






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer




class 							a_				(		tf.keras.layers.Layer	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = "longest"    ,    lowercase_ = True    ,    lowercase_ = 5_1_2    ,    lowercase_ = None    ,    lowercase_ = True    ,    lowercase_ = True    ,    lowercase_ = True    ,    **lowercase_    ,    )      ->      str:
														'''simple docstring'''

														super().__init__()
														if use_fast_bert_tokenizer:
																					lowerCAmelCase_       		=  FastBertTokenizer(
																					    lowercase_    ,    token_out_type=tf.intaa    ,    lower_case_nfd_strip_accents=lowercase_    ,    **lowercase_     )
														else:
																					lowerCAmelCase_       		=  tf.lookup.StaticVocabularyTable(
																					    tf.lookup.KeyValueTensorInitializer(
																					        keys=lowercase_    ,    key_dtype=tf.string    ,    values=tf.range(tf.size(lowercase_    ,    out_type=tf.intaa     )    ,    dtype=tf.intaa     )    ,    value_dtype=tf.intaa    ,    )    ,    num_oov_buckets=1    ,    )
																					lowerCAmelCase_       		=  BertTokenizerLayer(
																					    lowercase_    ,    token_out_type=tf.intaa    ,    lower_case=lowercase_    ,    **lowercase_     )

														lowerCAmelCase_       		=  vocab_list
														lowerCAmelCase_       		=  do_lower_case
														lowerCAmelCase_       		=  cls_token_id or vocab_list.index('[CLS]'     )
														lowerCAmelCase_       		=  sep_token_id or vocab_list.index('[SEP]'     )
														lowerCAmelCase_       		=  pad_token_id or vocab_list.index('[PAD]'     )
														lowerCAmelCase_       		=  ShrinkLongestTrimmer(max_length - 3    ,    axis=1     )  # Allow room for special tokens
														lowerCAmelCase_       		=  max_length
														lowerCAmelCase_       		=  padding
														lowerCAmelCase_       		=  truncation
														lowerCAmelCase_       		=  pad_to_multiple_of
														lowerCAmelCase_       		=  return_token_type_ids
														lowerCAmelCase_       		=  return_attention_mask

							@classmethod
							def   _lowercase (	cls    ,    lowercase_    ,    **lowercase_     )      ->      Optional[Any]:  # noqa: F821
														'''simple docstring'''

														lowerCAmelCase_       		=  kwargs.pop('do_lower_case'    ,    lowercase_     )
														lowerCAmelCase_       		=  tokenizer.do_lower_case if do_lower_case is None else do_lower_case
														lowerCAmelCase_       		=  kwargs.pop('cls_token_id'    ,    lowercase_     )
														lowerCAmelCase_       		=  tokenizer.cls_token_id if cls_token_id is None else cls_token_id
														lowerCAmelCase_       		=  kwargs.pop('sep_token_id'    ,    lowercase_     )
														lowerCAmelCase_       		=  tokenizer.sep_token_id if sep_token_id is None else sep_token_id
														lowerCAmelCase_       		=  kwargs.pop('pad_token_id'    ,    lowercase_     )
														lowerCAmelCase_       		=  tokenizer.pad_token_id if pad_token_id is None else pad_token_id

														lowerCAmelCase_       		=  tokenizer.get_vocab()
														lowerCAmelCase_       		=  sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]    ,    key=lambda lowercase_     : x[1]     )
														lowerCAmelCase_       		=  [entry[0] for entry in vocab]
														return cls(
														    vocab_list=lowercase_    ,    do_lower_case=lowercase_    ,    cls_token_id=lowercase_    ,    sep_token_id=lowercase_    ,    pad_token_id=lowercase_    ,    **lowercase_    ,    )

							@classmethod
							def   _lowercase (	cls    ,    lowercase_    ,    *lowercase_    ,    **lowercase_     )      ->      Dict:
														'''simple docstring'''

														try:
																					lowerCAmelCase_       		=  BertTokenizer.from_pretrained(lowercase_    ,    *lowercase_    ,    **lowercase_     )
														except:  # noqa: E722
																					from .tokenization_bert_fast import BertTokenizerFast

																					lowerCAmelCase_       		=  BertTokenizerFast.from_pretrained(lowercase_    ,    *lowercase_    ,    **lowercase_     )
														return cls.from_tokenizer(lowercase_    ,    **lowercase_     )

							def   _lowercase (	self    ,    lowercase_     )      ->      List[str]:
														'''simple docstring'''

														if self.do_lower_case:
																					lowerCAmelCase_       		=  case_fold_utfa(lowercase_     )
														lowerCAmelCase_       		=  self.tf_tokenizer.tokenize(lowercase_     )
														return tokens.merge_dims(1    ,    -1     )

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    lowercase_=None    ,    )      ->      Tuple:
														'''simple docstring'''

														if padding is None:
																					lowerCAmelCase_       		=  self.padding
														if padding not in ("longest", "max_length"):
																					raise ValueError('Padding must be either \'longest\' or \'max_length\'!'     )
														if max_length is not None and text_pair is not None:
																					# Because we have to instantiate a Trimmer to do it properly
																					raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'     )
														if max_length is None:
																					lowerCAmelCase_       		=  self.max_length
														if truncation is None:
																					lowerCAmelCase_       		=  self.truncation
														if pad_to_multiple_of is None:
																					lowerCAmelCase_       		=  self.pad_to_multiple_of
														if return_token_type_ids is None:
																					lowerCAmelCase_       		=  self.return_token_type_ids
														if return_attention_mask is None:
																					lowerCAmelCase_       		=  self.return_attention_mask
														if not isinstance(lowercase_    ,    tf.Tensor     ):
																					lowerCAmelCase_       		=  tf.convert_to_tensor(lowercase_     )
														if text_pair is not None and not isinstance(lowercase_    ,    tf.Tensor     ):
																					lowerCAmelCase_       		=  tf.convert_to_tensor(lowercase_     )
														if text_pair is not None:
																					if text.shape.rank > 1:
																												raise ValueError('text argument should not be multidimensional when a text pair is supplied!'     )
																					if text_pair.shape.rank > 1:
																												raise ValueError('text_pair should not be multidimensional!'     )
														if text.shape.rank == 2:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  text[:, 0], text[:, 1]
														lowerCAmelCase_       		=  self.unpaired_tokenize(lowercase_     )
														if text_pair is None:  # Unpaired text
																					if truncation:
																												lowerCAmelCase_       		=  text[:, : max_length - 2]  # Allow room for special tokens
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  combine_segments(
																					    (text,)    ,    start_of_sequence_id=self.cls_token_id    ,    end_of_segment_id=self.sep_token_id     )
														else:  # Paired text
																					lowerCAmelCase_       		=  self.unpaired_tokenize(lowercase_     )
																					if truncation:
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  self.paired_trimmer.trim([text, text_pair]     )
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  combine_segments(
																					    (text, text_pair)    ,    start_of_sequence_id=self.cls_token_id    ,    end_of_segment_id=self.sep_token_id     )
														if padding == "longest":
																					lowerCAmelCase_       		=  input_ids.bounding_shape(axis=1     )
																					if pad_to_multiple_of is not None:
																												# No ceiling division in tensorflow, so we negate floordiv instead
																												lowerCAmelCase_       		=  pad_to_multiple_of * (-tf.math.floordiv(-pad_length    ,    lowercase_     ))
														else:
																					lowerCAmelCase_       		=  max_length

														lowerCAmelCase_		,     lowerCAmelCase_       		=  pad_model_inputs(lowercase_    ,    max_seq_length=lowercase_    ,    pad_value=self.pad_token_id     )
														lowerCAmelCase_       		=  {'input_ids': input_ids}
														if return_attention_mask:
																					lowerCAmelCase_       		=  attention_mask
														if return_token_type_ids:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  pad_model_inputs(
																					    lowercase_    ,    max_seq_length=lowercase_    ,    pad_value=self.pad_token_id     )
																					lowerCAmelCase_       		=  token_type_ids
														return output






							def   _lowercase (	self     )      ->      str:
														'''simple docstring'''

														return {
														    "vocab_list": self.vocab_list,
														    "do_lower_case": self.do_lower_case,
														    "cls_token_id": self.cls_token_id,
														    "sep_token_id": self.sep_token_id,
														    "pad_token_id": self.pad_token_id,
														}











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_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_=False    ,    lowercase_=1_0_2_4    ,    lowercase_=1_0_2_4    ,    lowercase_=3.6    ,    lowercase_=False    ,    )      ->      Any:
														'''simple docstring'''

														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     )      ->      Union[str, Any]:
														'''simple docstring'''

														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(lowercase_     )[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(lowercase_    ,    truncation=lowercase_     )['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(lowercase_     )    ,    self.seq_length     ):
																												lowerCAmelCase_       		=  all_token_ids[i : i + self.seq_length]
																												if len(lowercase_     ) == self.seq_length:
																																			self.current_size += 1
																																			yield torch.tensor(lowercase_     )






							def   _lowercase (	self    ,    lowercase_=1_0_0_0     )      ->      Any:
														'''simple docstring'''

														return ShufflerIterDataPipe(self    ,    buffer_size=lowercase_     )


def   lowerCamelCase      (		a_			)			->    Any:
							lowerCAmelCase_       		=  args.model_ckpt.split('/'			)[-1]
							lowerCAmelCase_       		=  logging.getLogger(__name__			)
							lowerCAmelCase_       		=  Path(args.save_dir			) / 'log/'
							log_dir.mkdir(exist_ok=a_			)
							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(a_				,		vars(a_			)			)
														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   lowerCamelCase      (		a_			)			->    Union[str, Any]:
							lowerCAmelCase_       		=  {'streaming': True}
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_train				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  train_data.shuffle(buffer_size=args.shuffle_buffer				,		seed=args.seed			)
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_valid				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.train_batch_size				,		shuffle=a_			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.valid_batch_size			)
							return train_dataloader, eval_dataloader
def   lowerCamelCase      (		a_				,		a_				,		a_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			)			->    List[str]:
							lowerCAmelCase_		,     lowerCAmelCase_       		=  [], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay			):
																					params_without_wd.append(a_			)
														else:
																					params_with_wd.append(a_			)
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]
def   lowerCamelCase      (		a_				,		a_			)			->    List[str]:
							logger.info(F'''Step {step}: {metrics}'''			)
							if accelerator.is_main_process:
														accelerator.log(a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Tuple:
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							lowerCAmelCase_       		=  accelerator.unwrap_model(a_			).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 / (16.0 * config_model.n_layer * config_model.n_embd))
							)
							lowerCAmelCase_       		=  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
							return tflops
def   lowerCamelCase      (		a_			)			->    List[Any]:
							model.eval()
							lowerCAmelCase_       		=  []
							for step, batch in enumerate(a_			):
														with torch.no_grad():
																					lowerCAmelCase_       		=  model(a_				,		labels=a_			)
														lowerCAmelCase_       		=  outputs.loss.repeat(args.valid_batch_size			)
														losses.append(accelerator.gather(a_			)			)
														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							lowerCAmelCase_       		=  torch.cat(a_			)
							lowerCAmelCase_       		=  losses[: eval_dataloader.dataset.current_size].mean()
							try:
														lowerCAmelCase_       		=  torch.exp(a_			)
							except OverflowError:
														lowerCAmelCase_       		=  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   lowerCamelCase      (		)			->    str:
							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""")



