



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					(   lowerCamelCase__=32     , lowerCamelCase__=10     , lowerCamelCase__=100     , lowerCamelCase__=1_026     , lowerCamelCase__=True     , lowerCamelCase__="data/tokenized_stories_train_wikitext103.jbl"     , lowerCamelCase__="igf_context_pairs.jbl"     , ):

				"""simple docstring"""


				set_seed(3						)
				# generate train_data and objective_set
				lowercase__     :    Optional[Any]													=     generate_datasets(
				    lowerCamelCase__     , lowerCamelCase__     , number=lowerCamelCase__     , min_len=1_026     , 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?
				lowercase__     :    Any													=     torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)

				# load pretrained model
				lowercase__     :    Optional[int]													=     load_gpta("gpt2"						).to(lowerCamelCase__						)
				print("computing perplexity on objective set"						)
				lowercase__     :    Dict													=     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 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__=15     , lowerCamelCase__=128     , lowerCamelCase__=100     , lowerCamelCase__="igf_model.pt"     , ):

				"""simple docstring"""


				set_seed(42						)

				# Load pre-trained model
				lowercase__     :    Optional[int]													=     GPTaLMHeadModel.from_pretrained("gpt2"						)

				# Initialize secondary learner to use embedding weights of model
				lowercase__     :    List[Any]													=     SecondaryLearner(lowerCamelCase__						)

				# Train secondary learner
				lowercase__     :    List[str]													=     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 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__=32     , lowerCamelCase__=1_000     , lowerCamelCase__=16     , lowerCamelCase__=1.0     , lowerCamelCase__=recopy_gpta     , lowerCamelCase__=None     , lowerCamelCase__=10     , lowerCamelCase__="gpt2_finetuned.pt"     , ):

				"""simple docstring"""


				lowercase__     :    List[str]													=     torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)
				lowercase__     :    Union[str, Any]													=     RandomSampler(lowerCamelCase__						)
				lowercase__     :    str													=     DataLoader(lowerCamelCase__     , sampler=lowerCamelCase__						)

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

				model.train()
				if secondary_learner is not None:
								secondary_learner.to(lowerCamelCase__						)
								secondary_learner.eval()
				lowercase__     :    List[str]													=     []
				lowercase__     :    Any													=     0

				lowercase__     :    Optional[int]													=     []
				lowercase__     :    List[Any]													=     []

				# Compute the performance of the transformer model at the beginning
				lowercase__     :    Optional[int]													=     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()
												lowercase__     :    Optional[int]													=     random.randint(0     , example.size(2						) - context_len - 1						)
												lowercase__     :    Dict													=     example[0, 0, start : start + context_len]
												lm_optimizer.zero_grad()
												lowercase__     :    int													=     model(lowerCamelCase__     , labels=lowerCamelCase__						)
												lowercase__     :    Dict													=     True

												if secondary_learner is not None:
																lowercase__     :    str													=     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:
																				lowercase__     :    List[str]													=     -1
																if predicted_q < threshold:
																				lowercase__     :    str													=     False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()						)						)
																lowercase__     :    Optional[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()
																lowercase__     :    Optional[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:
																				lowercase__     :    Any													=     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 					__lowerCamelCase					(   ):

				"""simple docstring"""


				lowercase__     :    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=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=1_000     , 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=1_026     , 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=1_026     , trim=lowerCamelCase__     , data_file="data/tokenized_stories_train_wikitext103.jbl"     , igf_data_file="igf_context_pairs.jbl"     , )

				# Load train data for secondary learner
				lowercase__     :    int													=     joblib.load("data/IGF_values.jbl"						)

				# Train secondary learner
				lowercase__     :    List[Any]													=     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
				lowercase__     :    List[Any]													=     GPTaLMHeadModel.from_pretrained("gpt2"						)
				set_seed(42						)

				# Generate train and test data to train and evaluate gpt2 model
				lowercase__     :    List[str]													=     generate_datasets(
				    context_len=32     , file="data/tokenized_stories_train_wikitext103.jbl"     , number=100     , min_len=1_026     , trim=lowerCamelCase__						)

				# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
				finetune(
				    lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__     , context_len=32     , max_steps=1_000     , 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()












'''simple docstring'''







_A	:			Union[str, Any]         =range(2, 20 + 1)
_A	:			List[str]         =[10**k for k in range(ks[-1] + 1)]
_A	:			dict[int, dict[int, list[list[int]]]]         ={}





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							lowerCamelCase__					:	List[str]         =       sum(a_i[j] for j in range(UpperCamelCase				,			len(UpperCamelCase      )      )      )
							lowerCamelCase__					:	int         =       sum(a_i[j] * base[j] for j in range(min(len(UpperCamelCase      )				,			UpperCamelCase      )      )      )

							lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       0, 0
							lowerCamelCase__					:	List[str]         =       n - i

							lowerCamelCase__					:	Optional[Any]         =       memo.get(UpperCamelCase      )

							if sub_memo is not None:
														lowerCamelCase__					:	str         =       sub_memo.get(UpperCamelCase      )

														if jumps is not None and len(UpperCamelCase      ) > 0:
																					# find and make the largest jump without going over
																					lowerCamelCase__					:	Optional[Any]         =       -1
																					for _k in range(len(UpperCamelCase      ) - 1				,			-1				,			-1      ):
																												if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																																			lowerCamelCase__					:	Dict         =       _k
																																			break

																					if max_jump >= 0:
																												lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	str         =       jumps[max_jump]
																												# since the difference between jumps is cached, add c
																												lowerCamelCase__					:	Dict         =       diff + c
																												for j in range(min(UpperCamelCase				,			len(UpperCamelCase      )      )      ):
																																			lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       divmod(UpperCamelCase				,			10      )
																												if new_c > 0:
																																			add(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )

														else:
																					lowerCamelCase__					:	Any         =       []
							else:
														lowerCamelCase__					:	str         =       {c: []}
														lowerCamelCase__					:	Tuple         =       sub_memo

							if dn >= max_dn or c + diff >= base[k]:
														return diff, dn

							if k > ks[0]:
														while True:
																					# keep doing smaller jumps
																					lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       next_term(UpperCamelCase				,			k - 1				,			i + dn				,			UpperCamelCase      )
																					diff += _diff
																					dn += terms_jumped

																					if dn >= max_dn or c + diff >= base[k]:
																												break
							else:
														# would be too small a jump, just compute sequential terms instead
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       compute(UpperCamelCase				,			UpperCamelCase				,			i + dn				,			UpperCamelCase      )
														diff += _diff
														dn += terms_jumped

							lowerCamelCase__					:	str         =       sub_memo[c]

							# keep jumps sorted by # of terms skipped
							lowerCamelCase__					:	List[Any]         =       0
							while j < len(UpperCamelCase      ):
														if jumps[j][1] > dn:
																					break
														j += 1

							# cache the jump for this value digitsum(b) and c
							sub_memo[c].insert(UpperCamelCase				,			(diff, dn, k)      )
							return (diff, dn)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							if i >= n:
														return 0, i
							if k > len(UpperCamelCase      ):
														a_i.extend([0 for _ in range(k - len(UpperCamelCase      )      )]      )

							# note: a_i -> b * 10^k + c
							# ds_b -> digitsum(b)
							# ds_c -> digitsum(c)
							lowerCamelCase__					:	Optional[Any]         =       i
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       0, 0, 0
							for j in range(len(UpperCamelCase      )      ):
														if j >= k:
																					ds_b += a_i[j]
														else:
																					ds_c += a_i[j]

							while i < n:
														i += 1
														lowerCamelCase__					:	Optional[int]         =       ds_c + ds_b
														diff += addend
														lowerCamelCase__					:	int         =       0
														for j in range(UpperCamelCase      ):
																					lowerCamelCase__					:	str         =       a_i[j] + addend
																					lowerCamelCase__  ,		lowerCamelCase__					:	int         =       divmod(UpperCamelCase				,			10      )

																					ds_c += a_i[j]

														if addend > 0:
																					break

							if addend > 0:
														add(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
							return diff, i - start_i





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       str:
							for j in range(UpperCamelCase				,			len(UpperCamelCase      )      ):
														lowerCamelCase__					:	List[Any]         =       digits[j] + addend
														if s >= 10:
																					lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       divmod(UpperCamelCase				,			10      )
																					lowerCamelCase__					:	Any         =       addend // 10 + quotient
														else:
																					lowerCamelCase__					:	Any         =       s
																					lowerCamelCase__					:	Optional[Any]         =       addend // 10

														if addend == 0:
																					break

							while addend > 0:
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       divmod(UpperCamelCase				,			10      )
														digits.append(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase = 10**15      )      ->       int:
							lowerCamelCase__					:	Any         =       [1]
							lowerCamelCase__					:	List[str]         =       1
							lowerCamelCase__					:	Tuple         =       0
							while True:
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       next_term(UpperCamelCase				,			20				,			i + dn				,			UpperCamelCase      )
														dn += terms_jumped
														if dn == n - i:
																					break

							lowerCamelCase__					:	Union[str, Any]         =       0
							for j in range(len(UpperCamelCase      )      ):
														a_n += digits[j] * 10**j
							return a_n


if __name__ == "__main__":
			print(F'{solution() = }')

