings=UpperCamelCase__						,				is_encoder_decoder=UpperCamelCase__						,				**UpperCamelCase__   )

												if text_config is None:
																	SCREAMING_SNAKE_CASE           =							{}
																	logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."   )

												if vision_config is None:
																	SCREAMING_SNAKE_CASE           =							{}
																	logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."   )

												SCREAMING_SNAKE_CASE           =							PixaStructTextConfig(**UpperCamelCase__   )
												SCREAMING_SNAKE_CASE           =							PixaStructVisionConfig(**UpperCamelCase__   )

												SCREAMING_SNAKE_CASE           =							self.text_config.decoder_start_token_id
												SCREAMING_SNAKE_CASE           =							self.text_config.pad_token_id
												SCREAMING_SNAKE_CASE           =							self.text_config.eos_token_id

												SCREAMING_SNAKE_CASE           =							initializer_factor
												SCREAMING_SNAKE_CASE           =							initializer_range

												SCREAMING_SNAKE_CASE           =							self.initializer_range
												SCREAMING_SNAKE_CASE           =							self.initializer_range

												SCREAMING_SNAKE_CASE           =							is_vqa




							@classmethod
							def 						__snake_case(    cls :       int						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Tuple						,				**_UpperCamelCase :       Optional[int]   )       ->					Tuple:



												'''simple docstring'''

												return cls(text_config=text_config.to_dict()						,				vision_config=vision_config.to_dict()						,				**UpperCamelCase__   )





							def 						__snake_case(    self :       Union[str, Any]   )       ->					Union[str, Any]:



												'''simple docstring'''

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








"""simple docstring"""



import string







def     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





