




"""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  _snake_case				(							A__       ):

				'''simple docstring'''




				def __init__(							self      :  Dict					,							snake_case      :  Optional[Any]					,							snake_case      :  Dict					,							snake_case      :  Union[str, Any]=False					,							snake_case      :  List[Any]=1_024					,							snake_case      :  Dict=1_024					,							snake_case      :  Optional[int]=3.6					,							snake_case      :  Optional[int]=False					,							):
								UpperCAmelCase_					:List[Any]      			=      tokenizer
								UpperCAmelCase_					:List[Any]      			=      tokenizer.bos_token_id
								UpperCAmelCase_					:Optional[Any]      			=      dataset
								UpperCAmelCase_					:Any      			=      seq_length
								UpperCAmelCase_					:int      			=      0
								UpperCAmelCase_					:Dict      			=      infinite
								UpperCAmelCase_					:Optional[int]      			=      0
								UpperCAmelCase_					:Union[str, Any]      			=      tokenized

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





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





				def 				snake_case_ (							self      :  Any					,							snake_case      :  Dict=1_000     ):
								return ShufflerIterDataPipe(self					,							buffer_size=snake_case     )



def 				a (						__snake_case     :				Any  ):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      args.model_ckpt.split('''/'''  )[-1]
				UpperCAmelCase_					:List[str]      			=      logging.getLogger(__name__  )
				UpperCAmelCase_					:Union[str, Any]      			=      Path(args.save_dir  ) / '''log/'''
				log_dir.mkdir(exist_ok=__snake_case  )
				UpperCAmelCase_					:Optional[Any]      			=      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(__snake_case,    vars(__snake_case  )  )
								UpperCAmelCase_					:List[Any]      			=      accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO  )
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								UpperCAmelCase_					:Any      			=      ''''''
								logger.setLevel(logging.ERROR  )
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





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



def 				a (						__snake_case     :				Optional[int],    __snake_case     :				Optional[Any],    __snake_case     :				Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):



				'''simple docstring'''





				UpperCAmelCase_			,UpperCAmelCase_					:Any      			=      [], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay  ):
												params_without_wd.append(__snake_case  )
								else:
												params_with_wd.append(__snake_case  )
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Tuple  ):



				'''simple docstring'''





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



def 				a (						__snake_case     :				List[Any],    __snake_case     :				List[str],    __snake_case     :				Optional[int]  ):



				'''simple docstring'''





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



def 				a (						__snake_case     :				Optional[int]  ):



				'''simple docstring'''





				model.eval()
				UpperCAmelCase_					:str      			=      []
				for step, batch in enumerate(__snake_case  ):
								with torch.no_grad():
												UpperCAmelCase_					:Union[str, Any]      			=      model(__snake_case,    labels=__snake_case  )
								UpperCAmelCase_					:Optional[Any]      			=      outputs.loss.repeat(args.valid_batch_size  )
								losses.append(accelerator.gather(__snake_case  )  )
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				UpperCAmelCase_					:Optional[int]      			=      torch.cat(__snake_case  )
				UpperCAmelCase_					:Tuple      			=      losses[: eval_dataloader.dataset.current_size].mean()
				try:
								UpperCAmelCase_					:Dict      			=      torch.exp(__snake_case  )
				except OverflowError:
								UpperCAmelCase_					:Union[str, Any]      			=      float('''inf'''  )
				return loss.item(), perplexity.item()


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
__lowerCamelCase								=						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCamelCase								=						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 				a (						):



				'''simple docstring'''





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


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

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

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  str					,							snake_case      :  Optional[Any]					,							snake_case      :  str     ):
								UpperCAmelCase_					:Union[str, Any]      			=      key
								UpperCAmelCase_					:str      			=      freq





				def __str__(							self      :  Optional[Any]     ):
								return f'Node(key={self.key}, freq={self.freq})'



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[int],    __snake_case     :				Tuple,    __snake_case     :				Any,    __snake_case     :				Dict,    __snake_case     :				List[str]  ):



				'''simple docstring'''





				if i > j or i < 0 or j > len(__snake_case  ) - 1:
								return

				UpperCAmelCase_					:List[str]      			=      root[i][j]
				if parent == -1:  # root does not have a parent
								print(f'{key[node]} is the root of the binary search tree.'  )
				elif is_left:
								print(f'{key[node]} is the left child of key {parent}.'  )
				else:
								print(f'{key[node]} is the right child of key {parent}.'  )

				print_binary_search_tree(__snake_case,    __snake_case,    __snake_case,    node - 1,    key[node],    __snake_case  )
				print_binary_search_tree(__snake_case,    __snake_case,    node + 1,    __snake_case,    key[node],    __snake_case  )



def 				a (						__snake_case     :				Optional[Any]  ):



				'''simple docstring'''





				nodes.sort(key=lambda __snake_case  : node.key  )

				UpperCAmelCase_					:List[str]      			=      len(__snake_case  )

				UpperCAmelCase_					:Any      			=      [nodes[i].key for i in range(__snake_case  )]
				UpperCAmelCase_					:int      			=      [nodes[i].freq for i in range(__snake_case  )]

				# This 2D array stores the overall tree cost (which's as minimized as possible);
				# for a single key, cost is equal to frequency of the key.
				UpperCAmelCase_					:Dict      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
				# array
				UpperCAmelCase_					:Optional[Any]      			=      [[freqs[i] if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]
				# stores tree roots that will be used later for constructing binary search tree
				UpperCAmelCase_					:int      			=      [[i if i == j else 0 for j in range(__snake_case  )] for i in range(__snake_case  )]

				for interval_length in range(2,    n + 1  ):
								for i in range(n - interval_length + 1  ):
												UpperCAmelCase_					:Union[str, Any]      			=      i + interval_length - 1

												UpperCAmelCase_					:Optional[int]      			=      sys.maxsize  # set the value to "infinity"
												UpperCAmelCase_					:List[Any]      			=      total[i][j - 1] + freqs[j]

												# Apply Knuth's optimization
												# Loop without optimization: for r in range(i, j + 1):
												for r in range(root[i][j - 1],    root[i + 1][j] + 1  ):  # r is a temporal root
																UpperCAmelCase_					:Any      			=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																UpperCAmelCase_					:Optional[int]      			=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																UpperCAmelCase_					:Union[str, Any]      			=      left + total[i][j] + right

																if dp[i][j] > cost:
																				UpperCAmelCase_					:Optional[Any]      			=      cost
																				UpperCAmelCase_					:Optional[int]      			=      r

				print('''Binary search tree nodes:'''  )
				for node in nodes:
								print(__snake_case  )

				print(f'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'  )
				print_binary_search_tree(__snake_case,    __snake_case,    0,    n - 1,    -1,    __snake_case  )



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      [Node(__snake_case,    randint(1,    50  )  ) for i in range(10,    0,    -1  )]
				find_optimal_binary_search_tree(__snake_case  )


if __name__ == "__main__":
						main()


