





from __future__ import annotations

from decimal import Decimal

from numpy import array





def  __magic_name__  (				lowercase				)				-> list[list[float]]:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     Decimal

							# Check if the provided matrix has 2 rows and 2 columns
							# since this implementation only works for 2x2 matrices
							if len(lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
														# Calculate the determinant of the matrix
														lowercase_	:   int  				=     float(
														    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
														if determinant == 0:
																					raise ValueError("""This matrix has no inverse."""				)

														# Creates a copy of the matrix with swapped positions of the elements
														lowercase_	:   Dict  				=     [[0.0, 0.0], [0.0, 0.0]]
														lowercase_				,		lowercase_	:   Tuple  				=     matrix[1][1], matrix[0][0]
														lowercase_				,		lowercase_	:   Any  				=     -matrix[1][0], -matrix[0][1]

														# Calculate the inverse of the matrix
														return [
														    [(float(d(lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
														]
							elif (
							    len(lowercase				) == 3
							    and len(matrix[0]				) == 3
							    and len(matrix[1]				) == 3
							    and len(matrix[2]				) == 3
							):
														# Calculate the determinant of the matrix using Sarrus rule
														lowercase_	:   Optional[int]  				=     float(
														    (
														        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
														        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
														        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
														    )
														    - (
														        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
														        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
														        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
														    )				)
														if determinant == 0:
																					raise ValueError("""This matrix has no inverse."""				)

														# Creating cofactor matrix
														lowercase_	:   List[str]  				=     [
														    [d(0.0				), d(0.0				), d(0.0				)],
														    [d(0.0				), d(0.0				), d(0.0				)],
														    [d(0.0				), d(0.0				), d(0.0				)],
														]
														lowercase_	:   List[Any]  				=     (d(matrix[1][1]				) * d(matrix[2][2]				)) - (
														    d(matrix[1][2]				) * d(matrix[2][1]				)
														)
														lowercase_	:   int  				=     -(
														    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
														)
														lowercase_	:   int  				=     (d(matrix[1][0]				) * d(matrix[2][1]				)) - (
														    d(matrix[1][1]				) * d(matrix[2][0]				)
														)
														lowercase_	:   str  				=     -(
														    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
														)
														lowercase_	:   Optional[Any]  				=     (d(matrix[0][0]				) * d(matrix[2][2]				)) - (
														    d(matrix[0][2]				) * d(matrix[2][0]				)
														)
														lowercase_	:   Optional[int]  				=     -(
														    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
														)
														lowercase_	:   Tuple  				=     (d(matrix[0][1]				) * d(matrix[1][2]				)) - (
														    d(matrix[0][2]				) * d(matrix[1][1]				)
														)
														lowercase_	:   Optional[Any]  				=     -(
														    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
														)
														lowercase_	:   Optional[int]  				=     (d(matrix[0][0]				) * d(matrix[1][1]				)) - (
														    d(matrix[0][1]				) * d(matrix[1][0]				)
														)

														# Transpose the cofactor matrix (Adjoint matrix)
														lowercase_	:   Optional[Any]  				=     array(lowercase				)
														for i in range(3				):
																					for j in range(3				):
																												lowercase_	:   int  				=     cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
														lowercase_	:   Optional[int]  				=     array(lowercase				)
														for i in range(3				):
																					for j in range(3				):
																												inverse_matrix[i][j] /= d(lowercase				)

        # Calculate the inverse of the matrix
														return [[float(d(lowercase				)				) or 0.0 for n in row] for row in inverse_matrix]
							raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""				)






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     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask