






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			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__=False			,     UpperCamelCase__=1024			,     UpperCamelCase__=1024			,     UpperCamelCase__=3.6			,     UpperCamelCase__=False			,     ):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= tokenizer
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokenizer.bos_token_id
						SCREAMING_SNAKE_CASE_ :						Dict    				= dataset
						SCREAMING_SNAKE_CASE_ :						Dict    				= seq_length
						SCREAMING_SNAKE_CASE_ :						Any    				= 0
						SCREAMING_SNAKE_CASE_ :						Any    				= infinite
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 0
						SCREAMING_SNAKE_CASE_ :						str    				= tokenized

						if self.tokenized:
										SCREAMING_SNAKE_CASE_ :						Any    				= seq_length * num_of_sequences
										SCREAMING_SNAKE_CASE_ :						Tuple    				= 'input_ids'
						else:
										SCREAMING_SNAKE_CASE_ :						Dict    				= seq_length * chars_per_token * num_of_sequences
										SCREAMING_SNAKE_CASE_ :						Tuple    				= 'content'


		def __iter__(    self							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= iter(self.dataset							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= True
						while more_examples:
										SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Tuple    				= [], 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:
																						SCREAMING_SNAKE_CASE_ :						List[str]    				= iter(self.dataset							)
																						self.epoch += 1
																						logger.info(F'''Dataset epoch: {self.epoch}'''							)
																		else:
																						SCREAMING_SNAKE_CASE_ :						List[Any]    				= False
																						break
										if self.tokenized:
														SCREAMING_SNAKE_CASE_ :						Tuple    				= buffer
										else:
														SCREAMING_SNAKE_CASE_ :						Any    				= self.tokenizer(UpperCamelCase__			,     truncation=UpperCamelCase__							)['input_ids']
										SCREAMING_SNAKE_CASE_ :						Any    				= []
										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							):
														SCREAMING_SNAKE_CASE_ :						List[Any]    				= all_token_ids[i : i + self.seq_length]
														if len(UpperCamelCase__							) == self.seq_length:
																		self.current_size += 1
																		yield torch.tensor(UpperCamelCase__							)






		def 	__snake_case      (    self			,     UpperCamelCase__=1000							):
						return ShufflerIterDataPipe(self			,     buffer_size=UpperCamelCase__							)






def        _lowerCamelCase(      lowerCAmelCase__					:		str   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= args.model_ckpt.split('/'   )[-1]
				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= logging.getLogger(__name__   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= Path(args.save_dir   ) / 'log/'
				log_dir.mkdir(exist_ok=lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Union[str, 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(lowerCAmelCase__     ,     vars(lowerCAmelCase__   )   )
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO   )
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								SCREAMING_SNAKE_CASE_ :						Dict    				= ''
								logger.setLevel(logging.ERROR   )
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name



def        _lowerCamelCase(      lowerCAmelCase__					:		Union[str, Any]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {'streaming': True}
				SCREAMING_SNAKE_CASE_ :						Tuple    				= load_dataset(args.dataset_name_train     ,     split='train'     ,     **lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= train_data.shuffle(buffer_size=args.shuffle_buffer     ,     seed=args.seed   )
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= load_dataset(args.dataset_name_valid     ,     split='train'     ,     **lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						int    				= ConstantLengthDataset(
				    lowerCAmelCase__     ,     lowerCAmelCase__     ,     infinite=lowerCAmelCase__     ,     seq_length=args.seq_length     ,     tokenized=args.tokenized   )
				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ConstantLengthDataset(
				    lowerCAmelCase__     ,     lowerCAmelCase__     ,     infinite=lowerCAmelCase__     ,     seq_length=args.seq_length     ,     tokenized=args.tokenized   )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= train_dataset.shuffle(buffer_size=args.shuffle_buffer   )
				SCREAMING_SNAKE_CASE_ :						str    				= DataLoader(lowerCAmelCase__     ,     batch_size=args.train_batch_size     ,     shuffle=lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Dict    				= DataLoader(lowerCAmelCase__     ,     batch_size=args.valid_batch_size   )
				return train_dataloader, eval_dataloader



def        _lowerCamelCase(      lowerCAmelCase__					:		Union[str, Any]     ,     lowerCAmelCase__					:		Optional[int]     ,     lowerCAmelCase__					:		List[str]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Any    				= [], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay   ):
												params_without_wd.append(lowerCAmelCase__   )
								else:
												params_with_wd.append(lowerCAmelCase__   )
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]



def        _lowerCamelCase(      lowerCAmelCase__					:		Dict     ,     lowerCAmelCase__					:		Optional[int]   ):





				'''simple docstring'''







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



def        _lowerCamelCase(      lowerCAmelCase__					:		Optional[int]     ,     lowerCAmelCase__					:		Any     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						List[str]    				= accelerator.unwrap_model(lowerCAmelCase__   ).config
				SCREAMING_SNAKE_CASE_ :						Any    				= 4 if args.gradient_checkpointing else 3
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				SCREAMING_SNAKE_CASE_ :						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))
				)
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops



def        _lowerCamelCase(      lowerCAmelCase__					:		Any   ):





				'''simple docstring'''







				model.eval()
				SCREAMING_SNAKE_CASE_ :						str    				= []
				for step, batch in enumerate(lowerCAmelCase__   ):
								with torch.no_grad():
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(lowerCAmelCase__     ,     labels=lowerCAmelCase__   )
								SCREAMING_SNAKE_CASE_ :						List[Any]    				= outputs.loss.repeat(args.valid_batch_size   )
								losses.append(accelerator.gather(lowerCAmelCase__   )   )
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				SCREAMING_SNAKE_CASE_ :						List[str]    				= torch.cat(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Optional[int]    				= losses[: eval_dataloader.dataset.current_size].mean()
				try:
								SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= torch.exp(lowerCAmelCase__   )
				except OverflowError:
								SCREAMING_SNAKE_CASE_ :						List[str]    				= 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        _lowerCamelCase(      ):





				'''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')







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				__a				(	__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    HerbertTokenizer
		UpperCAmelCase__   :					List[Any]					     =    HerbertTokenizerFast
		UpperCAmelCase__   :					Optional[Any]					     =    True


		def 	__snake_case      (    self							):
						super().setUp()

						# Use a simpler test file without japanese/chinese characters
						with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''			,     encoding='utf-8'							) as f_data:
										SCREAMING_SNAKE_CASE_ :						Dict    				= f_data.read().replace('\n\n'			,     '\n'							).strip()

						SCREAMING_SNAKE_CASE_ :						str    				= [
						    '<s>',
						    '</s>',
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    'w</w>',
						    'r</w>',
						    't</w>',
						    'lo',
						    'low',
						    'er</w>',
						    'low</w>',
						    'lowest</w>',
						    'newer</w>',
						    'wider</w>',
						    ',</w>',
						    '<unk>',
						]
						SCREAMING_SNAKE_CASE_ :						str    				= dict(zip(UpperCamelCase__			,     range(len(UpperCamelCase__							)							)							)							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']							)
						SCREAMING_SNAKE_CASE_ :						int    				= os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['merges_file']							)
						with open(self.vocab_file			,     'w'							) as fp:
										fp.write(json.dumps(UpperCamelCase__							)							)
						with open(self.merges_file			,     'w'							) as fp:
										fp.write('\n'.join(UpperCamelCase__							)							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 'lower newer'
						SCREAMING_SNAKE_CASE_ :						int    				= 'lower newer'
						return input_text, output_text


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.tokenizer_class(vocab_file=self.vocab_file			,     merges_file=self.merges_file							)

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 'lower'
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ['low', 'er</w>']
						SCREAMING_SNAKE_CASE_ :						int    				= tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tokens + ['<unk>']
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= [16, 17, 23]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__							)			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						if not self.test_rust_tokenizer:
										return

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE_ :						List[str]    				= 'lower,newer'

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.tokenize(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= rust_tokenizer.tokenize(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= rust_tokenizer.encode(UpperCamelCase__			,     add_special_tokens=UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.encode(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= rust_tokenizer.encode(UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)






		@slow
		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'							)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= tokenizer.encode('konstruowanie sekwencji'			,     add_special_tokens=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= tokenizer.encode('konstruowanie wielu sekwencji'			,     add_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Any    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,     UpperCamelCase__							)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + text_a + [2]