





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel


def  A							(			snake_case__   :						Optional[int]=32 ,				snake_case__   :						Union[str, Any]=10 ,				snake_case__   :						Union[str, Any]=100 ,				snake_case__   :						Optional[Any]=1026 ,				snake_case__   :						Dict=True ,				snake_case__   :						int="data/tokenized_stories_train_wikitext103.jbl" ,				snake_case__   :						str="igf_context_pairs.jbl" ,				)					->				List[Any]:
 '''simple docstring'''
 set_seed(3				)
 # generate train_data and objective_set
 __snake_case         =			generate_datasets(
     __A ,				__A ,				number=__A ,				min_len=1026 ,				trim=__A				)
 # keeps model same across runs
 set_seed(4				)
 # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
 # can we train on GPU?
 __snake_case         =			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)

 # load pretrained model
 __snake_case         =			load_gpta('gpt2'				).to(__A				)
 print('computing perplexity on objective set'				)
 __snake_case         =			compute_perplexity(__A ,				__A ,				__A				).item()
 print('perplexity on objective set:' ,				__A				)

 # collect igf pairs and save to file demo.jbl
 collect_objective_set(__A ,				__A ,				__A ,				__A ,				__A ,				__A ,				__A ,				__A				)

 # clean up, delete model and data we don't need anymore
 del model, train_data, objective_set
 torch.cuda.empty_cache()


def  A							(			snake_case__   :						List[str] ,				snake_case__   :						str=15 ,				snake_case__   :						Optional[int]=128 ,				snake_case__   :						Union[str, Any]=100 ,				snake_case__   :						Dict="igf_model.pt" ,				)					->				Optional[Any]:
 '''simple docstring'''
 set_seed(42				)

 # Load pre-trained model
 __snake_case         =			GPTaLMHeadModel.from_pretrained('gpt2'				)

 # Initialize secondary learner to use embedding weights of model
 __snake_case         =			SecondaryLearner(__A				)

 # Train secondary learner
 __snake_case         =			train_secondary_learner(
     __A ,				__A ,				max_epochs=__A ,				batch_size=__A ,				eval_freq=100 ,				igf_model_path=__A ,				)

 del model, secondary_learner_train_data
 torch.cuda.empty_cache()

 return secondary_learner


def  A							(			snake_case__   :						Any ,				snake_case__   :						Dict ,				snake_case__   :						Any ,				snake_case__   :						Optional[int]=32 ,				snake_case__   :						int=1000 ,				snake_case__   :						Any=16 ,				snake_case__   :						Tuple=1.0 ,				snake_case__   :						Optional[int]=recopy_gpta ,				snake_case__   :						Optional[int]=None ,				snake_case__   :						List[Any]=10 ,				snake_case__   :						List[str]="gpt2_finetuned.pt" ,				)					->				Union[str, Any]:
 '''simple docstring'''
 __snake_case         =			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)
 __snake_case         =			RandomSampler(__A				)
 __snake_case         =			DataLoader(__A ,				sampler=__A				)

 __snake_case         =			max_steps // (len(__A				)) + 1
 __snake_case         =			0
 __snake_case         =			torch.zeros((1, context_len) ,				dtype=torch.long ,				device=__A				)
 __snake_case         =			recopy_model(__A ,				__A ,				__A				)

 model.train()
 if secondary_learner is not None:
  secondary_learner.to(__A				)
  secondary_learner.eval()
 __snake_case         =			[]
 __snake_case         =			0

 __snake_case         =			[]
 __snake_case         =			[]

 # Compute the performance of the transformer model at the beginning
 __snake_case         =			compute_perplexity(__A ,				__A ,				__A				)
 test_perps.append(__A				)
 print('Test perplexity, step' ,				__A ,				':' ,				__A				)
 for epoch in range(int(__A				)				):
  for step, example in enumerate(__A				):
   torch.cuda.empty_cache()
   __snake_case         =			random.randint(0 ,				example.size(2				) - context_len - 1				)
   __snake_case         =			example[0, 0, start : start + context_len]
   lm_optimizer.zero_grad()
   __snake_case         =			model(__A ,				labels=__A				)
   __snake_case         =			True

   if secondary_learner is not None:
    __snake_case         =			secondary_learner.forward(
        torch.tensor(__A ,				dtype=torch.long ,				device=__A				).unsqueeze(0				)				)[0].item()
    observed_qs.append(float(__A				)				)

    # Here we implement the simple non-constant threshold for the predicted IG(X) value
    # We will decay the selectivity of our secondary learner filter from
    # 1 standard deviation above average to 1 below average after 10 batches.

    if global_step == 10:
     __snake_case         =			-1
    if predicted_q < threshold:
     __snake_case         =			False

            # If we passed the filter, add the context to the batch!
   if do_backprop:
    contexts.append(np.array(context.cpu()				)				)
    __snake_case         =			outputs[0]
    lm_loss.backward()
    examples += 1

   del outputs

   # Once the batch is filled with enough contexts, backprop on the batch.
   if examples == batch_size:
    torch.cuda.empty_cache()
    __snake_case         =			0
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters() ,				3.0				)
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule
    global_step += 1
    # Compute the performance of the transformer model at this batch
    if global_step % eval_interval == 0:
     __snake_case         =			compute_perplexity(__A ,				__A ,				__A				)
     test_perps.append(__A				)

     print('Test perplexity, step' ,				__A ,				':' ,				__A				)
            # Break out of the loop after 60 batches
   if max_steps > 0 and global_step > 60:
    break
  if max_steps > 0 and global_step > 60:
   break

    # save finetuned transformer model
 torch.save(model.state_dict() ,				__A				)
 torch.cuda.empty_cache()
 # Do some cleaning up so we can reinitialize for the next run of this function
 del lm_optimizer
 del lm_scheduler
 return model


def  A							(			)					->				int:
 '''simple docstring'''
 __snake_case         =			argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'				)

 # Required parameters
 parser.add_argument(
     '--data_dir' ,				default=__A ,				type=__A ,				required=__A ,				help='The input data dir. Should contain data files for WikiText.' ,				)
 parser.add_argument(
     '--model_name_or_path' ,				default=__A ,				type=__A ,				required=__A ,				help='Path to pretrained model or model identifier from huggingface.co/models' ,				)
 parser.add_argument(
     '--data_file' ,				type=__A ,				default=__A ,				help=(
         'A jbl file containing tokenized data which can be split as objective dataset, '
         'train_dataset and test_dataset.'
     ) ,				)

 parser.add_argument(
     '--igf_data_file' ,				type=__A ,				default=__A ,				help='A jbl file containing the context and information gain pairs to train secondary learner.' ,				)

 parser.add_argument(
     '--output_dir' ,				default=__A ,				type=__A ,				required=__A ,				help='The output directory where the final fine-tuned model is stored.' ,				)

 parser.add_argument(
     '--tokenizer_name' ,				default=__A ,				type=__A ,				help='Pretrained tokenizer name or path if not the same as model_name' ,				)
 parser.add_argument('--seed' ,				type=__A ,				default=__A ,				help='A seed for reproducible training.'				)

 parser.add_argument(
     '--context_len' ,				default=32 ,				type=__A ,				help=(
         'The maximum total input sequence length after tokenization. Sequences longer '
         'than this will be truncated, sequences shorter will be padded.'
     ) ,				)

 parser.add_argument(
     '--size_objective_set' ,				default=100 ,				type=__A ,				help='number of articles that are long enough to be used as our objective set' ,				)
 parser.add_argument(
     '--eval_freq' ,				default=100 ,				type=__A ,				help='secondary model evaluation is triggered at eval_freq'				)

 parser.add_argument('--max_steps' ,				default=1000 ,				type=__A ,				help='To calculate training epochs'				)

 parser.add_argument(
     '--secondary_learner_batch_size' ,				default=128 ,				type=__A ,				help='batch size of training data for secondary learner' ,				)

 parser.add_argument(
     '--batch_size' ,				default=16 ,				type=__A ,				help='batch size of training data of language model(gpt2) '				)

 parser.add_argument(
     '--eval_interval' ,				default=10 ,				type=__A ,				help=(
         'decay the selectivity of our secondary learner filter from'
         '1 standard deviation above average to 1 below average after 10 batches'
     ) ,				)

 parser.add_argument(
     '--number' ,				default=100 ,				type=__A ,				help='The number of examples split to be used as objective_set/test_data'				)

 parser.add_argument(
     '--min_len' ,				default=1026 ,				type=__A ,				help='The minimum length of the article to be used as objective set'				)

 parser.add_argument(
     '--secondary_learner_max_epochs' ,				default=15 ,				type=__A ,				help='number of epochs to train secondary learner'				)

 parser.add_argument('--trim' ,				default=__A ,				type=__A ,				help='truncate the example if it exceeds context length'				)

 parser.add_argument(
     '--threshold' ,				default=1.0 ,				type=__A ,				help=(
         'The threshold value used by secondary learner to filter the train_data and allow only'
         ' informative data as input to the model'
     ) ,				)

 parser.add_argument('--finetuned_model_name' ,				default='gpt2_finetuned.pt' ,				type=__A ,				help='finetuned_model_name'				)

 parser.add_argument(
     '--recopy_model' ,				default=__A ,				type=__A ,				help='Reset the model to the original pretrained GPT-2 weights after each iteration' ,				)

 # function calls
 # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
 generate_n_pairs(
     context_len=32 ,				max_steps=10 ,				size_objective_set=100 ,				min_len=1026 ,				trim=__A ,				data_file='data/tokenized_stories_train_wikitext103.jbl' ,				igf_data_file='igf_context_pairs.jbl' ,				)

 # Load train data for secondary learner
 __snake_case         =			joblib.load('data/IGF_values.jbl'				)

 # Train secondary learner
 __snake_case         =			training_secondary_learner(
     __A ,				secondary_learner_max_epochs=15 ,				secondary_learner_batch_size=128 ,				eval_freq=100 ,				igf_model_path='igf_model.pt' ,				)

 # load pretrained gpt2 model
 __snake_case         =			GPTaLMHeadModel.from_pretrained('gpt2'				)
 set_seed(42				)

 # Generate train and test data to train and evaluate gpt2 model
 __snake_case         =			generate_datasets(
     context_len=32 ,				file='data/tokenized_stories_train_wikitext103.jbl' ,				number=100 ,				min_len=1026 ,				trim=__A				)

 # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
 finetune(
     __A ,				__A ,				__A ,				context_len=32 ,				max_steps=1000 ,				batch_size=16 ,				threshold=1.0 ,				recopy_model=__A ,				secondary_learner=__A ,				eval_interval=10 ,				finetuned_model_name='gpt2_finetuned.pt' ,				)


if __name__ == "__main__":
  main()









'''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


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





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Optional[Any]=1_00    ,		__A				: int=" "   ):
				a_		:	Optional[int]									=   text.split(__A   )
				return [character.join(text[i : i + n]   ).strip() for i in range(0    ,		len(__A   )    ,		__A   )]





def 		_UpperCAmelCase (				__A				: dict   ):
				a_					,      a_		:	List[Any]									=   [], []
				for title, text in zip(documents['''title''']    ,		documents['''text''']   ):
								if text is not None:
												for passage in split_text(__A   ):
																titles.append(title if title is not None else ''''''   )
																texts.append(__A   )
				return {"title": titles, "text": texts}





def 		_UpperCAmelCase (				__A				: dict    ,		__A				: DPRContextEncoder    ,		__A				: DPRContextEncoderTokenizerFast   ):
				a_		:	int									=   ctx_tokenizer(
				    documents['''title''']    ,		documents['''text''']    ,		truncation=__A    ,		padding='''longest'''    ,		return_tensors='''pt'''   )['''input_ids''']
				a_		:	List[Any]									=   ctx_encoder(input_ids.to(device=__A   )    ,		return_dict=__A   ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}





def 		_UpperCAmelCase (				__A				: "RagExampleArguments"    ,		__A				: "ProcessingArguments"    ,		__A				: "IndexHnswArguments"    ,		):
				######################################
				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
				a_		:	List[str]									=   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
				a_		:	Dict									=   dataset.map(__A    ,		batched=__A    ,		num_proc=processing_args.num_proc   )

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

				# And finally save your dataset
				a_		:	Tuple									=   os.path.join(rag_example_args.output_dir    ,		'''my_knowledge_dataset'''   )
				dataset.save_to_disk(__A   )
				# 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
				a_		:	Dict									=   faiss.IndexHNSWFlat(index_hnsw_args.d    ,		index_hnsw_args.m    ,		faiss.METRIC_INNER_PRODUCT   )
				dataset.add_faiss_index('''embeddings'''    ,		custom_index=__A   )

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





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=str(Path(SCREAMING_SNAKE_CASE_							).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"							)						, metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, 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=str(Path(SCREAMING_SNAKE_CASE_							).parent / "test_run" / "dummy-kb"							)						, metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}						, )





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, 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 						SCREAMING_SNAKE_CASE       :
			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)

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




