





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    None
					A__				 =    None
					A__				 =    None
					A__				 =    None

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase=1	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=512	,  _lowerCAmelCase="cls"	,  _lowerCAmelCase=False	,  _lowerCAmelCase=True	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)
										lowerCamelCase__       =       project_dim
										lowerCamelCase__       =       pooler_fn
										lowerCamelCase__       =       learn_encoder
										lowerCamelCase__       =       use_attention_mask

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    [r"pooler", r"logit_scale"]
					A__				 =    [r"position_ids", r"predictions.decoder.bias"]
					A__				 =    "roberta"
					A__				 =    RobertaSeriesConfig





					def __init__( self	,  _lowerCAmelCase	):
										super().__init__(_lowerCAmelCase	)
										lowerCamelCase__       =       XLMRobertaModel(_lowerCAmelCase	)
										lowerCamelCase__       =       nn.Linear(config.hidden_size	,  config.project_dim	)
										lowerCamelCase__       =       getattr(_lowerCAmelCase	,  "has_pre_transformation"	,  _lowerCAmelCase	)
										if self.has_pre_transformation:
															lowerCamelCase__       =       nn.Linear(config.hidden_size	,  config.project_dim	)
															lowerCamelCase__       =       nn.LayerNorm(config.hidden_size	,  eps=config.layer_norm_eps	)
										self.post_init()






					def 					__magic_name__ ( self	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  ):
										lowerCamelCase__       =       return_dict if return_dict is not None else self.config.use_return_dict

										lowerCamelCase__       =       self.base_model(
										    input_ids=_lowerCAmelCase	,  attention_mask=_lowerCAmelCase	,  token_type_ids=_lowerCAmelCase	,  position_ids=_lowerCAmelCase	,  head_mask=_lowerCAmelCase	,  inputs_embeds=_lowerCAmelCase	,  encoder_hidden_states=_lowerCAmelCase	,  encoder_attention_mask=_lowerCAmelCase	,  output_attentions=_lowerCAmelCase	,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,  return_dict=_lowerCAmelCase	,  )

										if self.has_pre_transformation:
															lowerCamelCase__       =       outputs["hidden_states"][-2]
															lowerCamelCase__       =       self.pre_LN(_lowerCAmelCase	)
															lowerCamelCase__       =       self.transformation_pre(_lowerCAmelCase	)

															return TransformationModelOutput(
															    projection_state=_lowerCAmelCase	,  last_hidden_state=outputs.last_hidden_state	,  hidden_states=outputs.hidden_states	,  attentions=outputs.attentions	,  )
										else:
															lowerCamelCase__       =       self.transformation(outputs.last_hidden_state	)
															return TransformationModelOutput(
															    projection_state=_lowerCAmelCase	,  last_hidden_state=outputs.last_hidden_state	,  hidden_states=outputs.hidden_states	,  attentions=outputs.attentions	,  )








def    __UpperCamelCase      (    a,     a) ->Dict:
					print("\nThe shortest path matrix using Floyd Warshall algorithm\n")
					for i in range(a):
										for j in range(a):
															if dist[i][j] != float("inf"):
																				print(int(dist[i][j]),     end="\t")
															else:
																				print("INF",     end="\t")
										print()





def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [[float("inf") for _ in range(a)] for _ in range(a)]

					for i in range(a):
										for j in range(a):
															lowerCamelCase__       =       graph[i][j]

															# check vertex k against all other vertices (i, j)
					for k in range(a):
										# looping through rows of graph array
										for i in range(a):
															# looping through columns of graph array
															for j in range(a):
																				if (
																				    dist[i][k] != float("inf")
																				    and dist[k][j] != float("inf")
																				    and dist[i][k] + dist[k][j] < dist[i][j]
																				):
																									lowerCamelCase__       =       dist[i][k] + dist[k][j]

					_print_dist(a,     a)
					return dist, v


if __name__ == "__main__":
						A_												=     int(input("Enter number of vertices: "))
						A_												=     int(input("Enter number of edges: "))

						A_												=     [[float("inf") for i in range(v)] for j in range(v)]

						for i in range(v):
												A_												=     0.0

												# src and dst are indices that must be within the array size graph[e][v]
												# failure to follow this will result in an error
						for i in range(e):
												print("\nEdge ", i + 1)
												A_												=     int(input("Enter source:"))
												A_												=     int(input("Enter destination:"))
												A_												=     float(input("Enter weight:"))
												A_												=     weight

						floyd_warshall(graph, v)

						# Example Input
						# Enter number of vertices: 3
						# Enter number of edges: 2

						# # generated graph from vertex and edge inputs
						# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
						# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

						# specify source, destination and weight for edge #1
						# Edge  1
						# Enter source:1
						# Enter destination:2
						# Enter weight:2

						# specify source, destination and weight for edge #2
						# Edge  2
						# Enter source:2
						# Enter destination:1
						# Enter weight:1

						# # Expected Output from the vertice, edge and src, dst, weight inputs!!
						# 0		INF	INF
						# INF	0	2
						# INF	1	0

