"""simple docstring"""







import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


SCREAMING_SNAKE_CASE						=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE						=    'cuda' if torch.cuda.is_available() else 'cpu'


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase=100  ,__UpperCAmelCase=" "      ):






	"""simple docstring"""





	_lowercase			:     str      					=				text.split(__UpperCAmelCase      )
	return [character.join(text[i : i + n]      ).strip() for i in range(0  ,len(__UpperCAmelCase      )  ,__UpperCAmelCase      )]


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase					,   _lowercase			:     str      					=				[], []
	for title, text in zip(documents['title']  ,documents['text']      ):
		if text is not None:
			for passage in split_text(__UpperCAmelCase      ):
				titles.append(title if title is not None else ''      )
				texts.append(__UpperCAmelCase      )
	return {"title": titles, "text": texts}


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     List[Any]      					=				ctx_tokenizer(
	    documents['title']  ,documents['text']  ,truncation=__UpperCAmelCase  ,padding='longest'  ,return_tensors='pt'      )['input_ids']
	_lowercase			:     Any      					=				ctx_encoder(input_ids.to(device=__UpperCAmelCase      )  ,return_dict=__UpperCAmelCase      ).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,):






	"""simple docstring"""





	logger.info('Step 1 - Create the dataset'      )
	######################################

	# The dataset needed for RAG must have three columns:
	# - title (string): title of the document
	# - text (string): text of a passage of the document
	# - embeddings (array of dimension d): DPR representation of the passage

	# Let's say you have documents in tab-separated csv files with columns "title" and "text"
	assert os.path.isfile(rag_example_args.csv_path      ), "Please provide a valid path to a csv file"

	# You can load a Dataset object this way
	_lowercase			:     Union[str, Any]      					=				load_dataset(
	    'csv'  ,data_files=[rag_example_args.csv_path]  ,split='train'  ,delimiter='\t'  ,column_names=['title', 'text']      )

	# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

	# Then split the documents into passages of 100 words
	_lowercase			:     List[str]      					=				dataset.map(__UpperCAmelCase  ,batched=__UpperCAmelCase  ,num_proc=processing_args.num_proc      )

	# And compute the embeddings
	_lowercase			:     Dict      					=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      ).to(device=__UpperCAmelCase      )
	_lowercase			:     List[str]      					=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name      )
	_lowercase			:     List[str]      					=				Features(
	    {'text': Value('string'      ), 'title': Value('string'      ), 'embeddings': Sequence(Value('float32'      )      )}      )  # optional, save as float32 instead of float64 to save space
	_lowercase			:     List[Any]      					=				dataset.map(
	    partial(__UpperCAmelCase  ,ctx_encoder=__UpperCAmelCase  ,ctx_tokenizer=__UpperCAmelCase      )  ,batched=__UpperCAmelCase  ,batch_size=processing_args.batch_size  ,features=__UpperCAmelCase  ,)

	# And finally save your dataset
	_lowercase			:     Dict      					=				os.path.join(rag_example_args.output_dir  ,'my_knowledge_dataset'      )
	dataset.save_to_disk(__UpperCAmelCase      )
	# from datasets import load_from_disk
	# dataset = load_from_disk(passages_path)  # to reload the dataset

	######################################
	logger.info('Step 2 - Index the dataset'      )
	######################################

	# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
	_lowercase			:     int      					=				faiss.IndexHNSWFlat(index_hnsw_args.d  ,index_hnsw_args.m  ,faiss.METRIC_INNER_PRODUCT      )
	dataset.add_faiss_index('embeddings'  ,custom_index=__UpperCAmelCase      )

	# And save the index
	_lowercase			:     List[Any]      					=				os.path.join(rag_example_args.output_dir  ,'my_knowledge_dataset_hnsw_index.faiss'      )
	dataset.get_index('embeddings'      ).save(__UpperCAmelCase      )
	# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

	######################################
	logger.info('Step 3 - Load RAG'      )
	######################################

	# Easy way to load the model
	_lowercase			:     Any      					=				RagRetriever.from_pretrained(
	    rag_example_args.rag_model_name  ,index_name='custom'  ,indexed_dataset=__UpperCAmelCase      )
	_lowercase			:     Optional[int]      					=				RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,retriever=__UpperCAmelCase      )
	_lowercase			:     List[str]      					=				RagTokenizer.from_pretrained(rag_example_args.rag_model_name      )

	# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
	# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

	######################################
	logger.info('Step 4 - Have fun'      )
	######################################

	_lowercase			:     Any      					=				rag_example_args.question or 'What does Moses\' rod turn into ?'
	_lowercase			:     List[Any]      					=				tokenizer.question_encoder(__UpperCAmelCase  ,return_tensors='pt'      )['input_ids']
	_lowercase			:     Optional[Any]      					=				model.generate(__UpperCAmelCase      )
	_lowercase			:     List[str]      					=				tokenizer.batch_decode(__UpperCAmelCase  ,skip_special_tokens=__UpperCAmelCase      )[0]
	logger.info('Q: ' + question      )
	logger.info('A: ' + generated_string      )



@dataclass
class 			_lowerCamelCase      :
				_snake_case  =	field(
				    default=str(Path(_UpperCamelCase  ).parent / "test_data" / "my_knowledge_dataset.csv"  )    ,				metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}    ,				)
				_snake_case  =	field(
				    default=_UpperCamelCase    ,				metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}    ,				)
				_snake_case  =	field(
				    default="facebook/rag-sequence-nq"    ,				metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}    ,				)
				_snake_case  =	field(
				    default="facebook/dpr-ctx_encoder-multiset-base"    ,				metadata={
				        "help": (
				            "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
				            " 'facebook/dpr-ctx_encoder-multiset-base'"
				        )
				    }    ,				)
				_snake_case  =	field(
				    default=_UpperCamelCase    ,				metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}    ,				)



@dataclass
class 			_lowerCamelCase      :
				_snake_case  =	field(
				    default=_UpperCamelCase    ,				metadata={
				        "help": "The number of processes to use to split the documents into passages. Default is single process."
				    }    ,				)
				_snake_case  =	field(
				    default=16    ,				metadata={
				        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
				    }    ,				)



@dataclass
class 			_lowerCamelCase      :
				_snake_case  =	field(
				    default=768    ,				metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}    ,				)
				_snake_case  =	field(
				    default=128    ,				metadata={
				        "help": (
				            "The number of bi-directional links created for every new element during the HNSW index construction."
				        )
				    }    ,				)


if __name__ == "__main__":
						logging.basicConfig(level=logging.WARNING)
						logger.setLevel(logging.INFO)

						SCREAMING_SNAKE_CASE						=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						SCREAMING_SNAKE_CASE						=    parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												SCREAMING_SNAKE_CASE						=    rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)









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        snake_case__				(_UpperCamelCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	Dict   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Optional[Any]=10_24   , __lowerCamelCase							:	str=10_24   , __lowerCamelCase							:	Any=3.6   , __lowerCamelCase							:	List[Any]=False   , )   ->	Optional[Any]:
							a       =     tokenizer
							a       =     tokenizer.bos_token_id
							a       =     dataset
							a       =     seq_length
							a       =     0
							a       =     infinite
							a       =     0
							a       =     tokenized

							if self.tokenized:
										a       =     seq_length * num_of_sequences
										a       =     "input_ids"
							else:
										a       =     seq_length * chars_per_token * num_of_sequences
										a       =     "content"





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





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Optional[int]=10_00				)   ->	Union[str, Any]:
							return ShufflerIterDataPipe(self   , buffer_size=__lowerCamelCase				)




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







			a       =     args.model_ckpt.split("/"  )[-1]
			a       =     logging.getLogger(__name__  )
			a       =     Path(args.save_dir  ) / "log/"
			log_dir.mkdir(exist_ok=A  )
			a       =     F"""debug_{accelerator.process_index}.log"""
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",    datefmt="%m/%d/%Y %H:%M:%S",    level=logging.INFO,    handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()],    )
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(A,    vars(A  )  )
						a       =     accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO  )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						a       =     ""
						logger.setLevel(logging.ERROR  )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name




def      __magic_name__							(					A						:   Any  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[Any],    A						:   Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   List[str],    A						:   List[str]  ):






			'''simple docstring'''







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




def      __magic_name__							(					A						:   Union[str, Any],    A						:   Optional[int],    A						:   List[str]  ):






			'''simple docstring'''







			a       =     accelerator.unwrap_model(A  ).config
			a       =     4 if args.gradient_checkpointing else 3
			a       =     args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			a       =     24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			a       =     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))
			)
			a       =     flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops




def      __magic_name__							(					A						:   List[Any]  ):






			'''simple docstring'''







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


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

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

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

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

# Logging
__lowerCAmelCase     ,	__lowerCAmelCase						:							Dict			     =						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowerCAmelCase						:							Union[str, Any]			     =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
__lowerCAmelCase						:							str			     =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowerCAmelCase     ,	__lowerCAmelCase						:							Tuple			     =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowerCAmelCase						:							Any			     =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowerCAmelCase						:							Optional[Any]			     =						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      __magic_name__							(					):






			'''simple docstring'''







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


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

# Train model
model.train()
__lowerCAmelCase						:							int			     =						0
__lowerCAmelCase						:							Union[str, Any]			     =						time.time()
__lowerCAmelCase						:							List[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
		__lowerCAmelCase						:							List[str]			     =						model(batch, labels=batch, use_cache=False).loss
		__lowerCAmelCase						:							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()})
		__lowerCAmelCase						:							Tuple			     =						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				__lowerCAmelCase						:							Optional[Any]			     =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				__lowerCAmelCase						:							str			     =						time.time() - t_start
				__lowerCAmelCase						:							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,
				    },
				)
				__lowerCAmelCase						:							Dict			     =						time.time()
				__lowerCAmelCase						:							str			     =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info('Evaluating and saving model checkpoint')
				__lowerCAmelCase     ,	__lowerCAmelCase						:							Any			     =						evaluate(args)
				log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
				accelerator.wait_for_everyone()
				__lowerCAmelCase						:							Tuple			     =						os.path.join(args.save_dir, F'''step_{step}''')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=F'''step {step}''')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
__lowerCAmelCase     ,	__lowerCAmelCase						:							str			     =						evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
__lowerCAmelCase						:							Optional[Any]			     =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowerCAmelCase						:							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='final model')






