

"""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        a    (							_lowerCamelCase					):



    """simple docstring"""



    def __init__(  self:			Tuple     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]     ,    UpperCamelCase:			int=False     ,    UpperCamelCase:			str=10_24     ,    UpperCamelCase:			Dict=10_24     ,    UpperCamelCase:			Optional[int]=3.6     ,    UpperCamelCase:			int=False     ,    ):






         """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]							):






         """simple docstring"""


         A__				      =	iter(self.dataset							)
         A__				      =	True
         while more_examples:
              A__      ,					A__				      =	[], 0
              while True:
                   if buffer_len >= self.max_buffer_size:
                        break
                   try:
                        buffer.append(next(UpperCamelCase							)[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(UpperCamelCase     ,    truncation=UpperCamelCase							)["""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(UpperCamelCase							)     ,    self.seq_length							):
                   A__				      =	all_token_ids[i : i + self.seq_length]
                   if len(UpperCamelCase							) == self.seq_length:
                        self.current_size += 1
                        yield torch.tensor(UpperCamelCase							)





    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			str=10_00							):






         """simple docstring"""


         return ShufflerIterDataPipe(self     ,    buffer_size=UpperCamelCase							)





def 			_snake_case  (     UpperCAmelCase_	:       str      ):
     A__				      =	args.model_ckpt.split("""/"""      )[-1]
     A__				      =	logging.getLogger(__name__      )
     A__				      =	Path(args.save_dir      ) / """log/"""
     log_dir.mkdir(exist_ok=UpperCAmelCase_      )
     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(UpperCAmelCase_       , vars(UpperCAmelCase_      )      )
          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 			_snake_case  (     UpperCAmelCase_	:       Tuple      ):
     A__				      =	{"""streaming""": True}
     A__				      =	load_dataset(args.dataset_name_train       , split="""train"""       , **UpperCAmelCase_      )
     A__				      =	train_data.shuffle(buffer_size=args.shuffle_buffer       , seed=args.seed      )
     A__				      =	load_dataset(args.dataset_name_valid       , split="""train"""       , **UpperCAmelCase_      )
     A__				      =	ConstantLengthDataset(
         UpperCAmelCase_       , UpperCAmelCase_       , infinite=UpperCAmelCase_       , seq_length=args.seq_length       , tokenized=args.tokenized      )
     A__				      =	ConstantLengthDataset(
         UpperCAmelCase_       , UpperCAmelCase_       , infinite=UpperCAmelCase_       , seq_length=args.seq_length       , tokenized=args.tokenized      )
     A__				      =	train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
     A__				      =	DataLoader(UpperCAmelCase_       , batch_size=args.train_batch_size       , shuffle=UpperCAmelCase_      )
     A__				      =	DataLoader(UpperCAmelCase_       , batch_size=args.valid_batch_size      )
     return train_dataloader, eval_dataloader





def 			_snake_case  (     UpperCAmelCase_	:       List[Any]       , UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Optional[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
     A__      ,					A__				      =	[], []
     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 			_snake_case  (     UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       str      ):
     logger.info(F"""Step {step}: {metrics}"""      )
     if accelerator.is_main_process:
          accelerator.log(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       Union[str, Any]       , UpperCAmelCase_	:       Tuple      ):
     # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
     A__				      =	accelerator.unwrap_model(UpperCAmelCase_      ).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 			_snake_case  (     UpperCAmelCase_	:       Optional[int]      ):
     model.eval()
     A__				      =	[]
     for step, batch in enumerate(UpperCAmelCase_      ):
          with torch.no_grad():
               A__				      =	model(UpperCAmelCase_       , labels=UpperCAmelCase_      )
          A__				      =	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
     A__				      =	torch.cat(UpperCAmelCase_      )
     A__				      =	losses[: eval_dataloader.dataset.current_size].mean()
     try:
          A__				      =	torch.exp(UpperCAmelCase_      )
     except OverflowError:
          A__				      =	float("""inf"""      )
     return loss.item(), perplexity.item()


# Settings
SCREAMING_SNAKE_CASE_       :						List[str]					=    HfArgumentParser(TrainingArguments)
SCREAMING_SNAKE_CASE_       :						int					=    parser.parse_args()

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

SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    Namespace(**vars(args), **acc_state)
SCREAMING_SNAKE_CASE_       :						Tuple					=    accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
 SCREAMING_SNAKE_CASE_       :						List[Any]					=    Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						Optional[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
SCREAMING_SNAKE_CASE_       :						Optional[int]					=    AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
 model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE_       :						List[Any]					=    AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE_       :						Union[str, 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 			_snake_case  (     ):
     return optimizer.param_groups[0]["lr"]


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

# Train model
model.train()
SCREAMING_SNAKE_CASE_       :						int					=    0
SCREAMING_SNAKE_CASE_       :						int					=    time.time()
SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    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
 SCREAMING_SNAKE_CASE_       :						str					=    model(batch, labels=batch, use_cache=False).loss
 SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    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()})
 SCREAMING_SNAKE_CASE_       :						str					=    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:
  SCREAMING_SNAKE_CASE_       :						Optional[int]					=    get_lr()
  accelerator.backward(loss)
  accelerator.clip_grad_norm_(model.parameters(), 1.0)
  optimizer.step()
  lr_scheduler.step()
  optimizer.zero_grad()
  SCREAMING_SNAKE_CASE_       :						List[Any]					=    time.time() - t_start
  SCREAMING_SNAKE_CASE_       :						Tuple					=    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,
      },
  )
  SCREAMING_SNAKE_CASE_       :						Tuple					=    time.time()
  SCREAMING_SNAKE_CASE_       :						List[Any]					=    0
  completed_steps += 1
 if step % args.save_checkpoint_steps == 0:
  logger.info('Evaluating and saving model checkpoint')
  SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						List[Any]					=    evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  SCREAMING_SNAKE_CASE_       :						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=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')
SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						str					=    evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE_       :						Any					=    accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
SCREAMING_SNAKE_CASE_       :						List[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 cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	zeros_like(UpperCAmelCase_      )
     for i in range(UpperCAmelCase_       , gray_img.shape[0] - bd      ):
          for j in range(UpperCAmelCase_       , gray_img.shape[1] - bd      ):
               # get mask according with mask
               A__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE_       :						int					=    imread('../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE_       :						str					=    cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 SCREAMING_SNAKE_CASE_       :						List[Any]					=    median_filter(gray, 3)
 SCREAMING_SNAKE_CASE_       :						str					=    median_filter(gray, 5)

 # show result images
 imshow('median filter with 3x3 mask', medianaxa)
 imshow('median filter with 5x5 mask', medianaxa)
 waitKey(0)






