
"""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__			(							UpperCAmelCase_=32		,      UpperCAmelCase_=10		,      UpperCAmelCase_=1_00		,      UpperCAmelCase_=10_26		,      UpperCAmelCase_=True		,      UpperCAmelCase_="data/tokenized_stories_train_wikitext103.jbl"		,      UpperCAmelCase_="igf_context_pairs.jbl"		,      )->							List[str]:







       """simple docstring"""
       set_seed(3				)
       # generate train_data and objective_set
       UpperCamelCase ,       UpperCamelCase					 =		generate_datasets(
           UpperCAmelCase_		,      UpperCAmelCase_		,      number=UpperCAmelCase_		,      min_len=10_26		,      trim=UpperCAmelCase_				)
       # 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					 =		torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)

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

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

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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=15		,      UpperCAmelCase_=1_28		,      UpperCAmelCase_=1_00		,      UpperCAmelCase_="igf_model.pt"		,      )->							int:







       """simple docstring"""
       set_seed(42				)

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

       # Initialize secondary learner to use embedding weights of model
       UpperCamelCase					 =		SecondaryLearner(UpperCAmelCase_				)

       # Train secondary learner
       UpperCamelCase					 =		train_secondary_learner(
           UpperCAmelCase_		,      UpperCAmelCase_		,      max_epochs=UpperCAmelCase_		,      batch_size=UpperCAmelCase_		,      eval_freq=1_00		,      igf_model_path=UpperCAmelCase_		,      )

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=32		,      UpperCAmelCase_=10_00		,      UpperCAmelCase_=16		,      UpperCAmelCase_=1.0		,      UpperCAmelCase_=recopy_gpta		,      UpperCAmelCase_=None		,      UpperCAmelCase_=10		,      UpperCAmelCase_="gpt2_finetuned.pt"		,      )->							Optional[int]:







       """simple docstring"""
       UpperCamelCase					 =		torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)
       UpperCamelCase					 =		RandomSampler(UpperCAmelCase_				)
       UpperCamelCase					 =		DataLoader(UpperCAmelCase_		,      sampler=UpperCAmelCase_				)

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

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

       UpperCamelCase					 =		[]
       UpperCamelCase					 =		[]

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

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

                            # 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					 =		-1
                            if predicted_q < threshold:
                                   UpperCamelCase					 =		False

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

                                   print("Test perplexity, step"		,      UpperCAmelCase_		,      ":"		,      UpperCAmelCase_				)
            # 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()		,      UpperCAmelCase_				)
       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__			(							)->							List[str]:







       """simple docstring"""
       UpperCamelCase					 =		argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"				)

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

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

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

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

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

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

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

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

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

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

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

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

       parser.add_argument(
           "--recopy_model"		,      default=UpperCAmelCase_		,      type=UpperCAmelCase_		,      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=UpperCAmelCase_		,      data_file="data/tokenized_stories_train_wikitext103.jbl"		,      igf_data_file="igf_context_pairs.jbl"		,      )

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

       # Train secondary learner
       UpperCamelCase					 =		training_secondary_learner(
           UpperCAmelCase_		,      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
       UpperCamelCase					 =		GPTaLMHeadModel.from_pretrained("gpt2"				)
       set_seed(42				)

       # Generate train and test data to train and evaluate gpt2 model
       UpperCamelCase ,       UpperCamelCase					 =		generate_datasets(
           context_len=32		,      file="data/tokenized_stories_train_wikitext103.jbl"		,      number=1_00		,      min_len=10_26		,      trim=UpperCAmelCase_				)

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


if __name__ == "__main__":
      main()







"""simple docstring"""


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Any   =			(EulerDiscreteScheduler,)
       UpperCamelCase_		:					Dict   =			10






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      **UpperCAmelCase_ :							str			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		{
                  "num_train_timesteps": 1_100,
                  "beta_start": 0.0001,
                  "beta_end": 0.02,
                  "beta_schedule": "linear",
              }

              config.update(**UpperCAmelCase_			)
              return config






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Any:







              """simple docstring"""
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Any:







              """simple docstring"""
              for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,      [0.0002, 0.002, 0.02]			):
                     self.check_over_configs(beta_start=UpperCAmelCase_       ,      beta_end=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Dict:







              """simple docstring"""
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for i, t in enumerate(scheduler.timesteps			):
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 10.0807			) < 1e-2
              assert abs(result_mean.item() - 0.0131			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config(prediction_type="v_prediction"			)
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for i, t in enumerate(scheduler.timesteps			):
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 0.0002			) < 1e-2
              assert abs(result_mean.item() - 2.2_676e-06			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps       ,      device=UpperCAmelCase_			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for t in scheduler.timesteps:
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 10.0807			) < 1e-2
              assert abs(result_mean.item() - 0.0131			) < 1e-3






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.scheduler_classes[0]
              UpperCamelCase					 =		self.get_scheduler_config()
              UpperCamelCase					 =		scheduler_class(**UpperCAmelCase_       ,      use_karras_sigmas=UpperCAmelCase_			)

              scheduler.set_timesteps(self.num_inference_steps       ,      device=UpperCAmelCase_			)

              UpperCamelCase					 =		torch.manual_seed(0			)

              UpperCamelCase					 =		self.dummy_model()
              UpperCamelCase					 =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCamelCase					 =		sample.to(UpperCAmelCase_			)

              for t in scheduler.timesteps:
                     UpperCamelCase					 =		scheduler.scale_model_input(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		model(UpperCAmelCase_       ,      UpperCAmelCase_			)

                     UpperCamelCase					 =		scheduler.step(UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_       ,      generator=UpperCAmelCase_			)
                     UpperCamelCase					 =		output.prev_sample

              UpperCamelCase					 =		torch.sum(torch.abs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		torch.mean(torch.abs(UpperCAmelCase_			)			)

              assert abs(result_sum.item() - 124.52299499511719			) < 1e-2
              assert abs(result_mean.item() - 0.16213932633399963			) < 1e-3





