
from __future__ import annotations

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						Dict       ) ->       Any:

				"""simple docstring"""







				print(F"Vertex\tShortest Distance from vertex {src}"       )
				for i, d in enumerate(__UpperCamelCase       ):
								print(F"{i}\t\t{d}"       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						List[str]							,						__UpperCamelCase :						str       ) ->       List[Any]:

				"""simple docstring"""







				for j in range(__UpperCamelCase       ):
								A__       ,							A__       ,							A__  :	Any         =							(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
								if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
												return True
				return False

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						str							,						__UpperCamelCase :						Optional[int]       ) ->       int:

				"""simple docstring"""







				A__  :	Optional[int]         =							[float('''inf'''       )] * vertex_count
				A__  :	Tuple         =							0.0

				for _ in range(vertex_count - 1       ):
								for j in range(__UpperCamelCase       ):
												A__       ,							A__       ,							A__  :	Any         =							(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

												if distance[u] != float('''inf'''       ) and distance[u] + w < distance[v]:
																A__  :	int         =							distance[u] + w

				A__  :	Optional[Any]         =							check_negative_cycle(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )
				if negative_cycle_exists:
								raise Exception('''Negative cycle found'''       )

				return distance


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			int(input('Enter number of vertices: ').strip())
			_SCREAMING_SNAKE_CASE    :      List[str]          =			int(input('Enter number of edges: ').strip())

			_SCREAMING_SNAKE_CASE    :      list[dict[str, int]]          =			[{} for _ in range(E)]

			for i in range(E):
						print('Edge ', i + 1)
						_SCREAMING_SNAKE_CASE    :      int          =			(
						    int(x)
						    for x in input('Enter source, destination, weight: ').strip().split(' ')
						)
						_SCREAMING_SNAKE_CASE    :      Dict          =			{"""src""": src, """dst""": dest, """weight""": weight}

			_SCREAMING_SNAKE_CASE    :      Optional[int]          =			int(input('\nEnter shortest path source:').strip())
			_SCREAMING_SNAKE_CASE    :      Any          =			bellman_ford(graph, V, E, source)
			print_distance(shortest_distance, 0)

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

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

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids