"""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 SCREAMING_SNAKE_CASE     (    __lowerCamelCase	):







 """simple docstring"""


 def __init__(   self		:      List[str]					,   _snake_case		:      List[str]					,   _snake_case		:      Optional[Any]					,   _snake_case		:      List[str]=False					,   _snake_case		:      str=1024					,   _snake_case		:      Dict=1024					,   _snake_case		:      Optional[Any]=3.6					,   _snake_case		:      Optional[Any]=False					,   )				->			Any:




    '''simple docstring'''






    a__     				=		tokenizer
    a__     				=		tokenizer.bos_token_id
    a__     				=		dataset
    a__     				=		seq_length
    a__     				=		0
    a__     				=		infinite
    a__     				=		0
    a__     				=		tokenized

    if self.tokenized:
       a__     				=		seq_length * num_of_sequences
       a__     				=		"""input_ids"""
    else:
       a__     				=		seq_length * chars_per_token * num_of_sequences
       a__     				=		"""content"""


 def __iter__(   self		:      List[str]				)				->			str:




    '''simple docstring'''






    a__     				=		iter(self.dataset				)
    a__     				=		True
    while more_examples:
       a__     				=		[], 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:
                a__     				=		iter(self.dataset				)
                self.epoch += 1
                logger.info(F'''Dataset epoch: {self.epoch}'''				)
             else:
                a__     				=		False
                break
       if self.tokenized:
          a__     				=		buffer
       else:
          a__     				=		self.tokenizer(_lowerCAmelCase					,   truncation=_lowerCAmelCase				)["""input_ids"""]
       a__     				=		[]
       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				):
          a__     				=		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		:      Union[str, Any]					,   _snake_case		:      int=1000				)				->			List[Any]:




    '''simple docstring'''






    return ShufflerIterDataPipe(self					,   buffer_size=_lowerCAmelCase				)





def   _lowerCamelCase ( UpperCAmelCase__					)							->     List[str]:


   '''simple docstring'''


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





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   a__     				=		{"""streaming""": True}
   a__     				=		load_dataset(args.dataset_name_train  ,split='train'  ,**__lowerCamelCase					)
   a__     				=		train_data.shuffle(buffer_size=args.shuffle_buffer  ,seed=args.seed					)
   a__     				=		load_dataset(args.dataset_name_valid  ,split='train'  ,**__lowerCamelCase					)
   a__     				=		ConstantLengthDataset(
       __lowerCamelCase  ,__lowerCamelCase  ,infinite=__lowerCamelCase  ,seq_length=args.seq_length  ,tokenized=args.tokenized					)
   a__     				=		ConstantLengthDataset(
       __lowerCamelCase  ,__lowerCamelCase  ,infinite=__lowerCamelCase  ,seq_length=args.seq_length  ,tokenized=args.tokenized					)
   a__     				=		train_dataset.shuffle(buffer_size=args.shuffle_buffer					)
   a__     				=		DataLoader(__lowerCamelCase  ,batch_size=args.train_batch_size  ,shuffle=__lowerCamelCase					)
   a__     				=		DataLoader(__lowerCamelCase  ,batch_size=args.valid_batch_size					)
   return train_dataloader, eval_dataloader





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


   '''simple docstring'''


   a__     				=		[], []
   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   _lowerCamelCase ( UpperCAmelCase__  ,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


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





def   _lowerCamelCase ( UpperCAmelCase__  ,UpperCAmelCase__  ,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		accelerator.unwrap_model(__lowerCamelCase					).config
   a__     				=		4 if args.gradient_checkpointing else 3
   a__     				=		args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
   a__     				=		24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
   a__     				=		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))
   )
   a__     				=		flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
   return tflops





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   model.eval()
   a__     				=		[]
   for step, batch in enumerate(__lowerCamelCase					):
      with torch.no_grad():
         a__     				=		model(__lowerCamelCase  ,labels=__lowerCamelCase					)
      a__     				=		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
   a__     				=		torch.cat(__lowerCamelCase					)
   a__     				=		losses[: eval_dataloader.dataset.current_size].mean()
   try:
      a__     				=		torch.exp(__lowerCamelCase					)
   except OverflowError:
      a__     				=		float('inf'					)
   return loss.item(), perplexity.item()


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

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

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

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

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

# Load dataset and dataloader
__magic_name__    				=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__magic_name__    				=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__magic_name__    				=					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   _lowerCamelCase ( )							->     str:


   '''simple docstring'''


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


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

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



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


