


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 				__SCREAMING_SNAKE_CASE   (			A__   ):


      def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=1024							,						SCREAMING_SNAKE_CASE__=1024							,						SCREAMING_SNAKE_CASE__=3.6							,						SCREAMING_SNAKE_CASE__=False							,						):
            lowercase			:   List[Any]					   =		tokenizer
            lowercase			:   Union[str, Any]					   =		tokenizer.bos_token_id
            lowercase			:   List[str]					   =		dataset
            lowercase			:   List[str]					   =		seq_length
            lowercase			:   List[str]					   =		0
            lowercase			:   Optional[int]					   =		infinite
            lowercase			:   Dict					   =		0
            lowercase			:   Dict					   =		tokenized

            if self.tokenized:
                  lowercase			:   Union[str, Any]					   =		seq_length * num_of_sequences
                  lowercase			:   str					   =		'''input_ids'''
            else:
                  lowercase			:   List[str]					   =		seq_length * chars_per_token * num_of_sequences
                  lowercase			:   Any					   =		'''content'''


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






      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=1000		):
            return ShufflerIterDataPipe(self							,						buffer_size=SCREAMING_SNAKE_CASE__		)





def 			__lowercase	(						_UpperCamelCase     )    ->Dict:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		args.model_ckpt.split('''/'''     )[-1]
      lowercase			:   Optional[Any]					   =		logging.getLogger(__name__     )
      lowercase			:   Tuple					   =		Path(args.save_dir     ) / '''log/'''
      log_dir.mkdir(exist_ok=_UpperCamelCase     )
      lowercase			:   Dict					   =		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     )     )
            lowercase			:   Tuple					   =		accelerator.trackers[0].run.name
            logger.setLevel(logging.INFO     )
            datasets.utils.logging.set_verbosity_info()
            transformers.utils.logging.set_verbosity_info()
      else:
            lowercase			:   Optional[Any]					   =		''''''
            logger.setLevel(logging.ERROR     )
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      return logger, run_name





def 			__lowercase	(						_UpperCamelCase     )    ->Optional[int]:







      """simple docstring"""

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





def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]     )    ->Optional[int]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		[], []
      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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->int:







      """simple docstring"""

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





def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->Union[str, Any]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		accelerator.unwrap_model(_UpperCamelCase     ).config
      lowercase			:   Optional[int]					   =		4 if args.gradient_checkpointing else 3
      lowercase			:   Any					   =		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
      lowercase			:   Any					   =		24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
      lowercase			:   Dict					   =		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))
      )
      lowercase			:   Optional[Any]					   =		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
      return tflops





def 			__lowercase	(						_UpperCamelCase     )    ->str:







      """simple docstring"""

      model.eval()
      lowercase			:   Dict					   =		[]
      for step, batch in enumerate(_UpperCamelCase     ):
            with torch.no_grad():
                  lowercase			:   int					   =		model(_UpperCamelCase,    labels=_UpperCamelCase     )
            lowercase			:   Union[str, Any]					   =		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
      lowercase			:   Any					   =		torch.cat(_UpperCamelCase     )
      lowercase			:   Optional[Any]					   =		losses[: eval_dataloader.dataset.current_size].mean()
      try:
            lowercase			:   Tuple					   =		torch.exp(_UpperCamelCase     )
      except OverflowError:
            lowercase			:   Union[str, Any]					   =		float('''inf'''     )
      return loss.item(), perplexity.item()


# Settings
__a   				=      HfArgumentParser(TrainingArguments)
__a   				=      parser.parse_args()

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

__a   				=      Namespace(**vars(args), **acc_state)
__a   				=      accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
__a				,      __a   				=      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
__a   				=      AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
  model.gradient_checkpointing_enable()
__a   				=      AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__a				,      __a   				=      create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__a   				=      AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__a   				=      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 			__lowercase	(						)    ->Optional[Any]:







      """simple docstring"""

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


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

# Train model
model.train()
__a   				=      0
__a   				=      time.time()
__a   				=      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
  __a   				=      model(batch, labels=batch, use_cache=False).loss
  __a   				=      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()})
  __a   				=      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:
    __a   				=      get_lr()
    accelerator.backward(loss)
    accelerator.clip_grad_norm_(model.parameters(), 1.0)
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()
    __a   				=      time.time() - t_start
    __a   				=      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,
        },
    )
    __a   				=      time.time()
    __a   				=      0
    completed_steps += 1
  if step % args.save_checkpoint_steps == 0:
    logger.info('''Evaluating and saving model checkpoint''')
    __a				,      __a   				=      evaluate(args)
    log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
    accelerator.wait_for_everyone()
    __a   				=      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''')
__a				,      __a   				=      evaluate(args)
log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
accelerator.wait_for_everyone()
__a   				=      accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__a   				=      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 argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__a   				=      logging.getLogger(__name__)




def 			__lowercase	(						_UpperCamelCase     )    ->Any:







      """simple docstring"""

      np.random.seed(args.seed     )
      torch.manual_seed(args.seed     )
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed     )




def 			__lowercase	(						)    ->List[str]:







      """simple docstring"""

      lowercase			:   List[str]					   =		argparse.ArgumentParser()
      parser.add_argument(
          '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
      parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
      parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
      parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
      parser.add_argument(
          '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
      parser.add_argument(
          '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
      parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
      parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
      parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )

      parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
      parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
      parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )

      parser.add_argument('''--seed''',    type=_UpperCamelCase,    default=42,    help='''random seed for initialization'''     )
      parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''     )
      parser.add_argument(
          '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
      lowercase			:   Any					   =		parser.parse_args()

      lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
      lowercase			:   List[str]					   =		0 if args.no_cuda else torch.cuda.device_count()

      logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )

      set_seed(_UpperCamelCase     )

      # Initialize the model and tokenizer
      lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
      lowercase			:   List[Any]					   =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )

      # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
      # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
      model.to(args.device     )

      if args.fpaa:
            model.half()

      logger.info(_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )

      lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}

      lowercase			:   Optional[int]					   =		model.generate(
          **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )

      lowercase			:   Union[str, Any]					   =		[]
      for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
            print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
            lowercase			:   Optional[Any]					   =		generated_sequence.tolist()

            # Decode text
            lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )

            # Remove all text after the stop token
            lowercase			:   Optional[int]					   =		text[: text.find(args.stop_token     ) if args.stop_token else None]

            # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
            lowercase			:   Optional[Any]					   =		(
                prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
            )

            generated_sequences.append(_UpperCamelCase     )
            print(_UpperCamelCase     )

      return generated_sequences


if __name__ == "__main__":
  main()

