





'''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 							__SCREAMING_SNAKE_CASE			(       A__							,   A__							,   A__      ):
      '''simple docstring'''



      @register_to_config
      def __init__(							self    : str   ,UpperCamelCase    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : float   ,UpperCamelCase    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : int   ,UpperCamelCase    : str   ,UpperCamelCase    : bool = False   ,)		->  Tuple:
           super().__init__()

           _lowercase							:      int  						=							nn.Linear(_lowerCAmelCase   ,_lowerCAmelCase   ,bias=_lowerCAmelCase  )

           _lowercase							:      int  						=							nn.Embedding(_lowerCAmelCase   ,_lowerCAmelCase  )
           _lowercase							:      Any  						=							False

           _lowercase							:      List[Any]  						=							nn.Dropout(p=_lowerCAmelCase  )

           _lowercase							:      int  						=							TaConfig(
               d_model=_lowerCAmelCase   ,num_heads=_lowerCAmelCase   ,d_kv=_lowerCAmelCase   ,d_ff=_lowerCAmelCase   ,feed_forward_proj=_lowerCAmelCase   ,dropout_rate=_lowerCAmelCase   ,is_decoder=_lowerCAmelCase   ,is_encoder_decoder=_lowerCAmelCase   ,)
           _lowercase							:      Optional[int]  						=							nn.ModuleList()
           for lyr_num in range(_lowerCAmelCase  ):
                _lowercase							:      str  						=							TaBlock(_lowerCAmelCase  )
                self.encoders.append(_lowerCAmelCase  )

           _lowercase							:      Dict  						=							TaLayerNorm(_lowerCAmelCase  )
           _lowercase							:      List[str]  						=							nn.Dropout(p=_lowerCAmelCase  )



      def   _lowerCamelCase		(							self    : Optional[int]   ,UpperCamelCase    : str   ,UpperCamelCase    : str  )		->  int:
           _lowercase							:      int  						=							self.input_proj(_lowerCAmelCase  )

           # terminal relative positional encodings
           _lowercase							:      int  						=							encoder_inputs.shape[1]
           _lowercase							:      int  						=							torch.arange(_lowerCAmelCase   ,device=encoder_inputs.device  )

           _lowercase							:      str  						=							encoder_inputs_mask.sum(-1  )
           _lowercase							:      List[str]  						=							torch.roll(input_positions.unsqueeze(0  )   ,tuple(seq_lens.tolist()  )   ,dims=0  )
           x += self.position_encoding(_lowerCAmelCase  )

           _lowercase							:      Dict  						=							self.dropout_pre(_lowerCAmelCase  )

           # inverted the attention mask
           _lowercase							:      Any  						=							encoder_inputs.size()
           _lowercase							:      Optional[Any]  						=							self.get_extended_attention_mask(_lowerCAmelCase   ,_lowerCAmelCase  )

           for lyr in self.encoders:
                _lowercase							:      Optional[Any]  						=							lyr(_lowerCAmelCase   ,_lowerCAmelCase  )[0]
           _lowercase							:      Optional[Any]  						=							self.layer_norm(_lowerCAmelCase  )

           return self.dropout_post(_lowerCAmelCase  ), encoder_inputs_mask






'''simple docstring'''







from __future__ import annotations

import math
import random

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	int)			->						list[int]:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							d * e - 1
     _lowercase							:      Optional[Any]  						=							0
     _lowercase							:      Dict  						=							0
     while p == 0:
          _lowercase							:      Optional[Any]  						=							random.randint(2       ,  n - 1)
          _lowercase							:      int  						=							k
          while True:
               if t % 2 == 0:
                    _lowercase							:      int  						=							t // 2
                    _lowercase							:      Any  						=							(g**t) % n
                    _lowercase							:      Tuple  						=							math.gcd(x - 1       ,  lowerCAmelCase__)
                    if x > 1 and y > 1:
                         _lowercase							:      Union[str, Any]  						=							y
                         _lowercase							:      Dict  						=							n // y
                         break  # find the correct factors
               else:
                    break  # t is not divisible by 2, break and choose another g
     return sorted([p, q])


if __name__ == "__main__":
    import doctest

    doctest.testmod()