




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       _UpperCamelCase   (					__A		):
      def __init__(      self :Union[str, Any]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :Dict			,   lowerCamelCase :Tuple=False			,   lowerCamelCase :Optional[Any]=1024			,   lowerCamelCase :str=1024			,   lowerCamelCase :Tuple=3.6			,   lowerCamelCase :Any=False			,   )  ->      Dict:
       UpperCAmelCase__							=    tokenizer
       UpperCAmelCase__							=    tokenizer.bos_token_id
       UpperCAmelCase__							=    dataset
       UpperCAmelCase__							=    seq_length
       UpperCAmelCase__							=    0
       UpperCAmelCase__							=    infinite
       UpperCAmelCase__							=    0
       UpperCAmelCase__							=    tokenized

       if self.tokenized:
        UpperCAmelCase__							=    seq_length * num_of_sequences
        UpperCAmelCase__							=    'input_ids'
       else:
        UpperCAmelCase__							=    seq_length * chars_per_token * num_of_sequences
        UpperCAmelCase__							=    'content'
      def __iter__(      self :str   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    iter(self.dataset   )
       UpperCAmelCase__							=    True
       while more_examples:
        UpperCAmelCase__							=    [], 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:
           UpperCAmelCase__							=    iter(self.dataset   )
           self.epoch += 1
           logger.info(f'''Dataset epoch: {self.epoch}'''   )
          else:
           UpperCAmelCase__							=    False
           break
        if self.tokenized:
         UpperCAmelCase__							=    buffer
        else:
         UpperCAmelCase__							=    self.tokenizer(UpperCamelCase__			,   truncation=UpperCamelCase__   )['input_ids']
        UpperCAmelCase__							=    []
        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   ):
         UpperCAmelCase__							=    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 :Dict			,   lowerCamelCase :List[str]=1000   )  ->      List[Any]:
       return ShufflerIterDataPipe(self			,   buffer_size=UpperCamelCase__   )



def     lowerCAmelCase		(		_lowerCAmelCase				: str						):




 """simple docstring"""







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



def     lowerCAmelCase		(		_lowerCAmelCase				: Union[str, Any]						):




 """simple docstring"""







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



def     lowerCAmelCase		(		_lowerCAmelCase				: Union[str, Any]    ,				_lowerCAmelCase				: Optional[int]    ,				_lowerCAmelCase				: List[str]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						):




 """simple docstring"""







 UpperCAmelCase__							=    [], []
 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		(		_lowerCAmelCase				: Dict    ,				_lowerCAmelCase				: Optional[int]						):




 """simple docstring"""







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



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[int]    ,				_lowerCAmelCase				: Any    ,				_lowerCAmelCase				: Tuple						):




 """simple docstring"""







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



def     lowerCAmelCase		(		_lowerCAmelCase				: Any						):




 """simple docstring"""







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


# Settings
_lowerCAmelCase    : List[Any]		= HfArgumentParser(TrainingArguments)
_lowerCAmelCase    : Union[str, Any]		= parser.parse_args()

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

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

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

# Logging
_lowerCAmelCase,       _lowerCAmelCase    : Tuple		= 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    : str		= 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,       _lowerCAmelCase    : Tuple		= create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowerCAmelCase    : str		= AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowerCAmelCase    : Optional[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     lowerCAmelCase		(		):




 """simple docstring"""







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


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

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













from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def        _lowerCamelCase(      lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.stack((horizontal_flow, vertical_flow)     ,     2   )

				# Create a grid of all pixel coordinates and subtract the flow to get the
				# target pixels coordinates
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.stack(
				    np.meshgrid(np.arange(0     ,     image.shape[1]   )     ,     np.arange(0     ,     image.shape[0]   )   )     ,     2   )
				SCREAMING_SNAKE_CASE_ :						List[str]    				= np.round(grid - flow   ).astype(np.intaa   )

				# Find the locations outside of the original image
				SCREAMING_SNAKE_CASE_ :						str    				= (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]   ))
				SCREAMING_SNAKE_CASE_ :						Dict    				= 0

				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 0

				return warped



def        _lowerCamelCase(      lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		np.ndarray     ,     lowerCAmelCase__					:		SupportsIndex     ,     lowerCAmelCase__					:		float | None = None     ,     ):





				'''simple docstring'''







				if alpha is None:
								SCREAMING_SNAKE_CASE_ :						Any    				= 0.1

				# Initialize flow
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= np.zeros_like(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Any    				= np.zeros_like(lowerCAmelCase__   )

				# Prepare kernels for the calculation of the derivatives and the average velocity
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.array([[-1, 1], [-1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Tuple    				= np.array([[-1, -1], [1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= np.array([[1, 1], [1, 1]]   ) * 0.25
				SCREAMING_SNAKE_CASE_ :						Tuple    				= np.array(
				    [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]   )

				# Iteratively refine the flow
				for _ in range(lowerCAmelCase__   ):
								SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= warp(lowerCAmelCase__     ,     lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Any    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   ) + convolve(lowerCAmelCase__     ,     -kernel_t   )

								SCREAMING_SNAKE_CASE_ :						List[str]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= convolve(lowerCAmelCase__     ,     lowerCAmelCase__   )

								# This updates the flow as proposed in the paper (Step 12)
								SCREAMING_SNAKE_CASE_ :						Tuple    				= (
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= update / (alpha**2 + derivative_x**2 + derivative_y**2)

								SCREAMING_SNAKE_CASE_ :						List[str]    				= avg_horizontal_velocity - derivative_x * update
								SCREAMING_SNAKE_CASE_ :						Optional[int]    				= avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
				import doctest

				doctest.testmod()