

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str]=32  ,      lowerCAmelCase:  Union[str, Any]=10  ,      lowerCAmelCase:  int=100  ,      lowerCAmelCase:  List[str]=1026  ,      lowerCAmelCase:  Dict=True  ,      lowerCAmelCase:  int="data/tokenized_stories_train_wikitext103.jbl"  ,      lowerCAmelCase:  Union[str, Any]="igf_context_pairs.jbl"  ,      )   ->       Union[str, Any]:
				set_seed(3 )
				# generate train_data and objective_set
				_UpperCAmelCase       :		Tuple 		=    generate_datasets(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      number=lowerCAmelCase  ,      min_len=1026  ,      trim=lowerCAmelCase )
				# 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[str] 		=    torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

				# load pretrained model
				_UpperCAmelCase       :		List[str] 		=    load_gpta("gpt2" ).to(lowerCAmelCase )
				print("computing perplexity on objective set" )
				_UpperCAmelCase       :		Union[str, Any] 		=    compute_perplexity(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase ).item()
				print("perplexity on objective set:"  ,      lowerCAmelCase )

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

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Any=15  ,      lowerCAmelCase:  List[Any]=128  ,      lowerCAmelCase:  Dict=100  ,      lowerCAmelCase:  Any="igf_model.pt"  ,      )   ->       Optional[int]:
				set_seed(42 )

				# Load pre-trained model
				_UpperCAmelCase       :		Union[str, Any] 		=    GPTaLMHeadModel.from_pretrained("gpt2" )

				# Initialize secondary learner to use embedding weights of model
				_UpperCAmelCase       :		Dict 		=    SecondaryLearner(lowerCAmelCase )

				# Train secondary learner
				_UpperCAmelCase       :		Union[str, Any] 		=    train_secondary_learner(
				    lowerCAmelCase  ,      lowerCAmelCase  ,      max_epochs=lowerCAmelCase  ,      batch_size=lowerCAmelCase  ,      eval_freq=100  ,      igf_model_path=lowerCAmelCase  ,      )

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str]  ,      lowerCAmelCase:  Union[str, Any]=32  ,      lowerCAmelCase:  Optional[Any]=1000  ,      lowerCAmelCase:  str=16  ,      lowerCAmelCase:  Union[str, Any]=1.0  ,      lowerCAmelCase:  str=recopy_gpta  ,      lowerCAmelCase:  Dict=None  ,      lowerCAmelCase:  Optional[int]=10  ,      lowerCAmelCase:  Union[str, Any]="gpt2_finetuned.pt"  ,      )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
				_UpperCAmelCase       :		Optional[Any] 		=    RandomSampler(lowerCAmelCase )
				_UpperCAmelCase       :		Optional[Any] 		=    DataLoader(lowerCAmelCase  ,      sampler=lowerCAmelCase )

				_UpperCAmelCase       :		Union[str, Any] 		=    max_steps // (len(lowerCAmelCase )) + 1
				_UpperCAmelCase       :		List[str] 		=    0
				_UpperCAmelCase       :		List[str] 		=    torch.zeros((1, context_len)  ,      dtype=torch.long  ,      device=lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    recopy_model(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )

				model.train()
				if secondary_learner is not None:
								secondary_learner.to(lowerCAmelCase )
								secondary_learner.eval()
				_UpperCAmelCase       :		Optional[int] 		=    []
				_UpperCAmelCase       :		List[Any] 		=    0

				_UpperCAmelCase       :		Tuple 		=    []
				_UpperCAmelCase       :		Tuple 		=    []

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

												if secondary_learner is not None:
																_UpperCAmelCase       :		Union[str, Any] 		=    secondary_learner.forward(
																    torch.tensor(lowerCAmelCase  ,      dtype=torch.long  ,      device=lowerCAmelCase ).unsqueeze(0 ) )[0].item()
																observed_qs.append(float(lowerCAmelCase ) )

																# 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       :		int 		=    -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       :		List[Any] 		=    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       :		Dict 		=    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       :		Optional[int] 		=    compute_perplexity(lowerCAmelCase  ,      lowerCAmelCase  ,      lowerCAmelCase )
																				test_perps.append(lowerCAmelCase )

																				print("Test perplexity, step"  ,      lowerCAmelCase  ,      ":"  ,      lowerCAmelCase )
            # 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()  ,      lowerCAmelCase )
				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 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		Optional[Any] 		=    argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

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

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

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

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

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

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

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

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

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

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

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

				parser.add_argument(
				    "--threshold"  ,      default=1.0  ,      type=lowerCAmelCase  ,      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=lowerCAmelCase  ,      help="finetuned_model_name" )

				parser.add_argument(
				    "--recopy_model"  ,      default=lowerCAmelCase  ,      type=lowerCAmelCase  ,      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=lowerCAmelCase  ,      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       :		List[str] 		=    training_secondary_learner(
				    lowerCAmelCase  ,      secondary_learner_max_epochs=15  ,      secondary_learner_batch_size=128  ,      eval_freq=100  ,      igf_model_path="igf_model.pt"  ,      )

				# load pretrained gpt2 model
				_UpperCAmelCase       :		Optional[Any] 		=    GPTaLMHeadModel.from_pretrained("gpt2" )
				set_seed(42 )

				# Generate train and test data to train and evaluate gpt2 model
				_UpperCAmelCase       :		Union[str, Any] 		=    generate_datasets(
				    context_len=32  ,      file="data/tokenized_stories_train_wikitext103.jbl"  ,      number=100  ,      min_len=1026  ,      trim=lowerCAmelCase )

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


if __name__ == "__main__":
					main()





import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


