





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_ (							A_=32    ,A_=10    ,A_=1_00    ,A_=10_26    ,A_=True    ,A_="data/tokenized_stories_train_wikitext103.jbl"    ,A_="igf_context_pairs.jbl"    ,):
    set_seed(3)
    # generate train_data and objective_set
    UpperCamelCase__				, UpperCamelCase__:	Any					=     generate_datasets(
        A_    ,A_    ,number=A_    ,min_len=10_26    ,trim=A_)
    # 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__:	Optional[Any]					=     torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    # load pretrained model
    UpperCamelCase__:	List[Any]					=     load_gpta("gpt2").to(A_)
    print("computing perplexity on objective set")
    UpperCamelCase__:	List[str]					=     compute_perplexity(A_    ,A_    ,A_).item()
    print("perplexity on objective set:"    ,A_)

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

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



def 				lowerCAmelCase_ (							A_    ,A_=15    ,A_=1_28    ,A_=1_00    ,A_="igf_model.pt"    ,):
    set_seed(42)

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

    # Initialize secondary learner to use embedding weights of model
    UpperCamelCase__:	List[Any]					=     SecondaryLearner(A_)

    # Train secondary learner
    UpperCamelCase__:	str					=     train_secondary_learner(
        A_    ,A_    ,max_epochs=A_    ,batch_size=A_    ,eval_freq=1_00    ,igf_model_path=A_    ,)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_=32    ,A_=10_00    ,A_=16    ,A_=1.0    ,A_=recopy_gpta    ,A_=None    ,A_=10    ,A_="gpt2_finetuned.pt"    ,):
    UpperCamelCase__:	int					=     torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    UpperCamelCase__:	List[str]					=     RandomSampler(A_)
    UpperCamelCase__:	Tuple					=     DataLoader(A_    ,sampler=A_)

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

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

    UpperCamelCase__:	Union[str, Any]					=     []
    UpperCamelCase__:	List[str]					=     []

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

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

                # 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__:	str					=     -1
                if predicted_q < threshold:
                    UpperCamelCase__:	Dict					=     False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()))
                UpperCamelCase__:	Dict					=     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__:	List[str]					=     0
                # Do LM backprop
                torch.nn.utils.clip_grad_norm_(model.parameters()    ,3.0)
                lm_optimizer.step()
                lm_scheduler.step()  # Update learning rate schedule
                global_step += 1
                # Compute the performance of the transformer model at this batch
                if global_step % eval_interval == 0:
                    UpperCamelCase__:	Tuple					=     compute_perplexity(A_    ,A_    ,A_)
                    test_perps.append(A_)

                    print("Test perplexity, step"    ,A_    ,":"    ,A_)
            # 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()    ,A_)
    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_ (							):
    UpperCamelCase__:	Optional[Any]					=     argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task")

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

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

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

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

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

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

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

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

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

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

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

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

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

    # Load train data for secondary learner
    UpperCamelCase__:	Optional[Any]					=     joblib.load("data/IGF_values.jbl")

    # Train secondary learner
    UpperCamelCase__:	Union[str, Any]					=     training_secondary_learner(
        A_    ,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__:	Any					=     GPTaLMHeadModel.from_pretrained("gpt2")
    set_seed(42)

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

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


if __name__ == "__main__":
   main()








from __future__ import annotations

from typing import Any




class        _a    :

      """simple docstring"""


      def __init__(		self:			Optional[int]	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     num_of_nodes
          UpperCamelCase__:	list[list[int]]					=     []
          UpperCamelCase__:	dict[int, int]					=     {}






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          self.m_edges.append([u_node, v_node, weight]					)






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          if self.m_component[u_node] == u_node:
              return u_node
          return self.find_component(self.m_component[u_node]					)






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          if self.m_component[u_node] != u_node:
              for k in self.m_component:
                  UpperCamelCase__:	Dict					=     self.find_component(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			list[int]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          if component_size[u_node] <= component_size[v_node]:
              UpperCamelCase__:	List[str]					=     v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(__lowerCamelCase					)

          elif component_size[u_node] >= component_size[v_node]:
              UpperCamelCase__:	Any					=     self.find_component(__lowerCamelCase					)
              component_size[u_node] += component_size[v_node]
              self.set_component(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     []
          UpperCamelCase__:	Optional[int]					=     0

          UpperCamelCase__:	list[Any]					=     [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes					):
              self.m_component.update({node: node}					)
              component_size.append(1					)

          UpperCamelCase__:	Optional[int]					=     self.m_num_of_nodes

          while num_of_components > 1:
              for edge in self.m_edges:
                  UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	List[str]					=     edge

                  UpperCamelCase__:	List[Any]					=     self.m_component[u]
                  UpperCamelCase__:	Tuple					=     self.m_component[v]

                  if u_component != v_component:

                      for component in (u_component, v_component):
                          if (
                              minimum_weight_edge[component] == -1
                              or minimum_weight_edge[component][2] > w
                          ):
                              UpperCamelCase__:	Any					=     [u, v, w]

              for edge in minimum_weight_edge:
                  if isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                      UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	List[str]					=     edge

                      UpperCamelCase__:	Tuple					=     self.m_component[u]
                      UpperCamelCase__:	Optional[Any]					=     self.m_component[v]

                      if u_component != v_component:
                          mst_weight += w
                          self.union(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)
                          print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"					)
                          num_of_components -= 1

              UpperCamelCase__:	List[Any]					=     [-1] * self.m_num_of_nodes
          print(F"The total weight of the minimal spanning tree is: {mst_weight}"					)



def 				lowerCAmelCase_ (							):
    pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()

