




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 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	def __init__(					self	,				lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase=False	,				lowerCamelCase=1024	,				lowerCamelCase=1024	,				lowerCamelCase=3.6	,				lowerCamelCase=False	,				) ->      int:





								"""simple docstring"""


								lowercase__  :	Optional[Any]        =							tokenizer
								lowercase__  :	int        =							tokenizer.bos_token_id
								lowercase__  :	Tuple        =							dataset
								lowercase__  :	Any        =							seq_length
								lowercase__  :	List[str]        =							0
								lowercase__  :	List[str]        =							infinite
								lowercase__  :	str        =							0
								lowercase__  :	List[Any]        =							tokenized

								if self.tokenized:
															lowercase__  :	List[str]        =							seq_length * num_of_sequences
															lowercase__  :	Any        =							"input_ids"
								else:
															lowercase__  :	int        =							seq_length * chars_per_token * num_of_sequences
															lowercase__  :	Optional[Any]        =							"content"

	def __iter__(					self    ) ->      Optional[int]:





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							iter(self.dataset    )
								lowercase__  :	Union[str, Any]        =							True
								while more_examples:
															lowercase__ ,       lowercase__  :	int        =							[], 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:
																																				lowercase__  :	List[str]        =							iter(self.dataset    )
																																				self.epoch += 1
																																				logger.info(f"""Dataset epoch: {self.epoch}"""    )
																													else:
																																				lowercase__  :	List[Any]        =							False
																																				break
															if self.tokenized:
																						lowercase__  :	List[Any]        =							buffer
															else:
																						lowercase__  :	Dict        =							self.tokenizer(lowerCamelCase	,				truncation=lowerCamelCase    )["input_ids"]
															lowercase__  :	int        =							[]
															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    ):
																						lowercase__  :	str        =							all_token_ids[i : i + self.seq_length]
																						if len(lowerCamelCase    ) == self.seq_length:
																													self.current_size += 1
																													yield torch.tensor(lowerCamelCase    )







	def    __a     (					self	,				lowerCamelCase=1000    ) ->      Dict:





								"""simple docstring"""


								return ShufflerIterDataPipe(self	,				buffer_size=lowerCamelCase    )

def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			Tuple:
							lowercase__  :	List[Any]        =							args.model_ckpt.split("/"    )[-1]
							lowercase__  :	List[Any]        =							logging.getLogger(__name__    )
							lowercase__  :	Tuple        =							Path(args.save_dir    ) / "log/"
							log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Optional[Any]        =							F"""debug_{accelerator.process_index}.log"""
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,datefmt="%m/%d/%Y %H:%M:%S"    ,level=logging.INFO    ,handlers=[logging.FileHandler(log_dir / filename    ), logging.StreamHandler()]    ,)
							if accelerator.is_main_process:  # we only want to setup logging once
														accelerator.init_trackers(SCREAMING_SNAKE_CASE_    ,vars(SCREAMING_SNAKE_CASE_    )    )
														lowercase__  :	Any        =							accelerator.trackers[0].run.name
														logger.setLevel(logging.INFO    )
														datasets.utils.logging.set_verbosity_info()
														transformers.utils.logging.set_verbosity_info()
							else:
														lowercase__  :	Dict        =							""
														logger.setLevel(logging.ERROR    )
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							return logger, run_name
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			List[Any]:
							lowercase__  :	Optional[int]        =							{"streaming": True}
							lowercase__  :	List[str]        =							load_dataset(args.dataset_name_train    ,split="train"    ,**SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Dict        =							train_data.shuffle(buffer_size=args.shuffle_buffer    ,seed=args.seed    )
							lowercase__  :	Optional[Any]        =							load_dataset(args.dataset_name_valid    ,split="train"    ,**SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Optional[Any]        =							ConstantLengthDataset(
							    SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,infinite=SCREAMING_SNAKE_CASE_    ,seq_length=args.seq_length    ,tokenized=args.tokenized    )
							lowercase__  :	Tuple        =							ConstantLengthDataset(
							    SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,infinite=SCREAMING_SNAKE_CASE_    ,seq_length=args.seq_length    ,tokenized=args.tokenized    )
							lowercase__  :	str        =							train_dataset.shuffle(buffer_size=args.shuffle_buffer    )
							lowercase__  :	List[Any]        =							DataLoader(SCREAMING_SNAKE_CASE_    ,batch_size=args.train_batch_size    ,shuffle=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	List[str]        =							DataLoader(SCREAMING_SNAKE_CASE_    ,batch_size=args.valid_batch_size    )
							return train_dataloader, eval_dataloader
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]    )      ->			Any:
							lowercase__ ,       lowercase__  :	Any        =							[], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay    ):
																					params_without_wd.append(SCREAMING_SNAKE_CASE_    )
														else:
																					params_with_wd.append(SCREAMING_SNAKE_CASE_    )
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Union[str, Any]:
							logger.info(F"""Step {step}: {metrics}"""    )
							if accelerator.is_main_process:
														accelerator.log(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Dict:
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							lowercase__  :	str        =							accelerator.unwrap_model(SCREAMING_SNAKE_CASE_    ).config
							lowercase__  :	Optional[int]        =							4 if args.gradient_checkpointing else 3
							lowercase__  :	int        =							args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							lowercase__  :	Optional[int]        =							24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
							lowercase__  :	List[str]        =							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__  :	List[Any]        =							flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
							return tflops
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			Dict:
							model.eval()
							lowercase__  :	Any        =							[]
							for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
														with torch.no_grad():
																					lowercase__  :	str        =							model(SCREAMING_SNAKE_CASE_    ,labels=SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Union[str, Any]        =							outputs.loss.repeat(args.valid_batch_size    )
														losses.append(accelerator.gather(SCREAMING_SNAKE_CASE_    )    )
														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							lowercase__  :	Any        =							torch.cat(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	Union[str, Any]        =							losses[: eval_dataloader.dataset.current_size].mean()
							try:
														lowercase__  :	Tuple        =							torch.exp(SCREAMING_SNAKE_CASE_    )
							except OverflowError:
														lowercase__  :	Any        =							float("inf"    )
							return loss.item(), perplexity.item()


# Settings
__a  :		List[str]		     =   HfArgumentParser(TrainingArguments)
__a  :		Optional[Any]		     =   parser.parse_args()

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

__a  :		int		     =   Namespace(**vars(args), **acc_state)
__a  :		Optional[int]		     =   accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
__a     ,	__a  :		Optional[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
__a  :		int		     =   AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
__a  :		Optional[Any]		     =   AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
__a  :		Any		     =   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__a  :		Tuple		     =   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  snake_case_					(		)      ->			int:
							return optimizer.param_groups[0]["lr"]


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

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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(pad_token_id=lowerCamelCase	,				**lowerCamelCase    )

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	@property
	def    __a     (					self    ) ->      Mapping[str, Mapping[int, str]]:





								"""simple docstring"""


								if self.task == "multiple-choice":
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]    )