




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_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def __init__(							self						:	Optional[Any]       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	int       ,       _lowerCAmelCase						:	Union[str, Any]=False       ,       _lowerCAmelCase						:	List[Any]=1_024       ,       _lowerCAmelCase						:	Any=1_024       ,       _lowerCAmelCase						:	Optional[int]=3.6       ,       _lowerCAmelCase						:	Any=False       ,       ):
       SCREAMING_SNAKE_CASE_					=    tokenizer
       SCREAMING_SNAKE_CASE_					=    tokenizer.bos_token_id
       SCREAMING_SNAKE_CASE_					=    dataset
       SCREAMING_SNAKE_CASE_					=    seq_length
       SCREAMING_SNAKE_CASE_					=    0
       SCREAMING_SNAKE_CASE_					=    infinite
       SCREAMING_SNAKE_CASE_					=    0
       SCREAMING_SNAKE_CASE_					=    tokenized

       if self.tokenized:
             SCREAMING_SNAKE_CASE_					=    seq_length * num_of_sequences
             SCREAMING_SNAKE_CASE_					=    'input_ids'
       else:
             SCREAMING_SNAKE_CASE_					=    seq_length * chars_per_token * num_of_sequences
             SCREAMING_SNAKE_CASE_					=    'content'



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




 def    lowerCAmelCase_						(							self						:	Tuple       ,       _lowerCAmelCase						:	Union[str, Any]=1_000		):
       return ShufflerIterDataPipe(self       ,       buffer_size=_lowerCAmelCase		)


def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    args.model_ckpt.split('/'							)[-1]
      SCREAMING_SNAKE_CASE_					=    logging.getLogger(__name__							)
      SCREAMING_SNAKE_CASE_					=    Path(args.save_dir							) / 'log/'
      log_dir.mkdir(exist_ok=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    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(__UpperCAmelCase    ,    vars(__UpperCAmelCase							)							)
            SCREAMING_SNAKE_CASE_					=    accelerator.trackers[0].run.name
            logger.setLevel(logging.INFO							)
            datasets.utils.logging.set_verbosity_info()
            transformers.utils.logging.set_verbosity_info()
      else:
            SCREAMING_SNAKE_CASE_					=    ''
            logger.setLevel(logging.ERROR							)
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      return logger, run_name

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    {'streaming': True}
      SCREAMING_SNAKE_CASE_					=    load_dataset(args.dataset_name_train    ,    split='train'    ,    **__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    train_data.shuffle(buffer_size=args.shuffle_buffer    ,    seed=args.seed							)
      SCREAMING_SNAKE_CASE_					=    load_dataset(args.dataset_name_valid    ,    split='train'    ,    **__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    ConstantLengthDataset(
          __UpperCAmelCase    ,    __UpperCAmelCase    ,    infinite=__UpperCAmelCase    ,    seq_length=args.seq_length    ,    tokenized=args.tokenized							)
      SCREAMING_SNAKE_CASE_					=    ConstantLengthDataset(
          __UpperCAmelCase    ,    __UpperCAmelCase    ,    infinite=__UpperCAmelCase    ,    seq_length=args.seq_length    ,    tokenized=args.tokenized							)
      SCREAMING_SNAKE_CASE_					=    train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
      SCREAMING_SNAKE_CASE_					=    DataLoader(__UpperCAmelCase    ,    batch_size=args.train_batch_size    ,    shuffle=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    DataLoader(__UpperCAmelCase    ,    batch_size=args.valid_batch_size							)
      return train_dataloader, eval_dataloader

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    [], []
      for n, p in model.named_parameters():
            if any(nd in n for nd in no_decay							):
                  params_without_wd.append(__UpperCAmelCase							)
            else:
                  params_with_wd.append(__UpperCAmelCase							)
      return [
          {"params": params_with_wd, "weight_decay": args.weight_decay},
          {"params": params_without_wd, "weight_decay": 0.0},
      ]

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			Any							)      ->				Dict:
      logger.info(f"Step {step}: {metrics}"							)
      if accelerator.is_main_process:
            accelerator.log(__UpperCAmelCase    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			int							)      ->				Dict:
      # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
      SCREAMING_SNAKE_CASE_					=    accelerator.unwrap_model(__UpperCAmelCase							).config
      SCREAMING_SNAKE_CASE_					=    4 if args.gradient_checkpointing else 3
      SCREAMING_SNAKE_CASE_					=    args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
      SCREAMING_SNAKE_CASE_					=    24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
      SCREAMING_SNAKE_CASE_					=    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))
      )
      SCREAMING_SNAKE_CASE_					=    flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
      return tflops

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


# Settings
lowerCamelCase__				:     str			       =							HfArgumentParser(TrainingArguments)
lowerCamelCase__				:     Any			       =							parser.parse_args()

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

lowerCamelCase__				:     Optional[Any]			       =							Namespace(**vars(args), **acc_state)
lowerCamelCase__				:     int			       =							accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
lowerCamelCase__				:     Union[str, 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
lowerCamelCase__				:     Optional[int]			       =							AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
   model.gradient_checkpointing_enable()
lowerCamelCase__				:     Dict			       =							AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase__				:     List[Any]			       =							create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase__				:     List[Any]			       =							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase__				:     List[Any]			       =							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       UpperCAmelCase_							(   )      ->				Optional[int]:
      return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCamelCase__				:     Any			       =							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)
      lowerCamelCase__				:     Dict			       =							os.path.basename(args.resume_from_checkpoint)
   else:
      # Get the most recent checkpoint
      lowerCamelCase__				:     List[str]			       =							[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)
      lowerCamelCase__				:     Optional[int]			       =							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
   # Extract the step of the checkpoint to continue from there
   lowerCamelCase__				:     Any			       =							os.path.splitext(path)[0]
   lowerCamelCase__				:     List[Any]			       =							int(training_difference.replace('step_', ''))

# Train model
model.train()
lowerCamelCase__				:     str			       =							0
lowerCamelCase__				:     Dict			       =							time.time()
lowerCamelCase__				:     int			       =							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
   lowerCamelCase__				:     Union[str, Any]			       =							model(batch, labels=batch, use_cache=False).loss
   lowerCamelCase__				:     List[str]			       =							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()})
   lowerCamelCase__				:     List[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:
      lowerCamelCase__				:     Optional[int]			       =							get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      lowerCamelCase__				:     int			       =							time.time() - t_start
      lowerCamelCase__				:     int			       =							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,
          },
      )
      lowerCamelCase__				:     Union[str, Any]			       =							time.time()
      lowerCamelCase__				:     Optional[int]			       =							0
      completed_steps += 1
   if step % args.save_checkpoint_steps == 0:
      logger.info('Evaluating and saving model checkpoint')
      lowerCamelCase__				:     Tuple			       =							evaluate(args)
      log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
      accelerator.wait_for_everyone()
      lowerCamelCase__				:     Optional[int]			       =							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')
lowerCamelCase__				:     Optional[Any]			       =							evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
lowerCamelCase__				:     List[Any]			       =							accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase__				:     int			       =							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')












import bsa
import requests

def       UpperCAmelCase_							(   __UpperCAmelCase		:			bsa.element.ResultSet							)      ->				dict[str, str]:
      return {
          "name": soup.ha.a.text,
          "genre": soup.find('span'    ,    class_='genre'							).text.strip(),
          "rating": soup.strong.text,
          "page_link": f"https://www.imdb.com{soup.a.get('href'							)}",
      }

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int = 5							)      ->				tuple:
      SCREAMING_SNAKE_CASE_					=    int(float(__UpperCAmelCase							)							)
      if num_movies < 1:
            return ()
      SCREAMING_SNAKE_CASE_					=    (
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      SCREAMING_SNAKE_CASE_					=    bsa.BeautifulSoup(requests.get(__UpperCAmelCase							).content    ,    'html.parser'							)
      return tuple(
          get_movie_data_from_soup(__UpperCAmelCase							)
          for movie in source.find_all('div'    ,    class_='lister-item mode-advanced'							)							)


if __name__ == "__main__":
   import json

   lowerCamelCase__				:     int			       =							int(input('How many movies would you like to see? '))
   print(
       ', '.join(
           json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
       )
   )