





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   (  a			):


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_=False      ,       UpperCAmelCase_=1024      ,       UpperCAmelCase_=1024      ,       UpperCAmelCase_=3.6      ,       UpperCAmelCase_=False      ,       ):
								lowerCamelCase		       =tokenizer
								lowerCamelCase		       =tokenizer.bos_token_id
								lowerCamelCase		       =dataset
								lowerCamelCase		       =seq_length
								lowerCamelCase		       =0
								lowerCamelCase		       =infinite
								lowerCamelCase		       =0
								lowerCamelCase		       =tokenized

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


				def __iter__(						self							):
								lowerCamelCase		       =iter(self.dataset							)
								lowerCamelCase		       =True
								while more_examples:
												lowerCamelCase       ,     lowerCamelCase		       =[], 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:
																								lowerCamelCase		       =iter(self.dataset							)
																								self.epoch += 1
																								logger.info(f"""Dataset epoch: {self.epoch}"""							)
																				else:
																								lowerCamelCase		       =False
																								break
												if self.tokenized:
																lowerCamelCase		       =buffer
												else:
																lowerCamelCase		       =self.tokenizer(UpperCAmelCase_      ,       truncation=UpperCAmelCase_							)["""input_ids"""]
												lowerCamelCase		       =[]
												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							):
																lowerCamelCase		       =all_token_ids[i : i + self.seq_length]
																if len(UpperCAmelCase_							) == self.seq_length:
																				self.current_size += 1
																				yield torch.tensor(UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_=1000							):
								return ShufflerIterDataPipe(self      ,       buffer_size=UpperCAmelCase_							)





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





def    _lowercase    (  _UpperCAmelCase )  ->     Union[str, Any]:
				lowerCamelCase		       ={"""streaming""": True}
				lowerCamelCase		       =load_dataset(args.dataset_name_train	,					split="""train"""	,					**_UpperCAmelCase )
				lowerCamelCase		       =train_data.shuffle(buffer_size=args.shuffle_buffer	,					seed=args.seed )
				lowerCamelCase		       =load_dataset(args.dataset_name_valid	,					split="""train"""	,					**_UpperCAmelCase )
				lowerCamelCase		       =ConstantLengthDataset(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					infinite=_UpperCAmelCase	,					seq_length=args.seq_length	,					tokenized=args.tokenized )
				lowerCamelCase		       =ConstantLengthDataset(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					infinite=_UpperCAmelCase	,					seq_length=args.seq_length	,					tokenized=args.tokenized )
				lowerCamelCase		       =train_dataset.shuffle(buffer_size=args.shuffle_buffer )
				lowerCamelCase		       =DataLoader(_UpperCAmelCase	,					batch_size=args.train_batch_size	,					shuffle=_UpperCAmelCase )
				lowerCamelCase		       =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"] )  ->     Tuple:
				lowerCamelCase       ,     lowerCamelCase		       =[], []
				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 )  ->     Dict:
				logger.info(F"""Step {step}: {metrics}""" )
				if accelerator.is_main_process:
								accelerator.log(_UpperCAmelCase	,					_UpperCAmelCase )





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Optional[int]:
				# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
				lowerCamelCase		       =accelerator.unwrap_model(_UpperCAmelCase ).config
				lowerCamelCase		       =4 if args.gradient_checkpointing else 3
				lowerCamelCase		       =args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				lowerCamelCase		       =24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				lowerCamelCase		       =factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
				)
				lowerCamelCase		       =flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops





def    _lowercase    (  _UpperCAmelCase )  ->     Optional[int]:
				model.eval()
				lowerCamelCase		       =[]
				for step, batch in enumerate(_UpperCAmelCase ):
								with torch.no_grad():
												lowerCamelCase		       =model(_UpperCAmelCase	,					labels=_UpperCAmelCase )
								lowerCamelCase		       =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
				lowerCamelCase		       =torch.cat(_UpperCAmelCase )
				lowerCamelCase		       =losses[: eval_dataloader.dataset.current_size].mean()
				try:
								lowerCamelCase		       =torch.exp(_UpperCAmelCase )
				except OverflowError:
								lowerCamelCase		       =float("""inf""" )
				return loss.item(), perplexity.item()


# Settings
UpperCAmelCase__  :	Any     =HfArgumentParser(TrainingArguments)
UpperCAmelCase__  :	List[Any]     =parser.parse_args()

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

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

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

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

# Load dataset and dataloader
UpperCAmelCase__ ,UpperCAmelCase__  :	str     =create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase__  :	List[str]     =AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase__  :	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    _lowercase    (  )  ->     str:
				return optimizer.param_groups[0]["lr"]


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

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

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row + 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _lowerCamelCase    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[int]:
       for row in range(1						,  image_shape[0]		):
              for col in range(1						,  image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=15						,  snake_case   :					Optional[int]=30						,  snake_case   :					int=128						,  snake_case   :					str=255		)->		List[Any]:
       # gaussian_filter
       _lowerCamelCase    	=					img_convolve(snake_case						,  gen_gaussian_kernel(9						,  sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _lowerCamelCase	,	_lowerCamelCase    	=					sobel_filter(snake_case		)
       _lowerCamelCase    	=					PI + np.radadeg(snake_case		)

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


if __name__ == "__main__":
      # read original image in gray mode
      A_     :      Optional[Any]								=cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      A_     :      Optional[Any]								=canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)






