


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        UpperCamelCase_(				_A   :int=32				,     _A   :Dict=10				,     _A   :Optional[int]=1_00				,     _A   :Optional[Any]=10_26				,     _A   :Any=True				,     _A   :Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"				,     _A   :Tuple="igf_context_pairs.jbl"				,     )->		Union[str, Any]:
			set_seed(3  )
			# generate train_data and objective_set
			UpperCamelCase__,      UpperCamelCase__					=       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__					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"  )

			# load pretrained model
			UpperCamelCase__					=       load_gpta("gpt2"  ).to(_A  )
			print("computing perplexity on objective set"  )
			UpperCamelCase__					=       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        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :List[Any]=15				,     _A   :Union[str, Any]=1_28				,     _A   :int=1_00				,     _A   :Union[str, Any]="igf_model.pt"				,     )->		Optional[Any]:
			set_seed(42  )

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

			# Initialize secondary learner to use embedding weights of model
			UpperCamelCase__					=       SecondaryLearner(_A  )

			# Train secondary learner
			UpperCamelCase__					=       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        UpperCamelCase_(				_A   :Any				,     _A   :List[Any]				,     _A   :Dict				,     _A   :List[str]=32				,     _A   :int=10_00				,     _A   :Optional[int]=16				,     _A   :Dict=1.0				,     _A   :List[Any]=recopy_gpta				,     _A   :List[str]=None				,     _A   :Any=10				,     _A   :Optional[int]="gpt2_finetuned.pt"				,     )->		Dict:
			UpperCamelCase__					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"  )
			UpperCamelCase__					=       RandomSampler(_A  )
			UpperCamelCase__					=       DataLoader(_A				,     sampler=_A  )

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

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

			UpperCamelCase__					=       []
			UpperCamelCase__					=       []

			# Compute the performance of the transformer model at the beginning
			UpperCamelCase__					=       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__					=       random.randint(0				,     example.size(2  ) - context_len - 1  )
									UpperCamelCase__					=       example[0, 0, start : start + context_len]
									lm_optimizer.zero_grad()
									UpperCamelCase__					=       model(_A				,     labels=_A  )
									UpperCamelCase__					=       True

									if secondary_learner is not None:
												UpperCamelCase__					=       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__					=       -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(_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        UpperCamelCase_(				)->		Optional[Any]:
			UpperCamelCase__					=       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__					=       joblib.load("data/IGF_values.jbl"  )

			# Train secondary learner
			UpperCamelCase__					=       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__					=       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=_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 typing import Any







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       data
						UpperCamelCase__					=       None







class        lowerCamelCase__	:






			"""simple docstring"""






			def __init__(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.head
						while temp is not None:
									print(temp.data      ,						end=" "     )
									UpperCamelCase__					=       temp.next
						print()




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       Node(snake_case     )
						UpperCamelCase__					=       self.head
						UpperCamelCase__					=       new_node







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						if node_data_a == node_data_a:
									return
						else:
									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									UpperCamelCase__					=       self.head
									while node_a is not None and node_a.data != node_data_a:
												UpperCamelCase__					=       node_a.next

									if node_a is None or node_a is None:
												return

									UpperCamelCase__,      UpperCamelCase__					=       node_a.data, node_a.data


if __name__ == "__main__":
						__UpperCamelCase    		=       LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print('After swapping')
						ll.print_list()


