





'''simple docstring'''

import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
   print('#################### AVAILABLE DEVICES ####################')
   print(jax.devices())
   print('###########################################################')

   # setup for wandb sweep
   UpperCAmelCase       :		Optional[int]		=				Args()
   UpperCAmelCase       :		Any		=				wandb.init(project='bigbird-natural-questions', config=args.__dict__)
   UpperCAmelCase       :		int		=				dict(logger.config)
   del wandb_args["batch_size"]
   UpperCAmelCase       :		Any		=				replace(args, **wandb_args)
   UpperCAmelCase       :		Union[str, Any]		=				args.base_dir + '-' + wandb.run.id
   UpperCAmelCase       :		List[str]		=				replace(args, base_dir=base_dir)
   print(args)

   UpperCAmelCase       :		int		=				load_dataset('json', data_files=args.tr_data_path)['train']
   UpperCAmelCase       :		Any		=				load_dataset('json', data_files=args.val_data_path)['train']

   # drop extra batch for now
   UpperCAmelCase       :		int		=				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   UpperCAmelCase       :		Optional[Any]		=				tr_dataset.shuffle().select(indices)
   UpperCAmelCase       :		Any		=				range(len(val_dataset) - len(val_dataset) % args.batch_size)
   UpperCAmelCase       :		Any		=				val_dataset.shuffle().select(indices)

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      UpperCAmelCase       :		str		=				tr_dataset.shuffle().select(range(8_0_0_0_0))
      UpperCAmelCase       :		Optional[Any]		=				val_dataset.shuffle().select(range(8_0_0_0))

   print(tr_dataset)
   print(val_dataset)

   UpperCAmelCase       :		Tuple		=				FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   UpperCAmelCase       :		List[str]		=				BigBirdTokenizerFast.from_pretrained(args.model_id)
   UpperCAmelCase       :		str		=				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

   UpperCAmelCase       :		List[str]		=				{
       'lr': args.lr,
       'init_lr': args.init_lr,
       'warmup_steps': args.warmup_steps,
       'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
       'weight_decay': args.weight_decay,
   }
   UpperCAmelCase,							UpperCAmelCase       :		str		=				build_tx(**tx_args)

   UpperCAmelCase       :		Tuple		=				Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )

   UpperCAmelCase       :		str		=				None
   UpperCAmelCase       :		Optional[int]		=				trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
   try:
      trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
      print('Oooops; TRAINING STOPPED UNFORTUNATELY')

   print('SAVING WEIGHTS IN `final-weights`')
   UpperCAmelCase       :		List[Any]		=				jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)









'''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))


