"""simple docstring"""





import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
			import onnxruntime as ort




class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"





				def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							Dict=0):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    floats_tensor((1, 3, 1_2_8, 1_2_8)      ,    rng=random.Random(_A))
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.random.RandomState(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    {
											    """prompt""": """A painting of a squirrel eating a burger""",
											    """image""": image,
											    """generator""": generator,
											    """num_inference_steps""": 3,
											    """strength""": 0.75,
											    """guidance_scale""": 7.5,
											    """output_type""": """numpy""",
											}
											return inputs





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,    provider="""CPUExecutionProvider""")
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    pipe(**_A).images
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.array([0.69_643, 0.58_484, 0.50_314, 0.58_760, 0.55_368, 0.59_643, 0.51_529, 0.41_217, 0.49_087])
											assert np.abs(image_slice - expected_slice).max() < 1e-1





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,    provider="""CPUExecutionProvider""")
											_SCREAMING_SNAKE_CASE		:						str			  =    PNDMScheduler.from_config(pipe.scheduler.config      ,    skip_prk_steps=_A)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    pipe(**_A).images
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.array([0.61_737, 0.54_642, 0.53_183, 0.54_465, 0.52_742, 0.60_525, 0.49_969, 0.40_655, 0.48_154])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,    provider="""CPUExecutionProvider""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    LMSDiscreteScheduler.from_config(pipe.scheduler.config)
											pipe.set_progress_bar_config(disable=_A)

											# warmup pass to apply optimizations
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    pipe(**self.get_dummy_inputs())

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pipe(**_A).images
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.array([0.52_761, 0.59_977, 0.49_033, 0.49_619, 0.54_282, 0.50_311, 0.47_600, 0.40_918, 0.45_203])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,    provider="""CPUExecutionProvider""")
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    EulerDiscreteScheduler.from_config(pipe.scheduler.config)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    pipe(**_A).images
											_SCREAMING_SNAKE_CASE		:						str			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,    provider="""CPUExecutionProvider""")
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE		:						Any			  =    pipe(**_A).images
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_SCREAMING_SNAKE_CASE		:						int			  =    np.array([0.52_911, 0.60_004, 0.49_229, 0.49_805, 0.54_502, 0.50_680, 0.47_777, 0.41_028, 0.45_304])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,    provider="""CPUExecutionProvider""")
											_SCREAMING_SNAKE_CASE		:						int			  =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    self.get_dummy_inputs()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pipe(**_A).images
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    image[0, -3:, -3:, -1]

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.array([0.65_331, 0.58_277, 0.48_204, 0.56_059, 0.53_665, 0.56_235, 0.50_969, 0.40_009, 0.46_552])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1




@nightly
@require_onnxruntime
@require_torch_gpu
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@property
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)





				@property
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    ort.SessionOptions()
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
											return options





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/img2img/sketch-mountains-input.jpg""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    init_image.resize((7_6_8, 5_1_2))
											# using the PNDM scheduler by default
											_SCREAMING_SNAKE_CASE		:						Any			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    """CompVis/stable-diffusion-v1-4"""      ,    revision="""onnx"""      ,    safety_checker=_A      ,    feature_extractor=_A      ,    provider=self.gpu_provider      ,    sess_options=self.gpu_options      ,    )
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    """A fantasy landscape, trending on artstation"""

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.random.RandomState(0)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pipe(
											    prompt=_A      ,    image=_A      ,    strength=0.75      ,    guidance_scale=7.5      ,    num_inference_steps=1_0      ,    generator=_A      ,    output_type="""np"""      ,    )
											_SCREAMING_SNAKE_CASE		:						str			  =    output.images
											_SCREAMING_SNAKE_CASE		:						Dict			  =    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

											assert images.shape == (1, 5_1_2, 7_6_8, 3)
											_SCREAMING_SNAKE_CASE		:						Any			  =    np.array([0.4_909, 0.5_059, 0.5_372, 0.4_623, 0.4_876, 0.5_049, 0.4_820, 0.4_956, 0.5_019])
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

											assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/img2img/sketch-mountains-input.jpg""")
											_SCREAMING_SNAKE_CASE		:						Dict			  =    init_image.resize((7_6_8, 5_1_2))
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    LMSDiscreteScheduler.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""      ,    subfolder="""scheduler"""      ,    revision="""onnx""")
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    OnnxStableDiffusionImgaImgPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""      ,    revision="""onnx"""      ,    scheduler=_A      ,    safety_checker=_A      ,    feature_extractor=_A      ,    provider=self.gpu_provider      ,    sess_options=self.gpu_options      ,    )
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						Any			  =    """A fantasy landscape, trending on artstation"""

											_SCREAMING_SNAKE_CASE		:						int			  =    np.random.RandomState(0)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    pipe(
											    prompt=_A      ,    image=_A      ,    strength=0.75      ,    guidance_scale=7.5      ,    num_inference_steps=2_0      ,    generator=_A      ,    output_type="""np"""      ,    )
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    output.images
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

											assert images.shape == (1, 5_1_2, 7_6_8, 3)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    np.array([0.8_043, 0.926, 0.9_581, 0.8_119, 0.8_954, 0.913, 0.7_209, 0.7_463, 0.7_431])
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

											assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2



"""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_(__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE=1_026     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"     ,	__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"     ,	)->    Union[str, Any]:
							set_seed(3  )
							# generate train_data and objective_set
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    generate_datasets(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	number=__SCREAMING_SNAKE_CASE     ,	min_len=1_026     ,	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		:						Dict			  =    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		:						Union[str, 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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=15     ,	__SCREAMING_SNAKE_CASE=128     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE="igf_model.pt"     ,	)->    Optional[int]:
							set_seed(42  )

							# Load pre-trained model
							_SCREAMING_SNAKE_CASE		:						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=100     ,	igf_model_path=__SCREAMING_SNAKE_CASE     ,	)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=1_000     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=1.0     ,	__SCREAMING_SNAKE_CASE=recopy_gpta     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"     ,	)->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RandomSampler(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataLoader(__SCREAMING_SNAKE_CASE     ,	sampler=__SCREAMING_SNAKE_CASE  )

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

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						int			  =    []

							# Compute the performance of the transformer model at the beginning
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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		:						Union[str, Any]			  =    model(__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    True

																					if secondary_learner is not None:
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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		:						List[str]			  =    False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()  )  )
																												_SCREAMING_SNAKE_CASE		:						Union[str, 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()
																												_SCREAMING_SNAKE_CASE		:						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:
																																			_SCREAMING_SNAKE_CASE		:						Tuple			  =    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      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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=100     ,	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=100     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""secondary model evaluation is triggered at eval_freq"""  )

							parser.add_argument("""--max_steps"""     ,	default=1_000     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""To calculate training epochs"""  )

							parser.add_argument(
							    """--secondary_learner_batch_size"""     ,	default=128     ,	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=100     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""The number of examples split to be used as objective_set/test_data"""  )

							parser.add_argument(
							    """--min_len"""     ,	default=1_026     ,	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=100     ,	min_len=1_026     ,	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		:						Optional[int]			  =    joblib.load("""data/IGF_values.jbl"""  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						int			  =    training_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	secondary_learner_max_epochs=15     ,	secondary_learner_batch_size=128     ,	eval_freq=100     ,	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		:						Optional[Any]			  =    generate_datasets(
							    context_len=32     ,	file="""data/tokenized_stories_train_wikitext103.jbl"""     ,	number=100     ,	min_len=1_026     ,	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=1_000     ,	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()


