


"""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 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							def __init__(    self			:  int		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  Optional[Any]=False		,			lowerCamelCase_			:  Union[str, Any]=1024		,			lowerCamelCase_			:  Union[str, Any]=1024		,			lowerCamelCase_			:  Optional[Any]=3.6		,			lowerCamelCase_			:  Optional[int]=False		,			):
													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			:  Dict					):
													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(lowerCamelCase_					)[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(lowerCamelCase_		,			truncation=lowerCamelCase_					)['''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(lowerCamelCase_					)		,			self.seq_length					):
																									UpperCAmelCase__												=     all_token_ids[i : i + self.seq_length]
																									if len(lowerCamelCase_					) == self.seq_length:
																															self.current_size += 1
																															yield torch.tensor(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  List[Any]		,			lowerCamelCase_			:  int=1000					):
													return ShufflerIterDataPipe(self		,			buffer_size=lowerCamelCase_					)






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     args.model_ckpt.split('''/'''			)[-1]
						UpperCAmelCase__												=     logging.getLogger(__name__			)
						UpperCAmelCase__												=     Path(args.save_dir			) / '''log/'''
						log_dir.mkdir(exist_ok=_UpperCamelCase			)
						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(_UpperCamelCase						,      vars(_UpperCamelCase			)			)
												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__			(							_UpperCamelCase			):





						"""simple docstring"""

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






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			):





						"""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(_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        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

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






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     accelerator.unwrap_model(_UpperCamelCase			).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__			(							_UpperCamelCase			):





						"""simple docstring"""

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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
__a      			= AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__a      			= 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`.
__a			,						__a			,						__a			,						__a      			= 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)
												__a      			= os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												__a      			= [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)
												__a      			= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract the step of the checkpoint to continue from there
						__a      			= os.path.splitext(path)[0]
						__a      			= int(training_difference.replace("step_", ""))

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__a      			= logging.get_logger(__name__)

__a      			= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a      			= {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__a      			= {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

__a      			= {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE              =   PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE              =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE              =   ["""input_ids""", """attention_mask""", """token_type_ids"""]
							SCREAMING_SNAKE_CASE              =   DebertaTokenizer






							def __init__(    self			:  Dict		,			lowerCamelCase_			:  Dict=None		,			lowerCamelCase_			:  Union[str, Any]=None		,			lowerCamelCase_			:  Tuple=None		,			lowerCamelCase_			:  Dict="replace"		,			lowerCamelCase_			:  List[Any]="[CLS]"		,			lowerCamelCase_			:  Optional[Any]="[SEP]"		,			lowerCamelCase_			:  List[Any]="[SEP]"		,			lowerCamelCase_			:  str="[CLS]"		,			lowerCamelCase_			:  List[str]="[UNK]"		,			lowerCamelCase_			:  Any="[PAD]"		,			lowerCamelCase_			:  Tuple="[MASK]"		,			lowerCamelCase_			:  List[str]=False		,			**lowerCamelCase_			:  List[str]		,			):
													super().__init__(
													    lowerCamelCase_		,			lowerCamelCase_		,			tokenizer_file=lowerCamelCase_		,			errors=lowerCamelCase_		,			bos_token=lowerCamelCase_		,			eos_token=lowerCamelCase_		,			unk_token=lowerCamelCase_		,			sep_token=lowerCamelCase_		,			cls_token=lowerCamelCase_		,			pad_token=lowerCamelCase_		,			mask_token=lowerCamelCase_		,			add_prefix_space=lowerCamelCase_		,			**lowerCamelCase_		,			)
													UpperCAmelCase__												=     kwargs.pop('''add_bos_token'''		,			lowerCamelCase_					)

													UpperCAmelCase__												=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
													if pre_tok_state.get('''add_prefix_space'''		,			lowerCamelCase_					) != add_prefix_space:
																			UpperCAmelCase__												=     getattr(lowerCamelCase_		,			pre_tok_state.pop('''type'''					)					)
																			UpperCAmelCase__												=     add_prefix_space
																			UpperCAmelCase__												=     pre_tok_class(**lowerCamelCase_					)

													UpperCAmelCase__												=     add_prefix_space






							@property
							def        lowerCAmelCase  (    self			:  int					):
													if self._mask_token is None:
																			if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.'''					)
																			return None
													return str(self._mask_token					)






							@mask_token.setter
							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     AddedToken(lowerCamelCase_		,			lstrip=lowerCamelCase_		,			rstrip=lowerCamelCase_					) if isinstance(lowerCamelCase_		,			lowerCamelCase_					) else value
													UpperCAmelCase__												=     value






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]
													UpperCAmelCase__												=     [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													UpperCAmelCase__												=     [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep					) * [0]
													return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  Optional[int]		,			**lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  str		,			**lowerCamelCase_			:  Any					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Optional[str] = None					):
													UpperCAmelCase__												=     self._tokenizer.model.save(lowerCamelCase_		,			name=lowerCamelCase_					)
													return tuple(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  "Conversation"					):
													UpperCAmelCase__												=     []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(lowerCamelCase_		,			add_special_tokens=lowerCamelCase_					) + [self.eos_token_id]					)

													if len(lowerCamelCase_					) > self.model_max_length:
																			UpperCAmelCase__												=     input_ids[-self.model_max_length :]
													return input_ids