'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


UpperCamelCase    :			Dict						     =  logging.getLogger(__name__)



@dataclass
class    UpperCamelCase :







    """simple docstring"""





    A			:  str            =       field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
    A			:  Optional[str]            =       field(
        default=snake_case__  ,			metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
    A			:  Optional[str]            =       field(
        default=snake_case__  ,			metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
    A			:  bool            =       field(default=snake_case__  ,			metadata={"help": "Set this flag to use fast tokenization."}    )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    A			:  Optional[str]            =       field(
        default=snake_case__  ,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,			)






def SCREAMING_SNAKE_CASE__   (							)					->							Tuple:







     """simple docstring"""
     a :				str				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )

     if len(sys.argv   ) == 2 and sys.argv[1].endswith('.json'   ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          a				,       a				,       a :				Union[str, Any]				=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
     else:
          a				,       a				,       a :				Dict				=      parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir   )
         and os.listdir(training_args.output_dir   )
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              ' --overwrite_output_dir to overcome.'   )

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,     )
     logger.warning(
         'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,     training_args.local_rank							,     training_args.device							,     training_args.n_gpu							,     bool(training_args.local_rank != -1   )							,     training_args.fpaa							,     )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank   ):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'							,     lowerCAmelCase_   )

     # Prepare Question-Answering task
     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     a :				List[str]				=      AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							,     )
     a :				int				=      AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							,     use_fast=lowerCAmelCase_							,     )
     a :				List[Any]				=      AutoModelForQuestionAnswering.from_pretrained(
         model_args.model_name_or_path							,     from_tf=bool('.ckpt' in model_args.model_name_or_path   )							,     config=lowerCAmelCase_							,     cache_dir=model_args.cache_dir							,     )

     # Get datasets
     a :				Union[str, Any]				=      hasattr(model.config							,     'lang2id'   )
     a :				Dict				=      (
         SquadDataset(
             lowerCAmelCase_							,     tokenizer=lowerCAmelCase_							,     is_language_sensitive=lowerCAmelCase_							,     cache_dir=model_args.cache_dir   )
         if training_args.do_train
         else None
     )
     a :				Optional[int]				=      (
         SquadDataset(
             lowerCAmelCase_							,     tokenizer=lowerCAmelCase_							,     mode='dev'							,     is_language_sensitive=lowerCAmelCase_							,     cache_dir=model_args.cache_dir							,     )
         if training_args.do_eval
         else None
     )

     # Data collator
     a :				Optional[Any]				=      DataCollatorWithPadding(lowerCAmelCase_							,     pad_to_multiple_of=8   ) if training_args.fpaa else None

     # Initialize our Trainer
     a :				int				=      Trainer(
         model=lowerCAmelCase_							,     args=lowerCAmelCase_							,     train_dataset=lowerCAmelCase_							,     eval_dataset=lowerCAmelCase_							,     data_collator=lowerCAmelCase_							,     )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir   )






def SCREAMING_SNAKE_CASE__   (							snake_case    :							str   )					->							Optional[int]:







     """simple docstring"""
     main()


if __name__ == "__main__":
      main()






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
__A				    =						{
    '''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_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



						UpperCAmelCase_=   {node: {node} for node in graph}

						UpperCAmelCase_=   {node: graph[node][:] for node in graph}

						while len(lowerCAmelCase_				) > 2:
												# Choose a random edge.
												UpperCAmelCase_=   random.choice(list(graph_copy.keys()				)				)
												UpperCAmelCase_=   random.choice(graph_copy[u]				)

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

												UpperCAmelCase_=   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.
						UpperCAmelCase_=   [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))




