
'''simple docstring'''







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Optional[int]:

	'''simple docstring'''





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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			str:

	'''simple docstring'''





	_snake_case     		=	[[float('inf'			) for _ in range(_lowerCamelCase			)] for _ in range(_lowerCamelCase			)]

	for i in range(_lowerCamelCase			):
		for j in range(_lowerCamelCase			):
			_snake_case     		=	graph[i][j]

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

	_print_dist(_lowerCamelCase    ,       _lowerCamelCase			)
	return dist, v


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

					lowercase					:							Optional[int]   =      [[float("inf") for i in range(v)] for j in range(v)]

					for i in range(v):
										lowercase					:							Union[str, Any]   =      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)
										lowercase					:							Dict   =      int(input("Enter source:"))
										lowercase					:							str   =      int(input("Enter destination:"))
										lowercase					:							int   =      float(input("Enter weight:"))
										lowercase					:							Any   =      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



'''simple docstring'''







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowercase					:							Optional[Any]   =      logging.get_logger(__name__)

lowercase					:							str   =      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase					:							Optional[Any]   =      {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowercase					:							List[Any]   =      {
    "facebook/blenderbot_small-90M": 512,
}


class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= VOCAB_FILES_NAMES
							__lowercase   	= PRETRAINED_VOCAB_FILES_MAP
							__lowercase   	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase   	= BlenderbotSmallTokenizer


							def __init__( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_=False					,     lowerCAmelCase_=True					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(
								    ByteLevelBPETokenizer(
								        vocab=lowerCAmelCase_					,     merges=lowerCAmelCase_					,     add_prefix_space=lowerCAmelCase_					,     trim_offsets=lowerCAmelCase_					,     )					,     bos_token=lowerCAmelCase_					,     eos_token=lowerCAmelCase_					,     unk_token=lowerCAmelCase_					,     **lowerCAmelCase_					,     )
								_snake_case     		=	add_prefix_space


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								_snake_case     		=	[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					,     lowerCAmelCase_					,     lowerCAmelCase_ = None						):





								"""simple docstring"""






								_snake_case     		=	[self.sep_token_id]
								_snake_case     		=	[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]

