





'''simple docstring'''

import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed





class lowerCamelCase    (a__       ):







  def __init__(		self  ,  lowercase__  ,  lowercase__  ,  lowercase__=False  ,  lowercase__=1_024  ,  lowercase__=1_024  ,  lowercase__=3.6  ,  lowercase__=False  ,  )		->    Any:






        """simple docstring"""





        _snake_case		:						Any         =  tokenizer
        _snake_case		:						List[Any]         =  tokenizer.bos_token_id
        _snake_case		:						Tuple         =  dataset
        _snake_case		:						Optional[Any]         =  seq_length
        _snake_case		:						Union[str, Any]         =  0
        _snake_case		:						Optional[int]         =  infinite
        _snake_case		:						Tuple         =  0
        _snake_case		:						List[Any]         =  tokenized

        if self.tokenized:
              _snake_case		:						Any         =  seq_length * num_of_sequences
              _snake_case		:						str         =  '''input_ids'''
        else:
              _snake_case		:						Optional[Any]         =  seq_length * chars_per_token * num_of_sequences
              _snake_case		:						int         =  '''content'''







  def __iter__(		self		)		->    Any:






        """simple docstring"""





        _snake_case		:						List[Any]         =  iter(self.dataset		)
        _snake_case		:						Optional[int]         =  True
        while more_examples:
              _snake_case  ,				_snake_case		:						Optional[Any]         =  [], 0
              while True:
                    if buffer_len >= self.max_buffer_size:
                          break
                    try:
                          buffer.append(next(lowercase__		)[self.content_field]		)
                          buffer_len += len(buffer[-1]		)
                    except StopIteration:
                          if self.infinite:
                                _snake_case		:						int         =  iter(self.dataset		)
                                self.epoch += 1
                                logger.info(F'''Dataset epoch: {self.epoch}'''		)
                          else:
                                _snake_case		:						Optional[int]         =  False
                                break
              if self.tokenized:
                    _snake_case		:						Union[str, Any]         =  buffer
              else:
                    _snake_case		:						Optional[Any]         =  self.tokenizer(lowercase__  ,  truncation=lowercase__		)['''input_ids''']
              _snake_case		:						Optional[Any]         =  []
              for tokenized_input in tokenized_inputs:
                    all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
              for i in range(0  ,  len(lowercase__		)  ,  self.seq_length		):
                    _snake_case		:						Optional[Any]         =  all_token_ids[i : i + self.seq_length]
                    if len(lowercase__		) == self.seq_length:
                          self.current_size += 1
                          yield torch.tensor(lowercase__		)







  def 					UpperCAmelCase_					(		self  ,  lowercase__=1_000		)		->    Optional[int]:






        """simple docstring"""





        return ShufflerIterDataPipe(self  ,  buffer_size=lowercase__		)




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Any         =  args.model_ckpt.split('''/'''						)[-1]
      _snake_case		:						Dict         =  logging.getLogger(__name__						)
      _snake_case		:						List[Any]         =  Path(args.save_dir						) / '''log/'''
      log_dir.mkdir(exist_ok=lowerCAmelCase_						)
      _snake_case		:						Any         =  f'''debug_{accelerator.process_index}.log'''
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''      ,   datefmt='''%m/%d/%Y %H:%M:%S'''      ,   level=logging.INFO      ,   handlers=[logging.FileHandler(log_dir / filename						), logging.StreamHandler()]      ,   )
      if accelerator.is_main_process:  # we only want to setup logging once
            accelerator.init_trackers(lowerCAmelCase_      ,   vars(lowerCAmelCase_						)						)
            _snake_case		:						Dict         =  accelerator.trackers[0].run.name
            logger.setLevel(logging.INFO						)
            datasets.utils.logging.set_verbosity_info()
            transformers.utils.logging.set_verbosity_info()
      else:
            _snake_case		:						Any         =  ''''''
            logger.setLevel(logging.ERROR						)
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      return logger, run_name




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Tuple         =  {'''streaming''': True}
      _snake_case		:						Optional[int]         =  load_dataset(args.dataset_name_train      ,   split='''train'''      ,   **lowerCAmelCase_						)
      _snake_case		:						Tuple         =  train_data.shuffle(buffer_size=args.shuffle_buffer      ,   seed=args.seed						)
      _snake_case		:						int         =  load_dataset(args.dataset_name_valid      ,   split='''train'''      ,   **lowerCAmelCase_						)
      _snake_case		:						str         =  ConstantLengthDataset(
          lowerCAmelCase_      ,   lowerCAmelCase_      ,   infinite=lowerCAmelCase_      ,   seq_length=args.seq_length      ,   tokenized=args.tokenized						)
      _snake_case		:						int         =  ConstantLengthDataset(
          lowerCAmelCase_      ,   lowerCAmelCase_      ,   infinite=lowerCAmelCase_      ,   seq_length=args.seq_length      ,   tokenized=args.tokenized						)
      _snake_case		:						str         =  train_dataset.shuffle(buffer_size=args.shuffle_buffer						)
      _snake_case		:						Optional[Any]         =  DataLoader(lowerCAmelCase_      ,   batch_size=args.train_batch_size      ,   shuffle=lowerCAmelCase_						)
      _snake_case		:						Dict         =  DataLoader(lowerCAmelCase_      ,   batch_size=args.valid_batch_size						)
      return train_dataloader, eval_dataloader




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						):





      """simple docstring"""



      _snake_case  ,				_snake_case		:						Optional[int]         =  [], []
      for n, p in model.named_parameters():
            if any(nd in n for nd in no_decay						):
                  params_without_wd.append(lowerCAmelCase_						)
            else:
                  params_with_wd.append(lowerCAmelCase_						)
      return [
          {"params": params_with_wd, "weight_decay": args.weight_decay},
          {"params": params_without_wd, "weight_decay": 0.0},
      ]




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      logger.info(f'''Step {step}: {metrics}'''						)
      if accelerator.is_main_process:
            accelerator.log(lowerCAmelCase_      ,   lowerCAmelCase_						)




def  _a     (				lowerCAmelCase_      ,   lowerCAmelCase_      ,   lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Tuple         =  accelerator.unwrap_model(lowerCAmelCase_						).config
      _snake_case		:						List[str]         =  4 if args.gradient_checkpointing else 3
      _snake_case		:						Optional[int]         =  args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
      _snake_case		:						List[Any]         =  24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
      _snake_case		:						Tuple         =  factor * (
          1.0
          + (args.seq_length / (6.0 * config_model.n_embd))
          + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
      )
      _snake_case		:						Optional[Any]         =  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
      return tflops




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      model.eval()
      _snake_case		:						Union[str, Any]         =  []
      for step, batch in enumerate(lowerCAmelCase_						):
            with torch.no_grad():
                  _snake_case		:						Optional[int]         =  model(lowerCAmelCase_      ,   labels=lowerCAmelCase_						)
            _snake_case		:						Optional[int]         =  outputs.loss.repeat(args.valid_batch_size						)
            losses.append(accelerator.gather(lowerCAmelCase_						)						)
            if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                  break
      _snake_case		:						Any         =  torch.cat(lowerCAmelCase_						)
      _snake_case		:						str         =  losses[: eval_dataloader.dataset.current_size].mean()
      try:
            _snake_case		:						Any         =  torch.exp(lowerCAmelCase_						)
      except OverflowError:
            _snake_case		:						List[str]         =  float('''inf'''						)
      return loss.item(), perplexity.item()


# Settings
UpperCAmelCase       :		Tuple		=				HfArgumentParser(TrainingArguments)
UpperCAmelCase       :		Any		=				parser.parse_args()

# Accelerator
UpperCAmelCase       :		Tuple		=				ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
UpperCAmelCase       :		Tuple		=				Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
UpperCAmelCase       :		Optional[Any]		=				{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

UpperCAmelCase       :		str		=				Namespace(**vars(args), **acc_state)
UpperCAmelCase       :		int		=				accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
   UpperCAmelCase       :		Dict		=				Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
UpperCAmelCase,							UpperCAmelCase       :		List[Any]		=				setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
   hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
UpperCAmelCase       :		Any		=				AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
   model.gradient_checkpointing_enable()
UpperCAmelCase       :		Optional[int]		=				AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCAmelCase,							UpperCAmelCase       :		str		=				create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase       :		Optional[Any]		=				AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase       :		int		=				get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)




def  _a     (				):





      """simple docstring"""



      return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase       :		Optional[int]		=				accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
   if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
      accelerator.load_state(args.resume_from_checkpoint)
      UpperCAmelCase       :		List[Any]		=				os.path.basename(args.resume_from_checkpoint)
   else:
      # Get the most recent checkpoint
      UpperCAmelCase       :		Dict		=				[f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
      dirs.sort(key=os.path.getctime)
      UpperCAmelCase       :		Optional[Any]		=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
   # Extract the step of the checkpoint to continue from there
   UpperCAmelCase       :		Optional[int]		=				os.path.splitext(path)[0]
   UpperCAmelCase       :		Optional[Any]		=				int(training_difference.replace('step_', ''))

# Train model
model.train()
UpperCAmelCase       :		str		=				0
UpperCAmelCase       :		Optional[Any]		=				time.time()
UpperCAmelCase       :		str		=				0
for step, batch in enumerate(train_dataloader, start=1):
   if args.resume_from_checkpoint and step < resume_step:
      continue  # we need to skip steps until we reach the resumed step
   UpperCAmelCase       :		Union[str, Any]		=				model(batch, labels=batch, use_cache=False).loss
   UpperCAmelCase       :		Dict		=				accelerator.gather(loss.repeat(args.train_batch_size)).mean()
   loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
   log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
   UpperCAmelCase       :		Union[str, Any]		=				loss / args.gradient_accumulation_steps
   if step % args.gradient_accumulation_steps != 0:
      # Prevent backward from doing gradient all_reduce in every step
      if accelerator.distributed_type == DistributedType.MULTI_GPU:
         with model.no_sync():
            accelerator.backward(loss)
      else:
         accelerator.backward(loss)
   else:
      UpperCAmelCase       :		str		=				get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      UpperCAmelCase       :		Union[str, Any]		=				time.time() - t_start
      UpperCAmelCase       :		Dict		=				compute_tflops(elapsed_time, accelerator, args)
      log_metrics(
          step,
          {
              'steps': completed_steps,
              'loss/train': loss_tracking,
              'lr': lr,
              'tflops': tflops,
              'time_per_iteration': elapsed_time,
          },
      )
      UpperCAmelCase       :		Union[str, Any]		=				time.time()
      UpperCAmelCase       :		Tuple		=				0
      completed_steps += 1
   if step % args.save_checkpoint_steps == 0:
      logger.info('Evaluating and saving model checkpoint')
      UpperCAmelCase,							UpperCAmelCase       :		Any		=				evaluate(args)
      log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
      accelerator.wait_for_everyone()
      UpperCAmelCase       :		List[Any]		=				os.path.join(args.save_dir, F"""step_{step}""")
      accelerator.save_state(save_dir)
      if accelerator.is_main_process:
         hf_repo.push_to_hub(commit_message=F"""step {step}""")
      model.train()
   if completed_steps >= args.max_train_steps:
      break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
UpperCAmelCase,							UpperCAmelCase       :		str		=				evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
UpperCAmelCase       :		int		=				accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
UpperCAmelCase       :		str		=				os.path.join(args.save_dir, F"""step_{step}""")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
   hf_repo.push_to_hub(commit_message='final model')









'''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))


