
"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		list[int]							,		UpperCamelCase     :		list[int]     )     ->						tuple[float, float]:

    # Check if the input is valid
    if not len(UpperCamelCase     ) == len(UpperCamelCase     ) == 3:
        raise ValueError('Please enter a valid equation.'     )
    if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
        raise ValueError('Both a & b of two equations can\'t be zero.'     )

    # Extract the coefficients
    a__	,     a__	,     a__		=			equationa
    a__	,     a__	,     a__		=			equationa

    # Calculate the determinants of the matrices
    a__		=			aa * ba - aa * ba
    a__		=			ca * ba - ca * ba
    a__		=			aa * ca - aa * ca

    # Check if the system of linear equations has a solution (using Cramer's rule)
    if determinant == 0:
        if determinant_x == determinant_y == 0:
            raise ValueError('Infinite solutions. (Consistent system)'     )
        else:
            raise ValueError('No solution. (Inconsistent system)'     )
    else:
        if determinant_x == determinant_y == 0:
            # Trivial solution (Inconsistent system)
            return (0.0, 0.0)
        else:
            a__		=			determinant_x / determinant
            a__		=			determinant_y / determinant
            # Non-Trivial Solution (Consistent system)
            return (x, y)




"""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     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


