





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase_    =				logging.get_logger(__name__)


lowerCAmelCase_    =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase_    =				{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase_    =				{
    '''RUCAIBox/mvp''': 1_0_2_4,
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Optional[Any]       			=      VOCAB_FILES_NAMES
			lowerCamelCase_     :     List[str]       			=      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_     :     Tuple       			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_     :     List[Any]       			=      ['''input_ids''', '''attention_mask''']
			lowerCamelCase_     :     Any       			=      MvpTokenizer






			def __init__(self					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__="replace"					,							__magic_name__="<s>"					,							__magic_name__="</s>"					,							__magic_name__="</s>"					,							__magic_name__="<s>"					,							__magic_name__="<unk>"					,							__magic_name__="<pad>"					,							__magic_name__="<mask>"					,							__magic_name__=False					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Optional[Any]:


							'''simple docstring'''



							super().__init__(
							    __magic_name__					,							__magic_name__					,							tokenizer_file=__magic_name__					,							errors=__magic_name__					,							bos_token=__magic_name__					,							eos_token=__magic_name__					,							sep_token=__magic_name__					,							cls_token=__magic_name__					,							unk_token=__magic_name__					,							pad_token=__magic_name__					,							mask_token=__magic_name__					,							add_prefix_space=__magic_name__					,							trim_offsets=__magic_name__					,							**__magic_name__					,							)

							snake_case_						:    Union[str, Any]				=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
							if pre_tok_state.get('''add_prefix_space'''					,							__magic_name__    ) != add_prefix_space:
											snake_case_						:    List[Any]				=  getattr(__magic_name__					,							pre_tok_state.pop('''type'''    )    )
											snake_case_						:    Any				=  add_prefix_space
											snake_case_						:    List[str]				=  pre_tok_class(**__magic_name__    )

							snake_case_						:    Optional[Any]				=  add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							snake_case_						:    List[str]				=  '''post_processor'''
							snake_case_						:    Union[str, Any]				=  getattr(self.backend_tokenizer					,							__magic_name__					,							__magic_name__    )
							if tokenizer_component_instance:
											snake_case_						:    Union[str, Any]				=  json.loads(tokenizer_component_instance.__getstate__()    )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															snake_case_						:    Optional[Any]				=  tuple(state['''sep''']    )
											if "cls" in state:
															snake_case_						:    Tuple				=  tuple(state['''cls''']    )

											snake_case_						:    Union[str, Any]				=  False

											if state.get('''add_prefix_space'''					,							__magic_name__    ) != add_prefix_space:
															snake_case_						:    Union[str, Any]				=  add_prefix_space
															snake_case_						:    Tuple				=  True

											if state.get('''trim_offsets'''					,							__magic_name__    ) != trim_offsets:
															snake_case_						:    Dict				=  trim_offsets
															snake_case_						:    Union[str, Any]				=  True

											if changes_to_apply:
															snake_case_						:    int				=  getattr(__magic_name__					,							state.pop('''type'''    )    )
															snake_case_						:    Optional[Any]				=  component_class(**__magic_name__    )
															setattr(self.backend_tokenizer					,							__magic_name__					,							__magic_name__    )






			@property
			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							if self._mask_token is None:
											if self.verbose:
															logger.error('''Using mask_token, but it is not set yet.'''    )
											return None
							return str(self._mask_token    )






			@mask_token.setter
			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  AddedToken(__magic_name__					,							lstrip=__magic_name__					,							rstrip=__magic_name__    ) if isinstance(__magic_name__					,							__magic_name__    ) else value
							snake_case_						:    Optional[int]				=  value






			def 			lowerCamelCase  (self					,							*__magic_name__					,							**__magic_name__    )     ->   BatchEncoding:


							'''simple docstring'''



							snake_case_						:    List[str]				=  kwargs.get('''is_split_into_words'''					,							__magic_name__    )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''    )

							return super()._batch_encode_plus(*__magic_name__					,							**__magic_name__    )






			def 			lowerCamelCase  (self					,							*__magic_name__					,							**__magic_name__    )     ->   BatchEncoding:


							'''simple docstring'''



							snake_case_						:    Tuple				=  kwargs.get('''is_split_into_words'''					,							__magic_name__    )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    '''to use it with pretokenized inputs.'''    )

							return super()._encode_plus(*__magic_name__					,							**__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None    )     ->   Tuple[str]:


							'''simple docstring'''



							snake_case_						:    Any				=  self._tokenizer.model.save(__magic_name__					,							name=__magic_name__    )
							return tuple(__magic_name__    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__=None    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = None    )     ->   List[int]:


							'''simple docstring'''



							snake_case_						:    str				=  [self.sep_token_id]
							snake_case_						:    Any				=  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep    ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]







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
