



'''simple docstring'''

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


snake_case_			=     logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case_			=     """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Dict=8       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				SCREAMING_SNAKE_CASE  :						int      					= width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




class 	a__     (				_lowercase	):


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDConditionModel,					__UpperCAmelCase : DDPMScheduler,					__UpperCAmelCase : VQModel,					)     ->			int:

					"""simple docstring"""

					super().__init__()

					self.register_modules(
					    unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase,					movq=__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						Any      					= 2 ** (len(self.movq.config.block_out_channels      ) - 1)


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : List[str],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict      )     ->			int:

					"""simple docstring"""

					if latents is None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase,					device=__UpperCAmelCase,					dtype=__UpperCAmelCase      )
					else:
									if latents.shape != shape:
													raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''      )
									SCREAMING_SNAKE_CASE  :						List[Any]      					= latents.to(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= latents * scheduler.init_noise_sigma
					return latents


	def 				lowercase__  (self : str,					__UpperCAmelCase : Dict=0      )     ->			Dict:

					"""simple docstring"""

					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= torch.device(F'''cuda:{gpu_id}'''      )

					SCREAMING_SNAKE_CASE  :						Any      					= [
					    self.unet,
					    self.movq,
					]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(__UpperCAmelCase,					__UpperCAmelCase      )


	def 				lowercase__  (self : int,					__UpperCAmelCase : str=0      )     ->			str:

					"""simple docstring"""

					if is_accelerate_available() and is_accelerate_version('''>=''',					'''0.17.0.dev0'''      ):
									from accelerate import cpu_offload_with_hook
					else:
									raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''      )

					SCREAMING_SNAKE_CASE  :						str      					= torch.device(F'''cuda:{gpu_id}'''      )

					if self.device.type != "cpu":
									self.to('''cpu''',					silence_dtype_warnings=__UpperCAmelCase      )
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= None
					for cpu_offloaded_model in [self.unet, self.movq]:
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= cpu_offload_with_hook(__UpperCAmelCase,					__UpperCAmelCase,					prev_module_hook=__UpperCAmelCase      )

					# We'll offload the last model manually.
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= hook


	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					if not hasattr(self.unet,					'''_hf_hook'''      ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(__UpperCAmelCase,					'''_hf_hook'''      )
									    and hasattr(module._hf_hook,					'''execution_device'''      )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device      )
					return self.device






	@torch.no_grad()
	@replace_example_docstring(__UpperCAmelCase      )
	def __call__(self : Any,					__UpperCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]],					__UpperCAmelCase : Union[torch.FloatTensor, List[torch.FloatTensor]],					__UpperCAmelCase : int = 512,					__UpperCAmelCase : int = 512,					__UpperCAmelCase : int = 100,					__UpperCAmelCase : float = 4.0,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[torch.FloatTensor] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					)     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self._execution_device

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= guidance_scale > 1.0

					if isinstance(__UpperCAmelCase,					__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						List[str]      					= torch.cat(__UpperCAmelCase,					dim=0      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= image_embeds.shape[0] * num_images_per_prompt
					if isinstance(__UpperCAmelCase,					__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.cat(__UpperCAmelCase,					dim=0      )

					if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_embeds.repeat_interleave(__UpperCAmelCase,					dim=0      )
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= negative_image_embeds.repeat_interleave(__UpperCAmelCase,					dim=0      )

					SCREAMING_SNAKE_CASE  :						Dict      					= torch.cat([negative_image_embeds, image_embeds],					dim=0      ).to(dtype=self.unet.dtype,					device=__UpperCAmelCase      )

					self.scheduler.set_timesteps(__UpperCAmelCase,					device=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler.timesteps

					SCREAMING_SNAKE_CASE  :						Any      					= self.unet.config.in_channels

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= downscale_height_and_width(__UpperCAmelCase,					__UpperCAmelCase,					self.movq_scale_factor      )

					# create initial latent
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.prepare_latents(
					    (batch_size, num_channels_latents, height, width),					image_embeds.dtype,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					self.scheduler,					)

					for i, t in enumerate(self.progress_bar(__UpperCAmelCase      )      ):
									# expand the latents if we are doing classifier free guidance
									SCREAMING_SNAKE_CASE  :						List[str]      					= torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

									SCREAMING_SNAKE_CASE  :						List[str]      					= {'''image_embeds''': image_embeds}
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet(
									    sample=__UpperCAmelCase,					timestep=__UpperCAmelCase,					encoder_hidden_states=__UpperCAmelCase,					added_cond_kwargs=__UpperCAmelCase,					return_dict=__UpperCAmelCase,					)[0]

									if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= noise_pred.split(latents.shape[1],					dim=1      )
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= noise_pred.chunk(2      )
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= variance_pred.chunk(2      )
													SCREAMING_SNAKE_CASE  :						Any      					= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													SCREAMING_SNAKE_CASE  :						Any      					= torch.cat([noise_pred, variance_pred_text],					dim=1      )

									if not (
									    hasattr(self.scheduler.config,					'''variance_type'''      )
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= noise_pred.split(latents.shape[1],					dim=1      )

									# compute the previous noisy sample x_t -> x_t-1
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step(
									    __UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase,					)[0]
					# post-processing
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.movq.decode(__UpperCAmelCase,					force_not_quantize=__UpperCAmelCase      )['''sample''']

					if output_type not in ["pt", "np", "pil"]:
									raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''      )

					if output_type in ["np", "pil"]:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image * 0.5 + 0.5
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= image.clamp(0,					1      )
									SCREAMING_SNAKE_CASE  :						str      					= image.cpu().permute(0,					2,					3,					1      ).float().numpy()

					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						str      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )









'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]=32    ,	_SCREAMING_SNAKE_CASE      :List[Any]=10    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=1_00    ,	_SCREAMING_SNAKE_CASE      :int=10_26    ,	_SCREAMING_SNAKE_CASE      :List[Any]=True    ,	_SCREAMING_SNAKE_CASE      :str="data/tokenized_stories_train_wikitext103.jbl"    ,	_SCREAMING_SNAKE_CASE      :List[Any]="igf_context_pairs.jbl"    ,	):
				set_seed(3       )
				# generate train_data and objective_set
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= generate_datasets(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	number=_SCREAMING_SNAKE_CASE    ,	min_len=10_26    ,	trim=_SCREAMING_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?
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )

				# load pretrained model
				SCREAMING_SNAKE_CASE  :						Any      					= load_gpta('''gpt2'''       ).to(_SCREAMING_SNAKE_CASE       )
				print('''computing perplexity on objective set'''       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= compute_perplexity(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ).item()
				print('''perplexity on objective set:'''    ,	_SCREAMING_SNAKE_CASE       )

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

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=15    ,	_SCREAMING_SNAKE_CASE      :Any=1_28    ,	_SCREAMING_SNAKE_CASE      :Any=1_00    ,	_SCREAMING_SNAKE_CASE      :List[str]="igf_model.pt"    ,	):
				set_seed(42       )

				# Load pre-trained model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )

				# Initialize secondary learner to use embedding weights of model
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= SecondaryLearner(_SCREAMING_SNAKE_CASE       )

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						Any      					= train_secondary_learner(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	max_epochs=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	eval_freq=1_00    ,	igf_model_path=_SCREAMING_SNAKE_CASE    ,	)

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=32    ,	_SCREAMING_SNAKE_CASE      :Tuple=10_00    ,	_SCREAMING_SNAKE_CASE      :int=16    ,	_SCREAMING_SNAKE_CASE      :List[str]=1.0    ,	_SCREAMING_SNAKE_CASE      :Any=recopy_gpta    ,	_SCREAMING_SNAKE_CASE      :Tuple=None    ,	_SCREAMING_SNAKE_CASE      :int=10    ,	_SCREAMING_SNAKE_CASE      :Optional[int]="gpt2_finetuned.pt"    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )
				SCREAMING_SNAKE_CASE  :						Any      					= RandomSampler(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= DataLoader(_SCREAMING_SNAKE_CASE    ,	sampler=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Dict      					= max_steps // (len(_SCREAMING_SNAKE_CASE       )) + 1
				SCREAMING_SNAKE_CASE  :						str      					= 0
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.zeros((1, context_len)    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= recopy_model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				model.train()
				if secondary_learner is not None:
								secondary_learner.to(_SCREAMING_SNAKE_CASE       )
								secondary_learner.eval()
				SCREAMING_SNAKE_CASE  :						str      					= []
				SCREAMING_SNAKE_CASE  :						List[str]      					= 0

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []

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

												if secondary_learner is not None:
																SCREAMING_SNAKE_CASE  :						str      					= secondary_learner.forward(
																    torch.tensor(_SCREAMING_SNAKE_CASE    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )       )[0].item()
																observed_qs.append(float(_SCREAMING_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:
																				SCREAMING_SNAKE_CASE  :						Dict      					= -1
																if predicted_q < threshold:
																				SCREAMING_SNAKE_CASE  :						Dict      					= False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()       )       )
																SCREAMING_SNAKE_CASE  :						Tuple      					= 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()
																SCREAMING_SNAKE_CASE  :						int      					= 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:
																				SCREAMING_SNAKE_CASE  :						Any      					= compute_perplexity(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																				test_perps.append(_SCREAMING_SNAKE_CASE       )

																				print('''Test perplexity, step'''    ,	_SCREAMING_SNAKE_CASE    ,	''':'''    ,	_SCREAMING_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()    ,	_SCREAMING_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       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Dict      					= argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''       )

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

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

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

				parser.add_argument(
				    '''--context_len'''    ,	default=32    ,	type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	help='''secondary model evaluation is triggered at eval_freq'''       )

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

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

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

				parser.add_argument(
				    '''--eval_interval'''    ,	default=10    ,	type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	help='''number of epochs to train secondary learner'''       )

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

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

				parser.add_argument(
				    '''--recopy_model'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	data_file='''data/tokenized_stories_train_wikitext103.jbl'''    ,	igf_data_file='''igf_context_pairs.jbl'''    ,	)

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

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						List[Any]      					= training_secondary_learner(
				    _SCREAMING_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
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )
				set_seed(42       )

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

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


if __name__ == "__main__":
	main()




