


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=params

         __UpperCamelCase    							=np.array(A_    )
         __UpperCamelCase    							=np.array([len(A_    ) for t in data]    )

         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
         self.remove_unknown_sequences()
         self.check()
         self.print_statistics()






    def __getitem__(			self					,   A_    )  ->       List[str]:
         return (self.token_ids[index], self.lengths[index])






    def __len__(			self    )  ->       Optional[Any]:
         return len(self.lengths    )






    def _a					(			self    )  ->       Any:
         assert len(self.token_ids    ) == len(self.lengths    )
         assert all(self.lengths[i] == len(self.token_ids[i]    ) for i in range(len(self.lengths    )    )    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.params.max_model_input_size
         __UpperCamelCase    							=self.lengths > max_len
         logger.info(f'Splitting {sum(A_    )} too long sequences.'    )

         def divide_chunks(A_					,   A_    ):
              return [l[i : i + n] for i in range(0					,   len(A_    )					,   A_    )]

         __UpperCamelCase    							=[]
         __UpperCamelCase    							=[]
         if self.params.mlm:
              __UpperCamelCase							,      __UpperCamelCase    							=self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
         else:
              __UpperCamelCase							,      __UpperCamelCase    							=self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

         for seq_, len_ in zip(self.token_ids					,   self.lengths    ):
              assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
              if len_ <= max_len:
                   new_tok_ids.append(seq_    )
                   new_lengths.append(len_    )
              else:
                   __UpperCamelCase    							=[]
                   for sub_s in divide_chunks(seq_					,   max_len - 2    ):
                        if sub_s[0] != cls_id:
                             __UpperCamelCase    							=np.insert(A_					,   0					,   A_    )
                        if sub_s[-1] != sep_id:
                             __UpperCamelCase    							=np.insert(A_					,   len(A_    )					,   A_    )
                        assert len(A_    ) <= max_len
                        assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                        sub_seqs.append(A_    )

                   new_tok_ids.extend(A_    )
                   new_lengths.extend([len(A_    ) for l in sub_seqs]    )

         __UpperCamelCase    							=np.array(A_    )
         __UpperCamelCase    							=np.array(A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=len(self    )
         __UpperCamelCase    							=self.lengths > 11
         __UpperCamelCase    							=self.token_ids[indices]
         __UpperCamelCase    							=self.lengths[indices]
         __UpperCamelCase    							=len(self    )
         logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'    )






    def _a					(			self    )  ->       int:
         if "unk_token" not in self.params.special_tok_ids:
              return
         else:
              __UpperCamelCase    							=self.params.special_tok_ids['unk_token']
         __UpperCamelCase    							=len(self    )
         __UpperCamelCase    							=np.array([np.count_nonzero(a == unk_token_id    ) for a in self.token_ids]    )
         __UpperCamelCase    							=(unk_occs / self.lengths) < 0.5
         __UpperCamelCase    							=self.token_ids[indices]
         __UpperCamelCase    							=self.lengths[indices]
         __UpperCamelCase    							=len(self    )
         logger.info(f'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'    )






    def _a					(			self    )  ->       int:
         if not self.params.is_master:
              return
         logger.info(f'{len(self    )} sequences'    )
         # data_len = sum(self.lengths)
         # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
         # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

         # unk_idx = self.params.special_tok_ids['unk_token']
         # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






    def _a					(			self					,   A_    )  ->       int:
         __UpperCamelCase    							=[t[0] for t in batch]
         __UpperCamelCase    							=[t[1] for t in batch]
         assert len(A_    ) == len(A_    )

         # Max for paddings
         __UpperCamelCase    							=max(A_    )

         # Pad token ids
         if self.params.mlm:
              __UpperCamelCase    							=self.params.special_tok_ids['pad_token']
         else:
              __UpperCamelCase    							=self.params.special_tok_ids['unk_token']
         __UpperCamelCase    							=[list(t.astype(A_    )    ) + [pad_idx] * (max_seq_len_ - len(A_    )) for t in token_ids]
         assert len(tk_    ) == len(A_    )
         assert all(len(A_    ) == max_seq_len_ for t in tk_    )

         __UpperCamelCase    							=torch.tensor(tk_    )  # (bs, max_seq_len_)
         __UpperCamelCase    							=torch.tensor(A_    )  # (bs)
         return tk_t, lg_t












def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						List[str]:






				"""simple docstring"""


				print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
				for i in range(_UpperCamelCase ):
								for j in range(_UpperCamelCase ):
												if dist[i][j] != float('''inf''' ):
																print(int(dist[i][j] ) ,				end='''\t''' )
												else:
																print('''INF''' ,				end='''\t''' )
								print()






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:    int				=  [[float('''inf''' ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase )]

				for i in range(_UpperCamelCase ):
								for j in range(_UpperCamelCase ):
												snake_case_						:    Dict				=  graph[i][j]

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

				_print_dist(_UpperCamelCase ,				_UpperCamelCase )
				return dist, v


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

	lowerCAmelCase_    =				[[float('''inf''') for i in range(v)] for j in range(v)]

	for i in range(v):
		lowerCAmelCase_    =				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)
		lowerCAmelCase_    =				int(input('''Enter source:'''))
		lowerCAmelCase_    =				int(input('''Enter destination:'''))
		lowerCAmelCase_    =				float(input('''Enter weight:'''))
		lowerCAmelCase_    =				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
