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 lowerCamelCase  (   SCREAMING_SNAKE_CASE=32  ,			SCREAMING_SNAKE_CASE=10  ,			SCREAMING_SNAKE_CASE=100  ,			SCREAMING_SNAKE_CASE=1_026  ,			SCREAMING_SNAKE_CASE=True  ,			SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"  ,			SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"  ,			):
    '''simple docstring'''







    set_seed(3	)
    # generate train_data and objective_set
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		generate_datasets(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			number=SCREAMING_SNAKE_CASE  ,			min_len=1_026  ,			trim=SCREAMING_SNAKE_CASE	)
    # 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?
    __UpperCamelCase     :List[Any]			  =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)

    # load pretrained model
    __UpperCamelCase     :str			  =		load_gpta('''gpt2'''	).to(SCREAMING_SNAKE_CASE	)
    print('''computing perplexity on objective set'''	)
    __UpperCamelCase     :List[str]			  =		compute_perplexity(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	).item()
    print('''perplexity on objective set:'''  ,			SCREAMING_SNAKE_CASE	)

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=15  ,			SCREAMING_SNAKE_CASE=128  ,			SCREAMING_SNAKE_CASE=100  ,			SCREAMING_SNAKE_CASE="igf_model.pt"  ,			):
    '''simple docstring'''







    set_seed(42	)

    # Load pre-trained model
    __UpperCamelCase     :str			  =		GPTaLMHeadModel.from_pretrained('''gpt2'''	)

    # Initialize secondary learner to use embedding weights of model
    __UpperCamelCase     :List[str]			  =		SecondaryLearner(SCREAMING_SNAKE_CASE	)

    # Train secondary learner
    __UpperCamelCase     :Tuple			  =		train_secondary_learner(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			max_epochs=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE  ,			eval_freq=100  ,			igf_model_path=SCREAMING_SNAKE_CASE  ,			)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=32  ,			SCREAMING_SNAKE_CASE=1_000  ,			SCREAMING_SNAKE_CASE=16  ,			SCREAMING_SNAKE_CASE=1.0  ,			SCREAMING_SNAKE_CASE=recopy_gpta  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=10  ,			SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)
    __UpperCamelCase     :Tuple			  =		RandomSampler(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(SCREAMING_SNAKE_CASE  ,			sampler=SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :List[Any]			  =		max_steps // (len(SCREAMING_SNAKE_CASE	)) + 1
    __UpperCamelCase     :Optional[int]			  =		0
    __UpperCamelCase     :int			  =		torch.zeros((1, context_len)  ,			dtype=torch.long  ,			device=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		recopy_model(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    model.train()
    if secondary_learner is not None:
        secondary_learner.to(SCREAMING_SNAKE_CASE	)
        secondary_learner.eval()
    __UpperCamelCase     :List[str]			  =		[]
    __UpperCamelCase     :str			  =		0

    __UpperCamelCase     :int			  =		[]
    __UpperCamelCase     :int			  =		[]

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

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

                # 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:
                    __UpperCamelCase     :List[Any]			  =		-1
                if predicted_q < threshold:
                    __UpperCamelCase     :List[str]			  =		False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()	)	)
                __UpperCamelCase     :int			  =		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()
                __UpperCamelCase     :Any			  =		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:
                    __UpperCamelCase     :Tuple			  =		compute_perplexity(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
                    test_perps.append(SCREAMING_SNAKE_CASE	)

                    print('''Test perplexity, step'''  ,			SCREAMING_SNAKE_CASE  ,			''':'''  ,			SCREAMING_SNAKE_CASE	)
            # 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()  ,			SCREAMING_SNAKE_CASE	)
    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 lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''	)

    # Required parameters
    parser.add_argument(
        '''--data_dir'''  ,			default=SCREAMING_SNAKE_CASE  ,			type=SCREAMING_SNAKE_CASE  ,			required=SCREAMING_SNAKE_CASE  ,			help='''The input data dir. Should contain data files for WikiText.'''  ,			)
    parser.add_argument(
        '''--model_name_or_path'''  ,			default=SCREAMING_SNAKE_CASE  ,			type=SCREAMING_SNAKE_CASE  ,			required=SCREAMING_SNAKE_CASE  ,			help='''Path to pretrained model or model identifier from huggingface.co/models'''  ,			)
    parser.add_argument(
        '''--data_file'''  ,			type=SCREAMING_SNAKE_CASE  ,			default=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			default=SCREAMING_SNAKE_CASE  ,			help='''A jbl file containing the context and information gain pairs to train secondary learner.'''  ,			)

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

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

    parser.add_argument(
        '''--context_len'''  ,			default=32  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			help='''number of articles that are long enough to be used as our objective set'''  ,			)
    parser.add_argument(
        '''--eval_freq'''  ,			default=100  ,			type=SCREAMING_SNAKE_CASE  ,			help='''secondary model evaluation is triggered at eval_freq'''	)

    parser.add_argument('''--max_steps'''  ,			default=1_000  ,			type=SCREAMING_SNAKE_CASE  ,			help='''To calculate training epochs'''	)

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

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

    parser.add_argument(
        '''--eval_interval'''  ,			default=10  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			help='''The number of examples split to be used as objective_set/test_data'''	)

    parser.add_argument(
        '''--min_len'''  ,			default=1_026  ,			type=SCREAMING_SNAKE_CASE  ,			help='''The minimum length of the article to be used as objective set'''	)

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

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

    parser.add_argument(
        '''--threshold'''  ,			default=1.0  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			help='''finetuned_model_name'''	)

    parser.add_argument(
        '''--recopy_model'''  ,			default=SCREAMING_SNAKE_CASE  ,			type=SCREAMING_SNAKE_CASE  ,			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=1_026  ,			trim=SCREAMING_SNAKE_CASE  ,			data_file='''data/tokenized_stories_train_wikitext103.jbl'''  ,			igf_data_file='''igf_context_pairs.jbl'''  ,			)

    # Load train data for secondary learner
    __UpperCamelCase     :Optional[Any]			  =		joblib.load('''data/IGF_values.jbl'''	)

    # Train secondary learner
    __UpperCamelCase     :str			  =		training_secondary_learner(
        SCREAMING_SNAKE_CASE  ,			secondary_learner_max_epochs=15  ,			secondary_learner_batch_size=128  ,			eval_freq=100  ,			igf_model_path='''igf_model.pt'''  ,			)

    # load pretrained gpt2 model
    __UpperCamelCase     :Union[str, Any]			  =		GPTaLMHeadModel.from_pretrained('''gpt2'''	)
    set_seed(42	)

    # Generate train and test data to train and evaluate gpt2 model
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		generate_datasets(
        context_len=32  ,			file='''data/tokenized_stories_train_wikitext103.jbl'''  ,			number=100  ,			min_len=1_026  ,			trim=SCREAMING_SNAKE_CASE	)

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			context_len=32  ,			max_steps=1_000  ,			batch_size=16  ,			threshold=1.0  ,			recopy_model=SCREAMING_SNAKE_CASE  ,			secondary_learner=SCREAMING_SNAKE_CASE  ,			eval_interval=10  ,			finetuned_model_name='''gpt2_finetuned.pt'''  ,			)


if __name__ == "__main__":
    main()










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A__  :  Union[str, Any]								=					R'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''



@add_start_docstrings(UpperCamelCase_						)
class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''rag'''
							_a			      = True




							def __init__(       self     :       str				,				A_     :       List[Any]=None				,				A_     :       str=True				,				A_     :       Tuple=None				,				A_     :       Union[str, Any]=None				,				A_     :       List[str]=None				,				A_     :       List[str]=None				,				A_     :       List[Any]=None				,				A_     :       Union[str, Any]=" / "				,				A_     :       Tuple=" // "				,				A_     :       Any=5				,				A_     :       Optional[Any]=3_0_0				,				A_     :       Tuple=7_6_8				,				A_     :       Union[str, Any]=8				,				A_     :       Dict="wiki_dpr"				,				A_     :       Optional[Any]="train"				,				A_     :       Dict="compressed"				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				A_     :       str=False				,				A_     :       Dict=False				,				A_     :       Dict=0.0				,				A_     :       List[str]=True				,				A_     :       List[str]=False				,				A_     :       List[Any]=False				,				A_     :       Any=False				,				A_     :       Optional[int]=True				,				A_     :       int=None				,				**A_     :       List[str]				,				):
									super().__init__(
									    bos_token_id=A_				,				pad_token_id=A_				,				eos_token_id=A_				,				decoder_start_token_id=A_				,				forced_eos_token_id=A_				,				is_encoder_decoder=A_				,				prefix=A_				,				vocab_size=A_				,				**A_				,				)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									lowerCAmelCase_			:     List[str]									= kwargs.pop('''question_encoder''')
									lowerCAmelCase_			:     Tuple									= question_encoder_config.pop('''model_type''')
									lowerCAmelCase_			:     Tuple									= kwargs.pop('''generator''')
									lowerCAmelCase_			:     Dict									= decoder_config.pop('''model_type''')

									from ..auto.configuration_auto import AutoConfig

									lowerCAmelCase_			:     Union[str, Any]									= AutoConfig.for_model(A_				,				**A_)
									lowerCAmelCase_			:     int									= AutoConfig.for_model(A_				,				**A_)

									lowerCAmelCase_			:     List[Any]									= reduce_loss
									lowerCAmelCase_			:     Optional[Any]									= label_smoothing
									lowerCAmelCase_			:     Union[str, Any]									= exclude_bos_score
									lowerCAmelCase_			:     List[Any]									= do_marginalize

									lowerCAmelCase_			:     int									= title_sep
									lowerCAmelCase_			:     Optional[int]									= doc_sep
									lowerCAmelCase_			:     List[str]									= n_docs
									lowerCAmelCase_			:     int									= max_combined_length

									lowerCAmelCase_			:     Union[str, Any]									= dataset
									lowerCAmelCase_			:     int									= dataset_split
									lowerCAmelCase_			:     Dict									= index_name

									lowerCAmelCase_			:     Union[str, Any]									= retrieval_vector_size
									lowerCAmelCase_			:     Optional[Any]									= retrieval_batch_size
									lowerCAmelCase_			:     List[str]									= passages_path
									lowerCAmelCase_			:     Any									= index_path
									lowerCAmelCase_			:     int									= use_dummy_dataset

									lowerCAmelCase_			:     Tuple									= output_retrieved

									lowerCAmelCase_			:     List[Any]									= do_deduplication

									lowerCAmelCase_			:     Union[str, Any]									= use_cache

									if self.forced_eos_token_id is None:
											lowerCAmelCase_			:     List[Any]									= getattr(self.generator				,				'''forced_eos_token_id'''				,				A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       str				,				A_     :       PretrainedConfig				,				A_     :       PretrainedConfig				,				**A_     :       Any):
									return cls(question_encoder=question_encoder_config.to_dict()				,				generator=generator_config.to_dict()				,				**A_)







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Tuple									= self.question_encoder.to_dict()
									lowerCAmelCase_			:     Dict									= self.generator.to_dict()
									lowerCAmelCase_			:     str									= self.__class__.model_type
									return output





