




"""simple docstring"""





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 					_UpperCamelCase    (			UpperCamelCase=32				,       UpperCamelCase=10				,       UpperCamelCase=100				,       UpperCamelCase=1026				,       UpperCamelCase=True				,       UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"				,       UpperCamelCase="igf_context_pairs.jbl"				,       )			->			Dict:


			"""simple docstring"""
			set_seed(3							)
			# generate train_data and objective_set
			__UpperCAmelCase	: Dict		=					generate_datasets(
			    UpperCamelCase				,       UpperCamelCase				,       number=UpperCamelCase				,       min_len=1026				,       trim=UpperCamelCase							)
			# 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	: Tuple		=					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"							)

			# load pretrained model
			__UpperCAmelCase	: List[str]		=					load_gpta("gpt2"							).to(UpperCamelCase							)
			print("computing perplexity on objective set"							)
			__UpperCAmelCase	: Dict		=					compute_perplexity(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							).item()
			print("perplexity on objective set:"				,       UpperCamelCase							)

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

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

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=15				,       UpperCamelCase=128				,       UpperCamelCase=100				,       UpperCamelCase="igf_model.pt"				,       )			->			Optional[Any]:


			"""simple docstring"""
			set_seed(42							)

			# Load pre-trained model
			__UpperCAmelCase	: Any		=					GPTaLMHeadModel.from_pretrained("gpt2"							)

			# Initialize secondary learner to use embedding weights of model
			__UpperCAmelCase	: Optional[int]		=					SecondaryLearner(UpperCamelCase							)

			# Train secondary learner
			__UpperCAmelCase	: Optional[int]		=					train_secondary_learner(
			    UpperCamelCase				,       UpperCamelCase				,       max_epochs=UpperCamelCase				,       batch_size=UpperCamelCase				,       eval_freq=100				,       igf_model_path=UpperCamelCase				,       )

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=32				,       UpperCamelCase=1000				,       UpperCamelCase=16				,       UpperCamelCase=1.0				,       UpperCamelCase=recopy_gpta				,       UpperCamelCase=None				,       UpperCamelCase=10				,       UpperCamelCase="gpt2_finetuned.pt"				,       )			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Any		=					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"							)
			__UpperCAmelCase	: List[Any]		=					RandomSampler(UpperCamelCase							)
			__UpperCAmelCase	: Dict		=					DataLoader(UpperCamelCase				,       sampler=UpperCamelCase							)

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

			model.train()
			if secondary_learner is not None:
						secondary_learner.to(UpperCamelCase							)
						secondary_learner.eval()
			__UpperCAmelCase	: Dict		=					[]
			__UpperCAmelCase	: List[Any]		=					0

			__UpperCAmelCase	: Union[str, Any]		=					[]
			__UpperCAmelCase	: List[Any]		=					[]

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

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

												# 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	: str		=					-1
												if predicted_q < threshold:
															__UpperCAmelCase	: Optional[int]		=					False

            # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()							)							)
												__UpperCAmelCase	: Dict		=					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	: Any		=					compute_perplexity(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
															test_perps.append(UpperCamelCase							)

															print("Test perplexity, step"				,       UpperCamelCase				,       ":"				,       UpperCamelCase							)
            # 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()				,       UpperCamelCase							)
			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 					_UpperCamelCase    (			)			->			Tuple:


			"""simple docstring"""
			__UpperCAmelCase	: Union[str, Any]		=					argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"							)

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

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

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

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

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

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

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

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

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

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

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

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

			parser.add_argument(
			    "--recopy_model"				,       default=UpperCamelCase				,       type=UpperCamelCase				,       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=UpperCamelCase				,       data_file="data/tokenized_stories_train_wikitext103.jbl"				,       igf_data_file="igf_context_pairs.jbl"				,       )

			# Load train data for secondary learner
			__UpperCAmelCase	: List[str]		=					joblib.load("data/IGF_values.jbl"							)

			# Train secondary learner
			__UpperCAmelCase	: int		=					training_secondary_learner(
			    UpperCamelCase				,       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	: str		=					generate_datasets(
			    context_len=32				,       file="data/tokenized_stories_train_wikitext103.jbl"				,       number=100				,       min_len=1026				,       trim=UpperCamelCase							)

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


if __name__ == "__main__":
					main()








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


