
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowercase						:      Tuple									= logging.get_logger(__name__)

lowercase						:      Optional[int]									= {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

lowercase						:      int									= {
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

lowercase						:      List[str]									= {
    'jukebox': 512,
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  VOCAB_FILES_NAMES
	_A			=  PRETRAINED_VOCAB_FILES_MAP
	_A			=  PRETRAINED_LYRIC_TOKENS_SIZES
	_A			=  ['input_ids', 'attention_mask']







	def __init__(      self	:int  ,     a	:Union[str, Any]  ,     a	:Union[str, Any]  ,     a	:List[str]  ,     a	:Optional[Any]=["v3", "v2", "v2"]  ,     a	:Any=5_1_2  ,     a	:str=5  ,     a	:Tuple="<|endoftext|>"  ,     **a	:Optional[int]  ,     )	->				Any:
								__UpperCamelCase			:     Any         =      AddedToken(a  ,     lstrip=a  ,     rstrip=a		) if isinstance(a  ,     a		) else unk_token
								super().__init__(
								    unk_token=a  ,     n_genres=a  ,     version=a  ,     max_n_lyric_tokens=a  ,     **a  ,     )
								__UpperCamelCase			:     Optional[int]         =      version
								__UpperCamelCase			:     int         =      max_n_lyric_tokens
								__UpperCamelCase			:     Optional[int]         =      n_genres

								with open(a  ,     encoding="utf-8"		) as vocab_handle:
															__UpperCamelCase			:     List[str]         =      json.load(a		)

								with open(a  ,     encoding="utf-8"		) as vocab_handle:
															__UpperCamelCase			:     Dict         =      json.load(a		)

								with open(a  ,     encoding="utf-8"		) as vocab_handle:
															__UpperCamelCase			:     List[Any]         =      json.load(a		)

								__UpperCamelCase			:     Tuple         =      r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
								# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
								if len(self.lyrics_encoder		) == 7_9:
															__UpperCamelCase			:     Optional[Any]         =      oov.replace(r"\-'"  ,     r"\-+'"		)

								__UpperCamelCase			:     Optional[int]         =      regex.compile(a		)
								__UpperCamelCase			:     Optional[int]         =      {v: k for k, v in self.artists_encoder.items()}
								__UpperCamelCase			:     Union[str, Any]         =      {v: k for k, v in self.genres_encoder.items()}
								__UpperCamelCase			:     Any         =      {v: k for k, v in self.lyrics_encoder.items()}







	@property
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[Any]:
								return len(self.artists_encoder		) + len(self.genres_encoder		) + len(self.lyrics_encoder		)







	def      _lowerCamelCase       (      self	:List[Any]		)	->				str:
								return dict(self.artists_encoder  ,     self.genres_encoder  ,     self.lyrics_encoder		)







	def      _lowerCamelCase       (      self	:Dict  ,     a	:List[str]  ,     a	:Union[str, Any]  ,     a	:Tuple		)	->				Tuple:
								__UpperCamelCase			:     Union[str, Any]         =      [self.artists_encoder.get(a  ,     0		) for artist in list_artists]
								for genres in range(len(a		)		):
															__UpperCamelCase			:     List[Any]         =      [self.genres_encoder.get(a  ,     0		) for genre in list_genres[genres]]
															__UpperCamelCase			:     Optional[Any]         =      list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]		))

								__UpperCamelCase			:     Any         =      [[self.lyrics_encoder.get(a  ,     0		) for character in list_lyrics[0]], [], []]
								return artists_id, list_genres, lyric_ids







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Union[str, Any]		)	->				Tuple:
								return list(a		)







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:List[str]  ,     a	:Any  ,     a	:Dict  ,     **a	:List[str]		)	->				List[str]:
								__UpperCamelCase			:     List[str]         =      self.prepare_for_tokenization(a  ,     a  ,     a		)
								__UpperCamelCase			:     List[Any]         =      self._tokenize(a		)
								return artist, genre, lyrics







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:str  ,     a	:str  ,     a	:str  ,     a	:bool = False		)	->				Tuple[str, str, str, Dict[str, Any]]:
								for idx in range(len(self.version		)		):
															if self.version[idx] == "v3":
																						__UpperCamelCase			:     int         =      artists[idx].lower()
																						__UpperCamelCase			:     str         =      [genres[idx].lower()]
															else:
																						__UpperCamelCase			:     List[Any]         =      self._normalize(artists[idx]		) + ".v2"
																						__UpperCamelCase			:     List[str]         =      [
																						    self._normalize(a		) + ".v2" for genre in genres[idx].split("_"		)
																						]  # split is for the full dictionary with combined genres

								if self.version[0] == "v2":
															__UpperCamelCase			:     Union[str, Any]         =      regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"		)
															__UpperCamelCase			:     int         =      "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
															__UpperCamelCase			:     List[Any]         =      {vocab[index]: index + 1 for index in range(len(a		)		)}
															__UpperCamelCase			:     List[str]         =      0
															__UpperCamelCase			:     Optional[int]         =      len(a		) + 1
															__UpperCamelCase			:     int         =      self.vocab
															__UpperCamelCase			:     List[Any]         =      {v: k for k, v in self.vocab.items()}
															__UpperCamelCase			:     int         =      ""
								else:
															__UpperCamelCase			:     Optional[Any]         =      regex.compile(r"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"		)

								__UpperCamelCase			:     List[Any]         =      self._run_strip_accents(a		)
								__UpperCamelCase			:     str         =      lyrics.replace("\\"  ,     "\n"		)
								__UpperCamelCase			:     Optional[int]         =      self.out_of_vocab.sub(""  ,     a		), [], []
								return artists, genres, lyrics







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:List[str]		)	->				Union[str, Any]:
								__UpperCamelCase			:     Any         =      unicodedata.normalize("NFD"  ,     a		)
								__UpperCamelCase			:     List[str]         =      []
								for char in text:
															__UpperCamelCase			:     List[str]         =      unicodedata.category(a		)
															if cat == "Mn":
																						continue
															output.append(a		)
								return "".join(a		)







	def      _lowerCamelCase       (      self	:Any  ,     a	:str		)	->				str:
								__UpperCamelCase			:     Any         =      (
								    [chr(a		) for i in range(ord("a"		)  ,     ord("z"		) + 1		)]
								    + [chr(a		) for i in range(ord("A"		)  ,     ord("Z"		) + 1		)]
								    + [chr(a		) for i in range(ord("0"		)  ,     ord("9"		) + 1		)]
								    + ["."]
								)
								__UpperCamelCase			:     Tuple         =      frozenset(a		)
								__UpperCamelCase			:     Dict         =      re.compile(r"_+"		)
								__UpperCamelCase			:     Optional[int]         =      "".join([c if c in accepted else "_" for c in text.lower()]		)
								__UpperCamelCase			:     Tuple         =      pattern.sub("_"  ,     a		).strip("_"		)
								return text







	def      _lowerCamelCase       (      self	:Dict  ,     a	:List[str]		)	->				str:
								return " ".join(a		)







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:int  ,     a	:Optional[Union[str, TensorType]] = None  ,     a	:bool = False		)	->				Dict:
								# Convert to TensorType
								if not isinstance(a  ,     a		):
															__UpperCamelCase			:     Dict         =      TensorType(a		)

								# Get a function reference for the correct framework
								if tensor_type == TensorType.TENSORFLOW:
															if not is_tf_available():
																						raise ImportError(
																						    "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."		)
															import tensorflow as tf

															__UpperCamelCase			:     int         =      tf.constant
															__UpperCamelCase			:     Optional[int]         =      tf.is_tensor
								elif tensor_type == TensorType.PYTORCH:
															if not is_torch_available():
																						raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed."		)
															import torch

															__UpperCamelCase			:     Optional[int]         =      torch.tensor
															__UpperCamelCase			:     int         =      torch.is_tensor
								elif tensor_type == TensorType.JAX:
															if not is_flax_available():
																						raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed."		)
															import jax.numpy as jnp  # noqa: F811

															__UpperCamelCase			:     Dict         =      jnp.array
															__UpperCamelCase			:     Dict         =      _is_jax
								else:
															__UpperCamelCase			:     Union[str, Any]         =      np.asarray
															__UpperCamelCase			:     Union[str, Any]         =      _is_numpy

								# Do the tensor conversion in batch

								try:
															if prepend_batch_axis:
																						__UpperCamelCase			:     List[str]         =      [inputs]

															if not is_tensor(a		):
																						__UpperCamelCase			:     int         =      as_tensor(a		)
								except:  # noqa E722
															raise ValueError(
															    "Unable to create tensor, you should probably activate truncation and/or padding "
															    "with 'padding=True' 'truncation=True' to have batched tensors with the same length."		)

								return inputs







	def __call__(      self	:str  ,     a	:Tuple  ,     a	:List[str]  ,     a	:str=""  ,     a	:Optional[Any]="pt"		)	->				BatchEncoding:
								__UpperCamelCase			:     Any         =      [0, 0, 0]
								__UpperCamelCase			:     Dict         =      [artist] * len(self.version		)
								__UpperCamelCase			:     Any         =      [genres] * len(self.version		)

								__UpperCamelCase			:     Optional[Any]         =      self.tokenize(a  ,     a  ,     a		)
								__UpperCamelCase			:     List[str]         =      self._convert_token_to_id(a  ,     a  ,     a		)

								__UpperCamelCase			:     Union[str, Any]         =      [-INFINITY] * len(full_tokens[-1]		)
								__UpperCamelCase			:     Dict         =      [
								    self.convert_to_tensors(
								        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]  ,     tensor_type=a		)
								    for i in range(len(self.version		)		)
								]
								return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks}		)







	def      _lowerCamelCase       (      self	:str  ,     a	:str  ,     a	:Optional[str] = None		)	->				Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return

								__UpperCamelCase			:     Union[str, Any]         =      os.path.join(
								    a  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"]		)
								with open(a  ,     "w"  ,     encoding="utf-8"		) as f:
															f.write(json.dumps(self.artists_encoder  ,     ensure_ascii=a		)		)

								__UpperCamelCase			:     Any         =      os.path.join(
								    a  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"]		)
								with open(a  ,     "w"  ,     encoding="utf-8"		) as f:
															f.write(json.dumps(self.genres_encoder  ,     ensure_ascii=a		)		)

								__UpperCamelCase			:     Tuple         =      os.path.join(
								    a  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"]		)
								with open(a  ,     "w"  ,     encoding="utf-8"		) as f:
															f.write(json.dumps(self.lyrics_encoder  ,     ensure_ascii=a		)		)

								return (artists_file, genres_file, lyrics_file)







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:Dict  ,     a	:int  ,     a	:Optional[int]		)	->				Union[str, Any]:
								__UpperCamelCase			:     Tuple         =      self.artists_decoder.get(a		)
								__UpperCamelCase			:     Dict         =      [self.genres_decoder.get(a		) for genre in genres_index]
								__UpperCamelCase			:     str         =      [self.lyrics_decoder.get(a		) for character in lyric_index]
								return artist, genres, lyrics








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				:    int=32		,						_lowerCamelCase				:    str=10		,						_lowerCamelCase				:    Dict=100		,						_lowerCamelCase				:    int=1_026		,						_lowerCamelCase				:    Union[str, Any]=True		,						_lowerCamelCase				:    str="data/tokenized_stories_train_wikitext103.jbl"		,						_lowerCamelCase				:    Any="igf_context_pairs.jbl"		,						)      ->					str:






							'''simple docstring'''





							set_seed(3)
							# generate train_data and objective_set
							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, 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?
							__UpperCamelCase			:     Dict         =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

							# load pretrained model
							__UpperCamelCase			:     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				:    List[Any]		,						_lowerCamelCase				:    Any=15		,						_lowerCamelCase				:    Union[str, Any]=128		,						_lowerCamelCase				:    Any=100		,						_lowerCamelCase				:    List[Any]="igf_model.pt"		,						)      ->					Any:






							'''simple docstring'''





							set_seed(42)

							# Load pre-trained model
							__UpperCamelCase			:     int         =      GPTaLMHeadModel.from_pretrained("gpt2")

							# Initialize secondary learner to use embedding weights of model
							__UpperCamelCase			:     Any         =      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				:    List[str]		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    int=32		,						_lowerCamelCase				:    Tuple=1_000		,						_lowerCamelCase				:    Dict=16		,						_lowerCamelCase				:    Union[str, Any]=1.0		,						_lowerCamelCase				:    Optional[Any]=recopy_gpta		,						_lowerCamelCase				:    List[Any]=None		,						_lowerCamelCase				:    Optional[Any]=10		,						_lowerCamelCase				:    Union[str, Any]="gpt2_finetuned.pt"		,						)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
							__UpperCamelCase			:     List[Any]         =      RandomSampler(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      DataLoader(_lowerCamelCase		,						sampler=_lowerCamelCase)

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(_lowerCamelCase)
														secondary_learner.eval()
							__UpperCamelCase			:     Union[str, Any]         =      []
							__UpperCamelCase			:     Any         =      0

							__UpperCamelCase			:     List[Any]         =      []
							__UpperCamelCase			:     Any         =      []

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

																					if secondary_learner is not None:
																												__UpperCamelCase			:     Optional[int]         =      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			:     List[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			:     Optional[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			:     str         =      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			:     List[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 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      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
							__UpperCamelCase			:     Any         =      joblib.load("data/IGF_values.jbl")

							# Train secondary learner
							__UpperCamelCase			:     Optional[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
							__UpperCamelCase			:     int         =      GPTaLMHeadModel.from_pretrained("gpt2")
							set_seed(42)

							# Generate train and test data to train and evaluate gpt2 model
							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      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()