




from string import ascii_uppercase




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_ = False       ,  lowercase_ = ascii_uppercase   )							->						str:

    """simple docstring"""


    __UpperCamelCase			=    keyword.upper()
    __UpperCamelCase			=    plaintext.upper()
    __UpperCamelCase			=    set(lowercase_   )

    # create a list of unique characters in the keyword - their order matters
    # it determines how we will map plaintext characters to the ciphertext
    __UpperCamelCase			=    []
    for char in keyword:
        if char in alphabet_set and char not in unique_chars:
            unique_chars.append(lowercase_   )
    # the number of those unique characters will determine the number of rows
    __UpperCamelCase			=    len(lowercase_   )

    # create a shifted version of the alphabet
    __UpperCamelCase			=    unique_chars + [
        char for char in alphabet if char not in unique_chars
    ]

    # create a modified alphabet by splitting the shifted alphabet into rows
    __UpperCamelCase			=    [
        shifted_alphabet[k : k + num_unique_chars_in_keyword]
        for k in range(0       ,  26       ,  lowercase_   )
    ]

    # map the alphabet characters to the modified alphabet characters
    # going 'vertically' through the modified alphabet - consider columns first
    __UpperCamelCase			=    {}
    __UpperCamelCase			=    0
    for column in range(lowercase_   ):
        for row in modified_alphabet:
            # if current row (the last one) is too short, break out of loop
            if len(lowercase_   ) <= column:
                break

            # map current letter to letter in modified alphabet
            __UpperCamelCase			=    row[column]
            letter_index += 1

    if verbose:
        print(lowercase_   )
    # create the encrypted text by mapping the plaintext to the modified alphabet
    return "".join(mapping[char] if char in mapping else char for char in plaintext   )


if __name__ == "__main__":
 # example use
 print(mixed_keyword("college", "UNIVERSITY"))










"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





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

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask