





'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


UpperCAmelCase       :		Any		=				'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''





class lowerCamelCase    (a__       ):
  _lowercase	:						Tuple							       =					"""google/flan-t5-base"""
  _lowercase	:						List[Any]							       =					(
      """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
      """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
  )
  _lowercase	:						Optional[int]							       =					"""text_qa"""
  _lowercase	:						Union[str, Any]							       =					AutoTokenizer
  _lowercase	:						int							       =					AutoModelForSeqaSeqLM

  _lowercase	:						Any							       =					["""text""", """text"""]
  _lowercase	:						List[str]							       =					["""text"""]







  def 					UpperCAmelCase_					(		self  ,  lowercase__  ,  lowercase__		)		->    Any:






        """simple docstring"""





        _snake_case		:						Dict         =  QA_PROMPT.format(text=lowercase__  ,  question=lowercase__		)
        return self.pre_processor(lowercase__  ,  return_tensors='''pt'''		)







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    Any:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  self.model.generate(**lowercase__		)

        _snake_case  ,				_snake_case		:						Optional[int]         =  inputs['''input_ids'''].shape
        _snake_case		:						List[Any]         =  output_ids.shape[0]

        return output_ids.reshape(lowercase__  ,  out_b // in_b  ,  *output_ids.shape[1:]		)[0][0]







  def 					UpperCAmelCase_					(		self  ,  lowercase__		)		->    List[Any]:






        """simple docstring"""





        return self.pre_processor.decode(lowercase__  ,  skip_special_tokens=lowercase__  ,  clean_up_tokenization_spaces=lowercase__		)









'''simple docstring'''

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
UpperCAmelCase       :		List[Any]		=				{
    '1': ['2', '3', '4', '5'],
    '2': ['1', '3', '4', '5'],
    '3': ['1', '2', '4', '5', '10'],
    '4': ['1', '2', '3', '5', '6'],
    '5': ['1', '2', '3', '4', '7'],
    '6': ['7', '8', '9', '10', '4'],
    '7': ['6', '8', '9', '10', '5'],
    '8': ['6', '7', '9', '10'],
    '9': ['6', '7', '8', '10'],
    '10': ['6', '7', '8', '9', '3'],
}




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Dict         =  {node: {node} for node in graph}

      _snake_case		:						str         =  {node: graph[node][:] for node in graph}

      while len(lowerCAmelCase_						) > 2:
            # Choose a random edge.
            _snake_case		:						Optional[Any]         =  random.choice(list(graph_copy.keys()						)						)
            _snake_case		:						Union[str, Any]         =  random.choice(graph_copy[u]						)

            # Contract edge (u, v) to new node uv
            _snake_case		:						List[str]         =  u + v
            _snake_case		:						List[str]         =  list(set(graph_copy[u] + graph_copy[v]						)						)
            uv_neighbors.remove(lowerCAmelCase_						)
            uv_neighbors.remove(lowerCAmelCase_						)
            _snake_case		:						Tuple         =  uv_neighbors
            for neighbor in uv_neighbors:
                  graph_copy[neighbor].append(lowerCAmelCase_						)

            _snake_case		:						Tuple         =  set(contracted_nodes[u].union(contracted_nodes[v]						)						)

            # Remove nodes u and v.
            del graph_copy[u]
            del graph_copy[v]
            for neighbor in uv_neighbors:
                  if u in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(lowerCAmelCase_						)
                  if v in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(lowerCAmelCase_						)

    # Find cutset.
      _snake_case		:						Any         =  [contracted_nodes[node] for node in graph_copy]
      return {
          (node, neighbor)
          for node in groups[0]
          for neighbor in graph[node]
          if neighbor in groups[1]
      }


if __name__ == "__main__":
   print(partition_graph(TEST_GRAPH))


