


"""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       (snake_case__  ,						snake_case__  ,						snake_case__):





							'''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						,    )   ->List[str]:



														"""simple docstring"""



														super().__init__()

														snake_case_          =						nn.Embedding(UpperCAmelCase_						,    UpperCAmelCase_    )

														snake_case_          =						nn.Embedding(UpperCAmelCase_						,    UpperCAmelCase_    )
														snake_case_          =						False

														snake_case_          =						nn.Dropout(p=UpperCAmelCase_    )

														snake_case_          =						TaConfig(
														    vocab_size=UpperCAmelCase_						,    d_model=UpperCAmelCase_						,    num_heads=UpperCAmelCase_						,    d_kv=UpperCAmelCase_						,    d_ff=UpperCAmelCase_						,    dropout_rate=UpperCAmelCase_						,    feed_forward_proj=UpperCAmelCase_						,    is_decoder=UpperCAmelCase_						,    is_encoder_decoder=UpperCAmelCase_						,    )

														snake_case_          =						nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_    ):
																					snake_case_          =						TaBlock(UpperCAmelCase_    )
																					self.encoders.append(UpperCAmelCase_    )

														snake_case_          =						TaLayerNorm(UpperCAmelCase_    )
														snake_case_          =						nn.Dropout(p=UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: List[Any]    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						self.token_embedder(UpperCAmelCase_    )

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

														snake_case_          =						self.dropout_pre(UpperCAmelCase_    )

														# inverted the attention mask
														snake_case_          =						encoder_input_tokens.size()
														snake_case_          =						self.get_extended_attention_mask(UpperCAmelCase_						,    UpperCAmelCase_    )

														for lyr in self.encoders:
																					snake_case_          =						lyr(UpperCAmelCase_						,    UpperCAmelCase_    )[0]
														snake_case_          =						self.layer_norm(UpperCAmelCase_    )

														return self.dropout_post(UpperCAmelCase_    ), encoder_inputs_mask







"""simple docstring"""
import math





class        __A       :





							'''simple docstring'''

							def __init__(			self							: List[str]						,    UpperCAmelCase_							: Tuple=0    )   ->Optional[int]:  # a graph with Node 0,1,...,N-1



														"""simple docstring"""



														snake_case_          =						n
														snake_case_          =						[
														    [math.inf for j in range(0						,    UpperCAmelCase_    )] for i in range(0						,    UpperCAmelCase_    )
														]  # adjacency matrix for weight
														snake_case_          =						[
														    [math.inf for j in range(0						,    UpperCAmelCase_    )] for i in range(0						,    UpperCAmelCase_    )
														]  # dp[i][j] stores minimum distance from i to j



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: List[str]						,    UpperCAmelCase_							: Optional[Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						w



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Any:



														"""simple docstring"""



														for k in range(0						,    self.n    ):
																					for i in range(0						,    self.n    ):
																												for j in range(0						,    self.n    ):
																																			snake_case_          =						min(self.dp[i][j]						,    self.dp[i][k] + self.dp[k][j]    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: Dict						,    UpperCAmelCase_							: Optional[Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														return self.dp[u][v]


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      Graph(5)
				graph.add_edge(0, 2, 9)
				graph.add_edge(0, 4, 10)
				graph.add_edge(1, 3, 5)
				graph.add_edge(2, 3, 7)
				graph.add_edge(3, 0, 10)
				graph.add_edge(3, 1, 2)
				graph.add_edge(3, 2, 1)
				graph.add_edge(3, 4, 6)
				graph.add_edge(4, 1, 3)
				graph.add_edge(4, 2, 4)
				graph.add_edge(4, 3, 9)
				graph.floyd_warshall()
				graph.show_min(1, 4)
				graph.show_min(0, 3)



