
"""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      lowerCAmelCase__			(		__magic_name__=3_2			,   __magic_name__=1_0			,   __magic_name__=1_0_0			,   __magic_name__=1_0_2_6			,   __magic_name__=True			,   __magic_name__="data/tokenized_stories_train_wikitext103.jbl"			,   __magic_name__="igf_context_pairs.jbl"			,   )						->Dict:
			set_seed(3      )
			# generate train_data and objective_set
			__lowercase   ,					__lowercase       =       generate_datasets(
			    __magic_name__			,   __magic_name__			,   number=__magic_name__			,   min_len=1_0_2_6			,   trim=__magic_name__      )
			# 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       =       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"      )

			# load pretrained model
			__lowercase       =       load_gpta("gpt2"      ).to(__magic_name__      )
			print("computing perplexity on objective set"      )
			__lowercase       =       compute_perplexity(__magic_name__			,   __magic_name__			,   __magic_name__      ).item()
			print("perplexity on objective set:"			,   __magic_name__      )

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

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



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__=1_5			,   __magic_name__=1_2_8			,   __magic_name__=1_0_0			,   __magic_name__="igf_model.pt"			,   )						->Tuple:
			set_seed(4_2      )

			# Load pre-trained model
			__lowercase       =       GPTaLMHeadModel.from_pretrained("gpt2"      )

			# Initialize secondary learner to use embedding weights of model
			__lowercase       =       SecondaryLearner(__magic_name__      )

			# Train secondary learner
			__lowercase       =       train_secondary_learner(
			    __magic_name__			,   __magic_name__			,   max_epochs=__magic_name__			,   batch_size=__magic_name__			,   eval_freq=1_0_0			,   igf_model_path=__magic_name__			,   )

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__=3_2			,   __magic_name__=1_0_0_0			,   __magic_name__=1_6			,   __magic_name__=1.0			,   __magic_name__=recopy_gpta			,   __magic_name__=None			,   __magic_name__=1_0			,   __magic_name__="gpt2_finetuned.pt"			,   )						->Optional[Any]:
			__lowercase       =       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"      )
			__lowercase       =       RandomSampler(__magic_name__      )
			__lowercase       =       DataLoader(__magic_name__			,   sampler=__magic_name__      )

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

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

			__lowercase       =       []
			__lowercase       =       []

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

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

												# 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 == 1_0:
															__lowercase       =       -1
												if predicted_q < threshold:
															__lowercase       =       False

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

															print("Test perplexity, step"			,   __magic_name__			,   ":"			,   __magic_name__      )
            # Break out of the loop after 60 batches
									if max_steps > 0 and global_step > 6_0:
												break
						if max_steps > 0 and global_step > 6_0:
									break

    # save finetuned transformer model
			torch.save(model.state_dict()			,   __magic_name__      )
			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__			(		)						->Optional[Any]:
			__lowercase       =       argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"      )

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

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

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

			parser.add_argument(
			    "--context_len"			,   default=3_2			,   type=__magic_name__			,   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=1_0_0			,   type=__magic_name__			,   help="number of articles that are long enough to be used as our objective set"			,   )
			parser.add_argument(
			    "--eval_freq"			,   default=1_0_0			,   type=__magic_name__			,   help="secondary model evaluation is triggered at eval_freq"      )

			parser.add_argument("--max_steps"			,   default=1_0_0_0			,   type=__magic_name__			,   help="To calculate training epochs"      )

			parser.add_argument(
			    "--secondary_learner_batch_size"			,   default=1_2_8			,   type=__magic_name__			,   help="batch size of training data for secondary learner"			,   )

			parser.add_argument(
			    "--batch_size"			,   default=1_6			,   type=__magic_name__			,   help="batch size of training data of language model(gpt2) "      )

			parser.add_argument(
			    "--eval_interval"			,   default=1_0			,   type=__magic_name__			,   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=1_0_0			,   type=__magic_name__			,   help="The number of examples split to be used as objective_set/test_data"      )

			parser.add_argument(
			    "--min_len"			,   default=1_0_2_6			,   type=__magic_name__			,   help="The minimum length of the article to be used as objective set"      )

			parser.add_argument(
			    "--secondary_learner_max_epochs"			,   default=1_5			,   type=__magic_name__			,   help="number of epochs to train secondary learner"      )

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

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

			parser.add_argument(
			    "--recopy_model"			,   default=__magic_name__			,   type=__magic_name__			,   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=3_2			,   max_steps=1_0			,   size_objective_set=1_0_0			,   min_len=1_0_2_6			,   trim=__magic_name__			,   data_file="data/tokenized_stories_train_wikitext103.jbl"			,   igf_data_file="igf_context_pairs.jbl"			,   )

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

			# Train secondary learner
			__lowercase       =       training_secondary_learner(
			    __magic_name__			,   secondary_learner_max_epochs=1_5			,   secondary_learner_batch_size=1_2_8			,   eval_freq=1_0_0			,   igf_model_path="igf_model.pt"			,   )

			# load pretrained gpt2 model
			__lowercase       =       GPTaLMHeadModel.from_pretrained("gpt2"      )
			set_seed(4_2      )

			# Generate train and test data to train and evaluate gpt2 model
			__lowercase   ,					__lowercase       =       generate_datasets(
			    context_len=3_2			,   file="data/tokenized_stories_train_wikitext103.jbl"			,   number=1_0_0			,   min_len=1_0_2_6			,   trim=__magic_name__      )

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    __magic_name__			,   __magic_name__			,   __magic_name__			,   context_len=3_2			,   max_steps=1_0_0_0			,   batch_size=1_6			,   threshold=1.0			,   recopy_model=__magic_name__			,   secondary_learner=__magic_name__			,   eval_interval=1_0			,   finetuned_model_name="gpt2_finetuned.pt"			,   )


if __name__ == "__main__":
					main()







"""simple docstring"""






import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       np.array([[1, item, train_mtch[i]] for i, item in enumerate(__magic_name__      )]      )
			__lowercase       =       np.array(__magic_name__      )
			__lowercase       =       np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()			,   __magic_name__      )      )			,   x.transpose()      )			,   __magic_name__      )
			return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       (1, 2, 1)
			__lowercase       =       (1, 1, 0, 7)
			__lowercase       =       SARIMAX(
			    __magic_name__			,   exog=__magic_name__			,   order=__magic_name__			,   seasonal_order=__magic_name__      )
			__lowercase       =       model.fit(disp=__magic_name__			,   maxiter=6_0_0			,   method="nm"      )
			__lowercase       =       model_fit.predict(1			,   len(__magic_name__      )			,   exog=[test_match]      )
			return result[0]



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__      )						->float:
			__lowercase       =       SVR(kernel="rbf"			,   C=1			,   gamma=0.1			,   epsilon=0.1      )
			regressor.fit(__magic_name__			,   __magic_name__      )
			__lowercase       =       regressor.predict(__magic_name__      )
			return y_pred[0]



def      lowerCAmelCase__			(		__magic_name__      )						->float:
			train_user.sort()
			__lowercase       =       np.percentile(__magic_name__			,   2_5      )
			__lowercase       =       np.percentile(__magic_name__			,   7_5      )
			__lowercase       =       qa - qa
			__lowercase       =       qa - (iqr * 0.1)
			return low_lim



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->bool:
			__lowercase       =       0
			__lowercase       =       0
			for i in list_vote:
						if i > actual_result:
									__lowercase       =       not_safe + 1
						else:
									if abs(abs(__magic_name__      ) - abs(__magic_name__      )      ) <= 0.1:
												safe += 1
									else:
												not_safe += 1
			return safe > not_safe


if __name__ == "__main__":
					# data_input_df = pd.read_csv("ex_data.csv", header=None)
					_lowercase							=				[[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
					_lowercase							=				pd.DataFrame(
					    data_input, columns=['''total_user''', '''total_even''', '''days''']
					)
					_lowercase							=				Normalizer().fit_transform(data_input_df.values)
					# split data
					_lowercase							=				normalize_df[:, 2].tolist()
					_lowercase							=				normalize_df[:, 0].tolist()
					_lowercase							=				normalize_df[:, 1].tolist()

					# for svr (input variable = total date and total match)
					_lowercase							=				normalize_df[:, [1, 2]].tolist()
					_lowercase							=				x[: len(x) - 1]
					_lowercase							=				x[len(x) - 1 :]

					# for linear regression & sarimax
					_lowercase							=				total_date[: len(total_date) - 1]
					_lowercase							=				total_user[: len(total_user) - 1]
					_lowercase							=				total_match[: len(total_match) - 1]

					_lowercase							=				total_date[len(total_date) - 1 :]
					_lowercase							=				total_user[len(total_user) - 1 :]
					_lowercase							=				total_match[len(total_match) - 1 :]

					# voting system with forecasting
					_lowercase							=				[
					    linear_regression_prediction(
					        trn_date, trn_user, trn_match, tst_date, tst_match
					    ),
					    sarimax_predictor(trn_user, trn_match, tst_match),
					    support_vector_regressor(x_train, x_test, trn_user),
					]

					# check the safety of today's data
					_lowercase							=				'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
					print('''Today\'s data is {not_str}safe.''')





