


"""simple docstring"""







import sys

sys.set_int_max_str_digits(0)  # type: ignore
def _A		(		lowercase						):







    """simple docstring"""
    a											=[0] * 11
    a											=[0] * 11

    # mark last 9 numbers
    for _ in range(9						):
        a											=1
        a											=number // 10
    # flag
    a											=True

    # check last 9 numbers for pandigitality

    for x in range(9						):
        if not check_last[x + 1]:
            a											=False
    if not f:
        return f

    # mark first 9 numbers
    a											=int(str(lowercase						)[:9]						)

    for _ in range(9						):
        a											=1
        a											=number // 10

    # check first 9 numbers for pandigitality

    for x in range(9						):
        if not check_front[x + 1]:
            a											=False
    return f
def _A		(		lowercase						):







    """simple docstring"""
    a											=[0] * 11

    # mark last 9 numbers
    for _ in range(9						):
        a											=1
        a											=number // 10
    # flag
    a											=True

    # check last 9 numbers for pandigitality

    for x in range(9						):
        if not check_last[x + 1]:
            a											=False
    return f
def _A		(		):







    """simple docstring"""
    a											=1
    a											=1
    a											=2
    # temporary Fibonacci numbers

    a											=1
    a											=1
    a											=2
    # temporary Fibonacci numbers mod 1e9

    # mod m=1e9, done for fast optimisation
    a											=[0] * 1_00_00_00
    a											=10_00_00_00_00

    for x in range(1_00_00_00						):
        a											=(aa + ba) % m
        a											=ba % m
        a											=ca % m
        if checka(lowercase						):
            a											=1

    for x in range(1_00_00_00						):
        a											=a + b
        a											=b
        a											=c
        # perform check only if in tocheck
        if tocheck[x + 3] and check(lowercase						):
            return x + 3  # first 2 already done
    return -1


if __name__ == "__main__":
       print(F'{solution() = }')



"""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