


"""simple docstring"""







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


lowerCamelCase_  :  Optional[Any]     		=    logging.get_logger(__name__)

lowerCamelCase_  :  Any     		=    {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    "graphormer"
 __lowerCAmelCase					   =    ["past_key_values"]


 def __init__(						self					,       __A = 1					,       __A = 512 * 9					,       __A = 512 * 3					,       __A = 512					,       __A = 512					,       __A = 512					,       __A = 128					,       __A = 5					,       __A = 1024					,       __A = "multi_hop"					,       __A = 512					,       __A = False					,       __A = 12					,       __A = 768					,       __A = 768					,       __A = 32					,       __A = 0.1					,       __A = 0.1					,       __A = 0.0					,       __A = False					,       __A = False					,       __A = False					,       __A = "gelu"					,       __A = None					,       __A = False					,       __A = 0					,       __A = False					,       __A = 0.0					,       __A = 8					,       __A = None					,       __A = None					,       __A = True					,       __A = True					,       __A=0					,       __A=1					,       __A=2					,       **__A					,       )   ->	Optional[int]:
     a											=num_classes
     a											=num_atoms
     a											=num_in_degree
     a											=num_out_degree
     a											=num_edges
     a											=num_spatial
     a											=num_edge_dis
     a											=edge_type
     a											=multi_hop_max_dist
     a											=spatial_pos_max
     a											=max_nodes
     a											=num_hidden_layers
     a											=embedding_dim
     a											=embedding_dim
     a											=ffn_embedding_dim
     a											=num_attention_heads
     a											=dropout
     a											=attention_dropout
     a											=layerdrop
     a											=encoder_normalize_before
     a											=pre_layernorm
     a											=apply_graphormer_init
     a											=activation_fn
     a											=embed_scale
     a											=freeze_embeddings
     a											=num_trans_layers_to_freeze
     a											=share_input_output_embed
     a											=traceable
     a											=q_noise
     a											=qn_block_size

     # These parameters are here for future extensions
     # atm, the model only supports self attention
     a											=kdim
     a											=vdim
     a											=self_attention
     a											=bias

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



"""simple docstring"""







from __future__ import annotations

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


 def __str__(						self		)   ->	str:
     return "".join(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	list[int]:
     for i in range(1					,       len(__A		)					,       2		):
         iterlist[i] *= -1
     return iterlist


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

     # creates points known as breakpoints to break the key_list_options at those
     # points and pivot each substring
     a											=sorted(set(self.__passcode		)		)
     a											=[]

     # algorithm for creating a new shuffled list, keys_l, out of key_list_options
     for i in key_list_options:
         temp_list.extend(__A		)

         # checking breakpoints at which to pivot temporary sublist and add it into
         # keys_l
         if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]		)
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
     return keys_l


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # decoding shift like Caesar cipher algorithm implementing negative shift or
     # reverse shift or left shift
     for i in encoded_message:
         a											=self.__key_list.index(__A		)
         decoded_message += self.__key_list[
             (position - self.__shift_key) % -len(self.__key_list		)
         ]

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # encoding shift like Caesar cipher algorithm implementing positive shift or
     # forward shift or right shift
     for i in plaintext:
         a											=self.__key_list.index(__A		)
         encoded_message += self.__key_list[
             (position + self.__shift_key) % len(self.__key_list		)
         ]

     return encoded_message

def _A		(		lowercase = "Hello, this is a modified Caesar cipher"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()