





from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def       _A  (      ):
   a__	:						List[str]      		=  9, 14  # noqa: F841
   a__	:						Tuple      		=  [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   a__	:						Dict      		=  defaultdict(lowerCamelCase   )
   for nodea, nodea, cost in edges:
      adjancency[nodea].append([nodea, cost]   )
      adjancency[nodea].append([nodea, cost]   )

   a__	:						List[str]      		=  mst(lowerCamelCase   )

   a__	:						Dict      		=  [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   for answer in expected:
      a__	:						str      		=  tuple(answer[:2]   )
      a__	:						Tuple      		=  tuple(edge[::-1]   )
      assert edge in result or reverse in result








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE__       :			Dict			      =						"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""







class   __lowerCAmelCase			(					unittest.TestCase		,_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Optional[int]      		=  load_tool("text-question-answering"      )
      self.tool.setup()
      a__	:						Dict      		=  load_tool("text-question-answering"					,			remote=snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  self.remote_tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Any      		=  self.tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						List[str]      		=  self.remote_tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )

