
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation

lowercase__							:							Optional[Any]													=     logging.get_logger(__name__)

lowercase__							:							Union[str, Any]													=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowercase__							:							Dict													=     {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

lowercase__							:							Union[str, Any]													=     {
    "gpt2": 1_0_2_4,
    "gpt2-medium": 1_0_2_4,
    "gpt2-large": 1_0_2_4,
    "gpt2-xl": 1_0_2_4,
    "distilgpt2": 1_0_2_4,
}




@lru_cache()
def 		A_	(							) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     (
						    list(range(ord('''!'''	)	, ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)	, ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)	, ord('''ÿ'''	) + 1	)	)
						)
						__UpperCamelCase       					=     bs[:]
						__UpperCamelCase       					=     0
						for b in range(2**8	):
												if b not in bs:
																		bs.append(snake_case	)
																		cs.append(2**8 + n	)
																		n += 1
						__UpperCamelCase       					=     [chr(snake_case	) for n in cs]
						return dict(zip(snake_case	, snake_case	)	)




def 		A_	(							snake_case							:				Optional[int]	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     set()
						__UpperCamelCase       					=     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)	)
												__UpperCamelCase       					=     char
						return pairs






class     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							_snake_case       						=					VOCAB_FILES_NAMES
							_snake_case       						=					PRETRAINED_VOCAB_FILES_MAP
							_snake_case       						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case       						=					['input_ids', 'attention_mask']



							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_="replace"       ,		SCREAMING_SNAKE_CASE_="<|endoftext|>"       ,		SCREAMING_SNAKE_CASE_="<|endoftext|>"       ,		SCREAMING_SNAKE_CASE_="<|endoftext|>"       ,		SCREAMING_SNAKE_CASE_=None       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=False       ,		**SCREAMING_SNAKE_CASE_       ,		)->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     AddedToken(SCREAMING_SNAKE_CASE_       ,		lstrip=SCREAMING_SNAKE_CASE_       ,		rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) else bos_token
													__UpperCamelCase       					=     AddedToken(SCREAMING_SNAKE_CASE_       ,		lstrip=SCREAMING_SNAKE_CASE_       ,		rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) else eos_token
													__UpperCamelCase       					=     AddedToken(SCREAMING_SNAKE_CASE_       ,		lstrip=SCREAMING_SNAKE_CASE_       ,		rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) else unk_token
													__UpperCamelCase       					=     AddedToken(SCREAMING_SNAKE_CASE_       ,		lstrip=SCREAMING_SNAKE_CASE_       ,		rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     ) else pad_token
													super().__init__(
													    errors=SCREAMING_SNAKE_CASE_       ,		unk_token=SCREAMING_SNAKE_CASE_       ,		bos_token=SCREAMING_SNAKE_CASE_       ,		eos_token=SCREAMING_SNAKE_CASE_       ,		pad_token=SCREAMING_SNAKE_CASE_       ,		add_prefix_space=SCREAMING_SNAKE_CASE_       ,		add_bos_token=SCREAMING_SNAKE_CASE_       ,		**SCREAMING_SNAKE_CASE_       ,		)
													__UpperCamelCase       					=     add_bos_token

													with open(SCREAMING_SNAKE_CASE_       ,		encoding='''utf-8'''     ) as vocab_handle:
																			__UpperCamelCase       					=     json.load(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     {v: k for k, v in self.encoder.items()}
													__UpperCamelCase       					=     errors  # how to handle errors in decoding
													__UpperCamelCase       					=     bytes_to_unicode()
													__UpperCamelCase       					=     {v: k for k, v in self.byte_encoder.items()}
													with open(SCREAMING_SNAKE_CASE_       ,		encoding='''utf-8'''     ) as merges_handle:
																			__UpperCamelCase       					=     merges_handle.read().split('''\n'''     )[1:-1]
													__UpperCamelCase       					=     [tuple(merge.split()     ) for merge in bpe_merges]
													__UpperCamelCase       					=     dict(zip(SCREAMING_SNAKE_CASE_       ,		range(len(SCREAMING_SNAKE_CASE_     )     )     )     )
													__UpperCamelCase       					=     {}
													__UpperCamelCase       					=     add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													__UpperCamelCase       					=     re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''     )



							@property
							def       A__						(					self     )->				Union[str, Any]:





													'''simple docstring'''






													return len(self.encoder     )



							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													return dict(self.encoder       ,		**self.added_tokens_encoder     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													if token in self.cache:
																			return self.cache[token]
													__UpperCamelCase       					=     tuple(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     get_pairs(SCREAMING_SNAKE_CASE_     )

													if not pairs:
																			return token

													while True:
																			__UpperCamelCase       					=     min(SCREAMING_SNAKE_CASE_       ,		key=lambda SCREAMING_SNAKE_CASE_     : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_       ,		float('''inf'''     )     )     )
																			if bigram not in self.bpe_ranks:
																									break
																			__UpperCamelCase					,							__UpperCamelCase       					=     bigram
																			__UpperCamelCase       					=     []
																			__UpperCamelCase       					=     0
																			while i < len(SCREAMING_SNAKE_CASE_     ):
																									try:
																															__UpperCamelCase       					=     word.index(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )
																									except ValueError:
																															new_word.extend(word[i:]     )
																															break
																									else:
																															new_word.extend(word[i:j]     )
																															__UpperCamelCase       					=     j

																									if word[i] == first and i < len(SCREAMING_SNAKE_CASE_     ) - 1 and word[i + 1] == second:
																															new_word.append(first + second     )
																															i += 2
																									else:
																															new_word.append(word[i]     )
																															i += 1
																			__UpperCamelCase       					=     tuple(SCREAMING_SNAKE_CASE_     )
																			__UpperCamelCase       					=     new_word
																			if len(SCREAMING_SNAKE_CASE_     ) == 1:
																									break
																			else:
																									__UpperCamelCase       					=     get_pairs(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     ''' '''.join(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     word
													return word



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None     )->				int:





													'''simple docstring'''






													if self.add_bos_token:
																			__UpperCamelCase       					=     [self.bos_token_id]
													else:
																			__UpperCamelCase       					=     []

													__UpperCamelCase       					=     bos_token_ids + token_ids_a

													if token_ids_a is None:
																			return output

													return output + bos_token_ids + token_ids_a



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		SCREAMING_SNAKE_CASE_ = False     )->				List[int]:





													'''simple docstring'''






													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=SCREAMING_SNAKE_CASE_       ,		token_ids_a=SCREAMING_SNAKE_CASE_       ,		already_has_special_tokens=SCREAMING_SNAKE_CASE_     )

													if not self.add_bos_token:
																			return super().get_special_tokens_mask(
																			    token_ids_a=SCREAMING_SNAKE_CASE_       ,		token_ids_a=SCREAMING_SNAKE_CASE_       ,		already_has_special_tokens=SCREAMING_SNAKE_CASE_     )

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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     []
													for token in re.findall(self.pat       ,		SCREAMING_SNAKE_CASE_     ):
																			__UpperCamelCase       					=     ''''''.join(
																			    self.byte_encoder[b] for b in token.encode('''utf-8'''     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_     ).split(''' '''     )     )
													return bpe_tokens



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Optional[Any]:





													'''simple docstring'''






													return self.encoder.get(SCREAMING_SNAKE_CASE_       ,		self.encoder.get(self.unk_token     )     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				int:





													'''simple docstring'''






													return self.decoder.get(SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     ''''''.join(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     bytearray([self.byte_decoder[c] for c in text]     ).decode('''utf-8'''       ,		errors=self.errors     )
													return text



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None     )->				Tuple[str]:





													'''simple docstring'''






													if not os.path.isdir(SCREAMING_SNAKE_CASE_     ):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
																			return
													__UpperCamelCase       					=     os.path.join(
													    SCREAMING_SNAKE_CASE_       ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )
													__UpperCamelCase       					=     os.path.join(
													    SCREAMING_SNAKE_CASE_       ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']     )

													with open(SCREAMING_SNAKE_CASE_       ,		'''w'''       ,		encoding='''utf-8'''     ) as f:
																			f.write(json.dumps(self.encoder       ,		indent=2       ,		sort_keys=SCREAMING_SNAKE_CASE_       ,		ensure_ascii=SCREAMING_SNAKE_CASE_     ) + '''\n'''     )

													__UpperCamelCase       					=     0
													with open(SCREAMING_SNAKE_CASE_       ,		'''w'''       ,		encoding='''utf-8'''     ) as writer:
																			writer.write('''#version: 0.2\n'''     )
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,		key=lambda SCREAMING_SNAKE_CASE_     : kv[1]     ):
																									if index != token_index:
																															logger.warning(
																															    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																															    ''' Please check that the tokenizer is not corrupted!'''     )
																															__UpperCamelCase       					=     token_index
																									writer.write(''' '''.join(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )
																									index += 1

													return vocab_file, merge_file



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       ,		**SCREAMING_SNAKE_CASE_     )->				str:





													'''simple docstring'''






													__UpperCamelCase       					=     kwargs.pop('''add_prefix_space'''       ,		self.add_prefix_space     )
													if is_split_into_words or add_prefix_space:
																			__UpperCamelCase       					=     ''' ''' + text
													return (text, kwargs)



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_     )->				List[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_       ,		add_special_tokens=SCREAMING_SNAKE_CASE_     ) + [self.eos_token_id]     )
													if len(SCREAMING_SNAKE_CASE_     ) > self.model_max_length:
																			__UpperCamelCase       					=     input_ids[-self.model_max_length :]
													return input_ids






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     SCREAMING_SNAKE_CASE__      (							SCREAMING_SNAKE_CASE_      ):

							"""simple docstring"""






							def __init__(					self       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=False       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=1024       ,		SCREAMING_SNAKE_CASE_=3.6       ,		SCREAMING_SNAKE_CASE_=False       ,		)->				Dict:





													'''simple docstring'''






													__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     )->				str:





													'''simple docstring'''






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



							def       A__						(					self       ,		SCREAMING_SNAKE_CASE_=1000     )->				int:





													'''simple docstring'''






													return ShufflerIterDataPipe(self       ,		buffer_size=SCREAMING_SNAKE_CASE_     )





def 		A_	(							snake_case							:				Any	) ->      Dict:



						'''simple docstring'''

						__UpperCamelCase       					=     args.model_ckpt.split('''/'''	)[-1]
						__UpperCamelCase       					=     logging.getLogger(__name__	)
						__UpperCamelCase       					=     Path(args.save_dir	) / '''log/'''
						log_dir.mkdir(exist_ok=snake_case	)
						__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(snake_case	, vars(snake_case	)	)
												__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_	(							snake_case							:				Any	) ->      Tuple:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Tuple	, snake_case							:				List[str]	, snake_case							:				Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	) ->      Union[str, Any]:



						'''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(snake_case	)
												else:
																		params_with_wd.append(snake_case	)
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]




def 		A_	(							snake_case							:				int	, snake_case							:				str	) ->      List[str]:



						'''simple docstring'''

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




def 		A_	(							snake_case							:				Any	, snake_case							:				Dict	, snake_case							:				Union[str, Any]	) ->      Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       					=     accelerator.unwrap_model(snake_case	).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_	(							snake_case							:				Tuple	) ->      Union[str, Any]:



						'''simple docstring'''

						model.eval()
						__UpperCamelCase       					=     []
						for step, batch in enumerate(snake_case	):
												with torch.no_grad():
																		__UpperCamelCase       					=     model(snake_case	, labels=snake_case	)
												__UpperCamelCase       					=     outputs.loss.repeat(args.valid_batch_size	)
												losses.append(accelerator.gather(snake_case	)	)
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						__UpperCamelCase       					=     torch.cat(snake_case	)
						__UpperCamelCase       					=     losses[: eval_dataloader.dataset.current_size].mean()
						try:
												__UpperCamelCase       					=     torch.exp(snake_case	)
						except OverflowError:
												__UpperCamelCase       					=     float('''inf'''	)
						return loss.item(), perplexity.item()


# Settings
lowercase__							:							Optional[int]													=     HfArgumentParser(TrainingArguments)
lowercase__							:							Optional[int]													=     parser.parse_args()

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

lowercase__							:							Union[str, Any]													=     Namespace(**vars(args), **acc_state)
lowercase__							:							List[Any]													=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
lowercase__						,					lowercase__							:							List[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
lowercase__							:							Union[str, Any]													=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
lowercase__							:							int													=     AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
lowercase__							:							int													=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowercase__							:							int													=     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_	(							) ->      Optional[int]:



						'''simple docstring'''

						return optimizer.param_groups[0]["lr"]


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

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




