



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	(    lowercase__):




				def __init__(  self    :  Any,    __lowercase    :  str,    __lowercase    :  List[str],    __lowercase    :  Tuple=False,    __lowercase    :  int=1024,    __lowercase    :  Dict=1024,    __lowercase    :  Optional[int]=3.6,    __lowercase    :  List[Any]=False,    ):
							lowercase__         = tokenizer
							lowercase__         = tokenizer.bos_token_id
							lowercase__         = dataset
							lowercase__         = seq_length
							lowercase__         = 0
							lowercase__         = infinite
							lowercase__         = 0
							lowercase__         = tokenized

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




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




				def 							A__					(  self    :  Tuple,    __lowercase    :  Optional[int]=1000  ):
							return ShufflerIterDataPipe(self,    buffer_size=UpperCAmelCase_  )



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



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



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]     ):
			lowercase__     ,				lowercase__         = [], []
			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 __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			logger.info(f'''Step {step}: {metrics}'''     )
			if accelerator.is_main_process:
						accelerator.log(_UpperCAmelCase  ,     _UpperCAmelCase     )



def __lowerCAmelCase       (   SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_     ):
			# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
			lowercase__         = accelerator.unwrap_model(_UpperCAmelCase     ).config
			lowercase__         = 4 if args.gradient_checkpointing else 3
			lowercase__         = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowercase__         = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowercase__         = 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))
			)
			lowercase__         = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops



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


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

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

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

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

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

# Load dataset and dataloader
lowercase_  =   create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowercase_  =   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase_  =   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       (   ):
			return optimizer.param_groups[0]["lr"]


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

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}

class 					__A   (  a			):
				__A            = """gpt2"""
				__A            = ["""past_key_values"""]
				__A            = {
				    """hidden_size""": """n_embd""",
				    """max_position_embeddings""": """n_positions""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}


				def __init__(						self      ,       UpperCAmelCase_=50257      ,       UpperCAmelCase_=1024      ,       UpperCAmelCase_=768      ,       UpperCAmelCase_=12      ,       UpperCAmelCase_=12      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="gelu_new"      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=1E-5      ,       UpperCAmelCase_=0.0_2      ,       UpperCAmelCase_="cls_index"      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=50256      ,       UpperCAmelCase_=50256      ,       UpperCAmelCase_=False      ,       UpperCAmelCase_=False      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =vocab_size
								lowerCamelCase		       =n_positions
								lowerCamelCase		       =n_embd
								lowerCamelCase		       =n_layer
								lowerCamelCase		       =n_head
								lowerCamelCase		       =n_inner
								lowerCamelCase		       =activation_function
								lowerCamelCase		       =resid_pdrop
								lowerCamelCase		       =embd_pdrop
								lowerCamelCase		       =attn_pdrop
								lowerCamelCase		       =layer_norm_epsilon
								lowerCamelCase		       =initializer_range
								lowerCamelCase		       =summary_type
								lowerCamelCase		       =summary_use_proj
								lowerCamelCase		       =summary_activation
								lowerCamelCase		       =summary_first_dropout
								lowerCamelCase		       =summary_proj_to_labels
								lowerCamelCase		       =scale_attn_weights
								lowerCamelCase		       =use_cache
								lowerCamelCase		       =scale_attn_by_inverse_layer_idx
								lowerCamelCase		       =reorder_and_upcast_attn

								lowerCamelCase		       =bos_token_id
								lowerCamelCase		       =eos_token_id

								super().__init__(bos_token_id=UpperCAmelCase_      ,       eos_token_id=UpperCAmelCase_      ,       **UpperCAmelCase_							)




class 					__A   (  a			):


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = "default"      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = False      ,       ):
								super().__init__(UpperCAmelCase_      ,       task=UpperCAmelCase_      ,       patching_specs=UpperCAmelCase_      ,       use_past=UpperCAmelCase_							)
								if not getattr(self._config      ,       """pad_token_id"""      ,       UpperCAmelCase_							):
												# TODO: how to do that better?
												lowerCamelCase		       =0


				@property
				def _snake_case				(						self							):
								lowerCamelCase		       =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
								if self.use_past:
												self.fill_with_past_key_values_(UpperCAmelCase_      ,       direction="""inputs"""							)
												lowerCamelCase		       ={0: """batch""", 1: """past_sequence + sequence"""}
								else:
												lowerCamelCase		       ={0: """batch""", 1: """sequence"""}

								return common_inputs


				@property
				def _snake_case				(						self							):
								return self._config.n_layer


				@property
				def _snake_case				(						self							):
								return self._config.n_head


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = -1      ,       UpperCAmelCase_ = -1      ,       UpperCAmelCase_ = False      ,       UpperCAmelCase_ = None      ,       ):
								lowerCamelCase		       =super(UpperCAmelCase_      ,       self							).generate_dummy_inputs(
								    UpperCAmelCase_      ,       batch_size=UpperCAmelCase_      ,       seq_length=UpperCAmelCase_      ,       is_pair=UpperCAmelCase_      ,       framework=UpperCAmelCase_							)

								# We need to order the input in the way they appears in the forward()
								lowerCamelCase		       =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

								# Need to add the past_keys
								if self.use_past:
												if not is_torch_available():
																raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
												else:
																import torch

																lowerCamelCase       ,     lowerCamelCase		       =common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																lowerCamelCase		       =seqlen + 2
																lowerCamelCase		       =(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																lowerCamelCase		       =[
																    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(self.num_layers							)
																]

								lowerCamelCase		       =common_inputs["""attention_mask"""]
								if self.use_past:
												lowerCamelCase		       =ordered_inputs["""attention_mask"""].dtype
												lowerCamelCase		       =torch.cat(
												    [ordered_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_      ,       UpperCAmelCase_      ,       dtype=UpperCAmelCase_							)]      ,       dim=1							)

								return ordered_inputs


				@property
				def _snake_case				(						self							):
								return 13





