





"""simple docstring"""




import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
		import bitsandbytes as bnb

from copy import deepcopy


a						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    torch.nn.Module    ,       _lowercase						:    BnbQuantizationConfig    ,       _lowercase						:    Union[str, os.PathLike] = None    ,       _lowercase						:    Optional[Dict[str, Union[int, str, torch.device]]] = None    ,       _lowercase						:    Optional[List[str]] = None    ,       _lowercase						:    Optional[Dict[Union[int, str], Union[int, str]]] = None    ,       _lowercase						:    Optional[Union[str, os.PathLike]] = None    ,       _lowercase						:    bool = False    ,       ) ->Union[str, Any]:






	'''simple docstring'''






	a      :		Tuple	=				bnb_quantization_config.load_in_abit
	a      :		int	=				bnb_quantization_config.load_in_abit

	if load_in_abit and not is_abit_bnb_available():
		raise ImportError(
		    "You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"
		    " make sure you have the latest version of `bitsandbytes` installed."					)
	if load_in_abit and not is_abit_bnb_available():
		raise ValueError(
		    "You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"
		    "make sure you have the latest version of `bitsandbytes` installed."					)

	a      :		Tuple	=				[]
	# custom device map
	if isinstance(_lowercase    ,       _lowercase					) and len(device_map.keys()					) > 1:
		a      :		List[str]	=				[key for key, value in device_map.items() if value in ["disk", "cpu"]]

	# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
	if bnb_quantization_config.skip_modules is None:
		a      :		List[str]	=				get_keys_to_not_convert(_lowercase					)

	# add cpu modules to skip modules only for 4-bit modules
	if load_in_abit:
		bnb_quantization_config.skip_modules.extend(_lowercase					)
	a      :		Optional[Any]	=				bnb_quantization_config.skip_modules

	# We add the modules we want to keep in full precision
	if bnb_quantization_config.keep_in_fpaa_modules is None:
		a      :		List[str]	=				[]
	a      :		List[Any]	=				bnb_quantization_config.keep_in_fpaa_modules
	modules_to_not_convert.extend(_lowercase					)

	# compatibility with peft
	a      :		List[str]	=				load_in_abit
	a      :		Optional[int]	=				load_in_abit

	a      :		Union[str, Any]	=				get_parameter_device(_lowercase					)
	if model_device.type != "meta":
		# quantization of an already loaded model
		logger.warning(
		    "It is not recommended to quantize a loaded model. "
		    "The model should be instantiated under the `init_empty_weights` context manager."					)
		a      :		Optional[int]	=				replace_with_bnb_layers(_lowercase    ,       _lowercase    ,       modules_to_not_convert=_lowercase					)
		# convert param to the right dtype
		a      :		Optional[int]	=				bnb_quantization_config.torch_dtype
		for name, param in model.state_dict().items():
			if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules					):
				param.to(torch.floataa					)
				if param.dtype != torch.floataa:
					a      :		Optional[int]	=				name.replace(".weight"    ,       ""					).replace(".bias"    ,       ""					)
					a      :		Any	=				getattr(_lowercase    ,       _lowercase    ,       _lowercase					)
					if param is not None:
						param.to(torch.floataa					)
			elif torch.is_floating_point(_lowercase					):
				param.to(_lowercase					)
		if model_device.type == "cuda":
			# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
			model.cuda(torch.cuda.current_device()					)
			torch.cuda.empty_cache()
		elif torch.cuda.is_available():
			model.to(torch.cuda.current_device()					)
		else:
			raise RuntimeError("No GPU found. A GPU is needed for quantization."					)
		logger.info(
		    F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
		    "We move the model to cuda."					)
		return model

	elif weights_location is None:
		raise RuntimeError(
		    F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """					)

	else:
		with init_empty_weights():
			a      :		int	=				replace_with_bnb_layers(
			    _lowercase    ,       _lowercase    ,       modules_to_not_convert=_lowercase					)

		a      :		Tuple	=				get_quantized_model_device_map(
		    _lowercase    ,       _lowercase    ,       _lowercase    ,       max_memory=_lowercase    ,       no_split_module_classes=_lowercase    ,       )
		if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
			a      :		List[str]	=				True

		a      :		Tuple	=				any(x in list(device_map.values()					) for x in ["cpu", "disk"]					)

		load_checkpoint_in_model(
		    _lowercase    ,       _lowercase    ,       _lowercase    ,       dtype=bnb_quantization_config.torch_dtype    ,       offload_folder=_lowercase    ,       offload_state_dict=_lowercase    ,       keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules    ,       offload_abit_bnb=load_in_abit and offload    ,       )
		return dispatch_model(_lowercase    ,       device_map=_lowercase    ,       offload_dir=_lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]=None    ,       _lowercase						:    Any=None    ,       _lowercase						:    Union[str, Any]=None					) ->List[Any]:






	'''simple docstring'''






	if device_map is None:
		if torch.cuda.is_available():
			a      :		Union[str, Any]	=				{"": torch.cuda.current_device()}
		else:
			raise RuntimeError("No GPU found. A GPU is needed for quantization."					)
		logger.info("The device_map was not initialized." "Setting device_map to `{'':torch.cuda.current_device()}`."					)

	if isinstance(_lowercase    ,       _lowercase					):
		if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
			raise ValueError(
			    "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
			    "'sequential'."					)

		a      :		List[str]	=				{}
		special_dtypes.update(
		    {
		        name: bnb_quantization_config.torch_dtype
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.skip_modules					)
		    }					)
		special_dtypes.update(
		    {
		        name: torch.floataa
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules					)
		    }					)

		a      :		Optional[Any]	=				{}
		a      :		Optional[int]	=				special_dtypes
		a      :		Union[str, Any]	=				no_split_module_classes
		a      :		List[str]	=				bnb_quantization_config.target_dtype

		# get max_memory for each device.
		if device_map != "sequential":
			a      :		Optional[int]	=				get_balanced_memory(
			    _lowercase    ,       low_zero=(device_map == "balanced_low_0")    ,       max_memory=_lowercase    ,       **_lowercase    ,       )

		a      :		Any	=				max_memory
		a      :		Dict	=				infer_auto_device_map(_lowercase    ,       **_lowercase					)

	if isinstance(_lowercase    ,       _lowercase					):
		# check if don't have any quantized module on the cpu
		a      :		List[str]	=				bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

		a      :		Optional[int]	=				{
		    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
		}
		for device in ["cpu", "disk"]:
			if device in device_map_without_some_modules.values():
				if bnb_quantization_config.load_in_abit:
					raise ValueError(
					    "\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        "					)
				else:
					logger.info(
					    "Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"					)
		del device_map_without_some_modules
	return device_map






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Any    ,       _lowercase						:    Tuple    ,       _lowercase						:    Tuple=None    ,       _lowercase						:    List[Any]=None					) ->int:






	'''simple docstring'''






	if modules_to_not_convert is None:
		a      :		Union[str, Any]	=				[]

	a,       a      :		Optional[Any]	=				_replace_with_bnb_layers(
	    _lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase					)
	if not has_been_replaced:
		logger.warning(
		    "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
		    " this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."
		    " Please double check your model architecture, or submit an issue on github if you think this is"
		    " a bug."					)
	return model






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict    ,       _lowercase						:    Dict    ,       _lowercase						:    List[Any]=None    ,       _lowercase						:    str=None    ,       ) ->Optional[int]:






	'''simple docstring'''






	a      :		Tuple	=				False
	for name, module in model.named_children():
		if current_key_name is None:
			a      :		Tuple	=				[]
		current_key_name.append(_lowercase					)
		if isinstance(_lowercase    ,       nn.Linear					) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			a      :		Union[str, Any]	=				".".join(_lowercase					)
			a      :		Optional[Any]	=				True
			for key in modules_to_not_convert:
				if (
				    (key in current_key_name_str) and (key + "." in current_key_name_str)
				) or key == current_key_name_str:
					a      :		str	=				False
					break
			if proceed:
				# Load bnb module with empty weight and replace ``nn.Linear` module
				if bnb_quantization_config.load_in_abit:
					a      :		Union[str, Any]	=				bnb.nn.LinearabitLt(
					    module.in_features    ,       module.out_features    ,       module.bias is not None    ,       has_fpaa_weights=_lowercase    ,       threshold=bnb_quantization_config.llm_inta_threshold    ,       )
				elif bnb_quantization_config.load_in_abit:
					a      :		Union[str, Any]	=				bnb.nn.Linearabit(
					    module.in_features    ,       module.out_features    ,       module.bias is not None    ,       bnb_quantization_config.bnb_abit_compute_dtype    ,       compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant    ,       quant_type=bnb_quantization_config.bnb_abit_quant_type    ,       )
				else:
					raise ValueError("load_in_8bit and load_in_4bit can't be both False"					)
				a      :		Union[str, Any]	=				module.weight.data
				if module.bias is not None:
					a      :		List[str]	=				module.bias.data
				bnb_module.requires_grad_(_lowercase					)
				setattr(_lowercase    ,       _lowercase    ,       _lowercase					)
				a      :		Optional[int]	=				True
		if len(list(module.children()					)					) > 0:
			a,       a      :		Union[str, Any]	=				_replace_with_bnb_layers(
			    _lowercase    ,       _lowercase    ,       _lowercase    ,       _lowercase					)
			a      :		str	=				has_been_replaced | _has_been_replaced
		# Remove the last key for recursion
		current_key_name.pop(-1					)
	return model, has_been_replaced






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->Union[str, Any]:






	'''simple docstring'''






	with init_empty_weights():
		a      :		Dict	=				deepcopy(_lowercase					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

	a      :		int	=				find_tied_parameters(_lowercase					)
	# For compatibility with Accelerate < 0.18
	if isinstance(_lowercase    ,       _lowercase					):
		a      :		Dict	=				sum(list(tied_params.values()					)    ,       []					) + list(tied_params.keys()					)
	else:
		a      :		Union[str, Any]	=				sum(_lowercase    ,       []					)
	a      :		Any	=				len(_lowercase					) > 0

	# Check if it is a base model
	a      :		int	=				False
	if hasattr(_lowercase    ,       "base_model_prefix"					):
		a      :		List[Any]	=				not hasattr(_lowercase    ,       model.base_model_prefix					)

	# Ignore this for base models (BertModel, GPT2Model, etc.)
	if (not has_tied_params) and is_base_model:
		return []

	# otherwise they have an attached head
	a      :		Optional[Any]	=				list(model.named_children()					)
	a      :		Dict	=				[list_modules[-1][0]]

	# add last module together with tied weights
	a      :		str	=				set(_lowercase					) - set(_lowercase					)
	a      :		Optional[Any]	=				list(set(_lowercase					)					) + list(_lowercase					)

	# remove ".weight" from the keys
	a      :		Optional[int]	=				[".weight", ".bias"]
	a      :		List[Any]	=				[]
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				a      :		Dict	=				name.replace(_lowercase    ,       ""					)
		filtered_module_names.append(_lowercase					)

	return filtered_module_names






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int					) ->Optional[int]:






	'''simple docstring'''






	for m in model.modules():
		if isinstance(_lowercase    ,       bnb.nn.Linearabit					):
			return True
	return False






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    nn.Module					) ->Union[str, Any]:






	'''simple docstring'''






	return next(parameter.parameters()					).device







def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]    ,       _lowercase						:    Any    ,       _lowercase						:    Optional[Any]    ,       _lowercase						:    str    ,       _lowercase						:    str    ,       _lowercase						:    Union[str, Any]    ,       _lowercase						:    List[str]					) ->Dict:






	'''simple docstring'''






	if fpaa_statistics is None:
		set_module_tensor_to_device(_lowercase    ,       _lowercase    ,       0    ,       dtype=_lowercase    ,       value=_lowercase					)
		a      :		str	=				param_name
		a      :		int	=				model
		if "." in tensor_name:
			a      :		str	=				tensor_name.split("."					)
			for split in splits[:-1]:
				a      :		Tuple	=				getattr(_lowercase    ,       _lowercase					)
				if new_module is None:
					raise ValueError(F"""{module} has no attribute {split}."""					)
				a      :		Dict	=				new_module
			a      :		Any	=				splits[-1]
		# offload weights
		a      :		Dict	=				False
		offload_weight(module._parameters[tensor_name]    ,       _lowercase    ,       _lowercase    ,       index=_lowercase					)
		if hasattr(module._parameters[tensor_name]    ,       "SCB"					):
			offload_weight(
			    module._parameters[tensor_name].SCB    ,       param_name.replace("weight"    ,       "SCB"					)    ,       _lowercase    ,       index=_lowercase    ,       )
	else:
		offload_weight(_lowercase    ,       _lowercase    ,       _lowercase    ,       index=_lowercase					)
		offload_weight(_lowercase    ,       param_name.replace("weight"    ,       "SCB"					)    ,       _lowercase    ,       index=_lowercase					)

	set_module_tensor_to_device(_lowercase    ,       _lowercase    ,       "meta"    ,       dtype=_lowercase    ,       value=torch.empty(*param.size()					)					)






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    snake_case_     (  snake_case=32			,							snake_case=10			,							snake_case=1_00			,							snake_case=10_26			,							snake_case=True			,							snake_case="data/tokenized_stories_train_wikitext103.jbl"			,							snake_case="igf_context_pairs.jbl"			,							)  ->				Union[str, Any]:
       set_seed(3     )
       # generate train_data and objective_set
       lowercase__			,						lowercase__:   List[str]  			=   generate_datasets(
           snake_case			,							snake_case			,							number=snake_case			,							min_len=10_26			,							trim=snake_case     )
       # 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__:   Optional[Any]  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )

       # load pretrained model
       lowercase__:   str  			=   load_gpta('gpt2'     ).to(snake_case     )
       print('computing perplexity on objective set'     )
       lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     ).item()
       print('perplexity on objective set:'			,							snake_case     )

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

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




def    snake_case_     (  snake_case			,							snake_case=15			,							snake_case=1_28			,							snake_case=1_00			,							snake_case="igf_model.pt"			,							)  ->				Optional[Any]:
       set_seed(42     )

       # Load pre-trained model
       lowercase__:   Any  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )

       # Initialize secondary learner to use embedding weights of model
       lowercase__:   Any  			=   SecondaryLearner(snake_case     )

       # Train secondary learner
       lowercase__:   Tuple  			=   train_secondary_learner(
           snake_case			,							snake_case			,							max_epochs=snake_case			,							batch_size=snake_case			,							eval_freq=1_00			,							igf_model_path=snake_case			,							)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=32			,							snake_case=10_00			,							snake_case=16			,							snake_case=1.0			,							snake_case=recopy_gpta			,							snake_case=None			,							snake_case=10			,							snake_case="gpt2_finetuned.pt"			,							)  ->				Tuple:
       lowercase__:   Dict  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Optional[int]  			=   RandomSampler(snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(snake_case			,							sampler=snake_case     )

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

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

       lowercase__:   Tuple  			=   []
       lowercase__:   Dict  			=   []

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

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

                            # 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__:   Optional[Any]  			=   -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__:   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()
                            lowercase__:   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__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     )
                                   test_perps.append(snake_case     )

                                   print('Test perplexity, step'			,							snake_case			,							':'			,							snake_case     )
            # 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()			,							snake_case     )
       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    snake_case_     (  )  ->				str:
       lowercase__:   Tuple  			=   argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'     )

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

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

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

       parser.add_argument(
           '--context_len'			,							default=32			,							type=snake_case			,							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_00			,							type=snake_case			,							help='number of articles that are long enough to be used as our objective set'			,							)
       parser.add_argument(
           '--eval_freq'			,							default=1_00			,							type=snake_case			,							help='secondary model evaluation is triggered at eval_freq'     )

       parser.add_argument('--max_steps'			,							default=10_00			,							type=snake_case			,							help='To calculate training epochs'     )

       parser.add_argument(
           '--secondary_learner_batch_size'			,							default=1_28			,							type=snake_case			,							help='batch size of training data for secondary learner'			,							)

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

       parser.add_argument(
           '--eval_interval'			,							default=10			,							type=snake_case			,							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_00			,							type=snake_case			,							help='The number of examples split to be used as objective_set/test_data'     )

       parser.add_argument(
           '--min_len'			,							default=10_26			,							type=snake_case			,							help='The minimum length of the article to be used as objective set'     )

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

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

       parser.add_argument(
           '--threshold'			,							default=1.0			,							type=snake_case			,							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=snake_case			,							help='finetuned_model_name'     )

       parser.add_argument(
           '--recopy_model'			,							default=snake_case			,							type=snake_case			,							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=1_00			,							min_len=10_26			,							trim=snake_case			,							data_file='data/tokenized_stories_train_wikitext103.jbl'			,							igf_data_file='igf_context_pairs.jbl'			,							)

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

       # Train secondary learner
       lowercase__:   List[str]  			=   training_secondary_learner(
           snake_case			,							secondary_learner_max_epochs=15			,							secondary_learner_batch_size=1_28			,							eval_freq=1_00			,							igf_model_path='igf_model.pt'			,							)

       # load pretrained gpt2 model
       lowercase__:   Dict  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

       # Generate train and test data to train and evaluate gpt2 model
       lowercase__			,						lowercase__:   Tuple  			=   generate_datasets(
           context_len=32			,							file='data/tokenized_stories_train_wikitext103.jbl'			,							number=1_00			,							min_len=10_26			,							trim=snake_case     )

       # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
       finetune(
           snake_case			,							snake_case			,							snake_case			,							context_len=32			,							max_steps=10_00			,							batch_size=16			,							threshold=1.0			,							recopy_model=snake_case			,							secondary_learner=snake_case			,							eval_interval=10			,							finetuned_model_name='gpt2_finetuned.pt'			,							)


if __name__ == "__main__":
 main()


