
import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				str	, snake_case							:				Dict	, snake_case							:				Optional[Any]=None	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     None
						# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
						if str(weight.dtype	) == "torch.bfloat16":
												# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
												__UpperCamelCase       					=     weight.view(torch.intaa	)
												__UpperCamelCase       					=     '''bfloat16'''
						__UpperCamelCase       					=     weight.cpu().numpy()
						__UpperCamelCase       					=     os.path.join(snake_case	, f"{weight_name}.dat"	)
						if index is not None:
												if dtype is None:
																		__UpperCamelCase       					=     str(array.dtype	)
												__UpperCamelCase       					=     {'''dtype''': dtype, '''shape''': list(array.shape	)}
						if array.ndim == 0:
												__UpperCamelCase       					=     array[None]
						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=array.dtype	, mode='''w+'''	, shape=array.shape	)
						__UpperCamelCase       					=     array[:]
						file_array.flush()
						return index




def 		A_	(							snake_case							:				Tuple	, snake_case							:				Union[str, Any]	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     tuple(weight_info['''shape''']	)
						if shape == ():
												# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
												__UpperCamelCase       					=     (1,)

						__UpperCamelCase       					=     weight_info['''dtype''']
						if dtype == "bfloat16":
												# NumPy does not support bfloat16 so this was saved as a int16
												__UpperCamelCase       					=     '''int16'''

						__UpperCamelCase       					=     np.memmap(snake_case	, dtype=snake_case	, shape=snake_case	, mode='''r'''	)

						if len(weight_info['''shape''']	) == 0:
												__UpperCamelCase       					=     weight[0]
						__UpperCamelCase       					=     torch.tensor(snake_case	)
						if weight_info["dtype"] == "bfloat16":
												__UpperCamelCase       					=     weight.view(torch.bfloataa	)

						return weight




def 		A_	(							snake_case							:				Dict	, snake_case							:				int	) ->      str:



						'''simple docstring'''

						if index is None or len(snake_case	) == 0:
												# Nothing to save
												return

						__UpperCamelCase       					=     os.path.join(snake_case	, '''index.json'''	)
						if os.path.isfile(snake_case	):
												with open(snake_case	, '''r'''	, encoding='''utf-8'''	) as f:
																		__UpperCamelCase       					=     json.load(snake_case	)
						else:
												__UpperCamelCase       					=     {}
						current_index.update(snake_case	)

						with open(snake_case	, '''w'''	, encoding='''utf-8'''	) as f:
												json.dump(snake_case	, snake_case	, indent=2	)




def 		A_	(							snake_case							:				Union[str, os.PathLike]	, snake_case							:				Dict[str, torch.Tensor]	) ->      Dict:



						'''simple docstring'''

						os.makedirs(snake_case	, exist_ok=snake_case	)
						__UpperCamelCase       					=     {}
						for name, parameter in state_dict.items():
												__UpperCamelCase       					=     offload_weight(snake_case	, snake_case	, snake_case	, index=snake_case	)

						# Update index
						save_offload_index(snake_case	, snake_case	)






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     dataset
													__UpperCamelCase       					=     prefix



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return self.dataset[F"{self.prefix}{key}"]



							def __iter__(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )



							def __len__(					self     )->				str:





													'''simple docstring'''






													return len(self.dataset     )






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_=None       ,		)->				Tuple:





													'''simple docstring'''






													if state_dict is None and save_folder is None:
																			raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''     )

													__UpperCamelCase       					=     {} if state_dict is None else state_dict
													__UpperCamelCase       					=     save_folder
													if index is None and save_folder is not None:
																			with open(os.path.join(SCREAMING_SNAKE_CASE_       ,		'''index.json'''     )     ) as f:
																									__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {} if index is None else index
													__UpperCamelCase       					=     list(self.state_dict.keys()     )
													self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
													__UpperCamelCase       					=     device



							def __getitem__(					self       ,		SCREAMING_SNAKE_CASE_     )->				Tuple:





													'''simple docstring'''






													if key in self.state_dict:
																			return self.state_dict[key]
													__UpperCamelCase       					=     self.index[key]
													if weight_info.get('''safetensors_file'''     ) is not None:
																			if not is_safetensors_available():
																									raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''     )

																			from safetensors import safe_open

																			__UpperCamelCase       					=     '''cpu''' if self.device is None else self.device
																			with safe_open(weight_info['''safetensors_file''']       ,		framework='''pt'''       ,		device=SCREAMING_SNAKE_CASE_     ) as f:
																									__UpperCamelCase       					=     f.get_tensor(weight_info.get('''weight_name'''       ,		SCREAMING_SNAKE_CASE_     )     )

																			if "dtype" in weight_info:
																									return tensor.to(getattr(SCREAMING_SNAKE_CASE_       ,		weight_info['''dtype''']     )     )
																			else:
																									return tensor

													__UpperCamelCase       					=     os.path.join(self.save_folder       ,		F"{key}.dat"     )
													return load_offloaded_weight(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def __iter__(					self     )->				Dict:





													'''simple docstring'''






													return iter(self.all_keys     )



							def __len__(					self     )->				Dict:





													'''simple docstring'''






													return len(self.all_keys     )





def 		A_	(							snake_case							:				Dict[str, torch.Tensor]	, snake_case							:				List[str]	) ->      Tuple:



						'''simple docstring'''

						__UpperCamelCase       					=     {}
						for module_name in submodule_names:
												# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
												# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
												result.update(
												    {
												        key: param
												        for key, param in state_dict.items()
												        if key == module_name or key.startswith(module_name + '''.'''	)
												    }	)
						return result






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__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=3.6       ,		SCREAMING_SNAKE_CASE_=False       ,		)->				Dict:





													'''simple docstring'''






													__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:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=1000     )->				int:





													'''simple docstring'''






													return ShufflerIterDataPipe(self       ,		buffer_size=SCREAMING_SNAKE_CASE_     )





def 		A_	(							snake_case							:				Any	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     args.model_ckpt.split('''/'''	)[-1]
						__UpperCamelCase       					=     logging.getLogger(__name__	)
						__UpperCamelCase       					=     Path(args.save_dir	) / '''log/'''
						log_dir.mkdir(exist_ok=snake_case	)
						__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(snake_case	, vars(snake_case	)	)
												__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 		A_	(							snake_case							:				Any	) ->      Tuple:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	, snake_case							:				List[str]	, snake_case							:				Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	) ->      Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase					,							__UpperCamelCase       					=     [], []
						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							:				int	, snake_case							:				str	) ->      List[str]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	, snake_case							:				Dict	, snake_case							:				Union[str, Any]	) ->      Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       					=     accelerator.unwrap_model(snake_case	).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 		A_	(							snake_case							:				Tuple	) ->      Union[str, Any]:



						'''simple docstring'''

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


# Settings
lowercase__							:							Optional[int]													=     HfArgumentParser(TrainingArguments)
lowercase__							:							Optional[int]													=     parser.parse_args()

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

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

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

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

# Load dataset and dataloader
lowercase__						,					lowercase__							:							Union[str, Any]													=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase__							:							int													=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase__							:							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 		A_	(							) ->      Optional[int]:



						'''simple docstring'''

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


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

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




