


'''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      _UpperCamelCase   (		UpperCamelCase__=3_2			,   UpperCamelCase__=1_0			,   UpperCamelCase__=1_0_0			,   UpperCamelCase__=1_0_2_6			,   UpperCamelCase__=True			,   UpperCamelCase__="data/tokenized_stories_train_wikitext103.jbl"			,   UpperCamelCase__="igf_context_pairs.jbl"			,   ):
			set_seed(3       )
			# generate train_data and objective_set
			UpperCAmelCase__	:       Optional[int]   =  generate_datasets(
			    _UpperCamelCase			,   _UpperCamelCase			,   number=_UpperCamelCase			,   min_len=1_0_2_6			,   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__	:       List[Any]   =  torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""       )

			# load pretrained model
			UpperCAmelCase__	:       int   =  load_gpta("""gpt2"""       ).to(_UpperCamelCase       )
			print("""computing perplexity on objective set"""       )
			UpperCAmelCase__	:       List[Any]   =  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      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__=1_5			,   UpperCamelCase__=1_2_8			,   UpperCamelCase__=1_0_0			,   UpperCamelCase__="igf_model.pt"			,   ):
			set_seed(4_2       )

			# Load pre-trained model
			UpperCAmelCase__	:       Optional[int]   =  GPTaLMHeadModel.from_pretrained("""gpt2"""       )

			# Initialize secondary learner to use embedding weights of model
			UpperCAmelCase__	:       int   =  SecondaryLearner(_UpperCamelCase       )

			# Train secondary learner
			UpperCAmelCase__	:       Union[str, Any]   =  train_secondary_learner(
			    _UpperCamelCase			,   _UpperCamelCase			,   max_epochs=_UpperCamelCase			,   batch_size=_UpperCamelCase			,   eval_freq=1_0_0			,   igf_model_path=_UpperCamelCase			,   )

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner







def      _UpperCamelCase   (		UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__=3_2			,   UpperCamelCase__=1_0_0_0			,   UpperCamelCase__=1_6			,   UpperCamelCase__=1.0			,   UpperCamelCase__=recopy_gpta			,   UpperCamelCase__=None			,   UpperCamelCase__=1_0			,   UpperCamelCase__="gpt2_finetuned.pt"			,   ):
			UpperCAmelCase__	:       Union[str, Any]   =  torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""       )
			UpperCAmelCase__	:       Tuple   =  RandomSampler(_UpperCamelCase       )
			UpperCAmelCase__	:       Optional[int]   =  DataLoader(_UpperCamelCase			,   sampler=_UpperCamelCase       )

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

			model.train()
			if secondary_learner is not None:
						secondary_learner.to(_UpperCamelCase       )
						secondary_learner.eval()
			UpperCAmelCase__	:       Tuple   =  []
			UpperCAmelCase__	:       List[str]   =  0

			UpperCAmelCase__	:       Union[str, Any]   =  []
			UpperCAmelCase__	:       str   =  []

			# Compute the performance of the transformer model at the beginning
			UpperCAmelCase__	:       List[Any]   =  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__	:       Dict   =  random.randint(0			,   example.size(2       ) - context_len - 1       )
									UpperCAmelCase__	:       Dict   =  example[0, 0, start : start + context_len]
									lm_optimizer.zero_grad()
									UpperCAmelCase__	:       Optional[Any]   =  model(_UpperCamelCase			,   labels=_UpperCamelCase       )
									UpperCAmelCase__	:       Optional[int]   =  True

									if secondary_learner is not None:
												UpperCAmelCase__	:       int   =  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 == 1_0:
															UpperCAmelCase__	:       Any   =  -1
												if predicted_q < threshold:
															UpperCAmelCase__	:       Union[str, Any]   =  False

															  # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()       )       )
												UpperCAmelCase__	:       Optional[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()
												UpperCAmelCase__	:       Dict   =  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(_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 > 6_0:
												break
						if max_steps > 0 and global_step > 6_0:
									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      _UpperCamelCase   (		):
			UpperCAmelCase__	:       Union[str, Any]   =  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=3_2			,   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_0_0			,   type=_UpperCamelCase			,   help="""number of articles that are long enough to be used as our objective set"""			,   )
			parser.add_argument(
			    """--eval_freq"""			,   default=1_0_0			,   type=_UpperCamelCase			,   help="""secondary model evaluation is triggered at eval_freq"""       )

			parser.add_argument("""--max_steps"""			,   default=1_0_0_0			,   type=_UpperCamelCase			,   help="""To calculate training epochs"""       )

			parser.add_argument(
			    """--secondary_learner_batch_size"""			,   default=1_2_8			,   type=_UpperCamelCase			,   help="""batch size of training data for secondary learner"""			,   )

			parser.add_argument(
			    """--batch_size"""			,   default=1_6			,   type=_UpperCamelCase			,   help="""batch size of training data of language model(gpt2) """       )

			parser.add_argument(
			    """--eval_interval"""			,   default=1_0			,   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_0_0			,   type=_UpperCamelCase			,   help="""The number of examples split to be used as objective_set/test_data"""       )

			parser.add_argument(
			    """--min_len"""			,   default=1_0_2_6			,   type=_UpperCamelCase			,   help="""The minimum length of the article to be used as objective set"""       )

			parser.add_argument(
			    """--secondary_learner_max_epochs"""			,   default=1_5			,   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=3_2			,   max_steps=1_0			,   size_objective_set=1_0_0			,   min_len=1_0_2_6			,   trim=_UpperCamelCase			,   data_file="""data/tokenized_stories_train_wikitext103.jbl"""			,   igf_data_file="""igf_context_pairs.jbl"""			,   )

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

			# Train secondary learner
			UpperCAmelCase__	:       str   =  training_secondary_learner(
			    _UpperCamelCase			,   secondary_learner_max_epochs=1_5			,   secondary_learner_batch_size=1_2_8			,   eval_freq=1_0_0			,   igf_model_path="""igf_model.pt"""			,   )

			# load pretrained gpt2 model
			UpperCAmelCase__	:       str   =  GPTaLMHeadModel.from_pretrained("""gpt2"""       )
			set_seed(4_2       )

			# Generate train and test data to train and evaluate gpt2 model
			UpperCAmelCase__	:       Union[str, Any]   =  generate_datasets(
			    context_len=3_2			,   file="""data/tokenized_stories_train_wikitext103.jbl"""			,   number=1_0_0			,   min_len=1_0_2_6			,   trim=_UpperCamelCase       )

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   context_len=3_2			,   max_steps=1_0_0_0			,   batch_size=1_6			,   threshold=1.0			,   recopy_model=_UpperCamelCase			,   secondary_learner=_UpperCamelCase			,   eval_interval=1_0			,   finetuned_model_name="""gpt2_finetuned.pt"""			,   )


if __name__ == "__main__":
		main()

"""simple docstring"""




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  stooge(_UpperCamelCase			,					0			,					len(_UpperCamelCase    ) - 1    )
  return arr




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _lowercase			,					_lowercase:      Optional[Any]   			=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _lowercase:      Tuple   			=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )

    # Recursively sort last 2/3 elements
    stooge(_UpperCamelCase			,					i + t			,					(_UpperCamelCase)    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )


if __name__ == "__main__":
  A__ :       Dict				= input('Enter numbers separated by a comma:\n').strip()
  A__ :       Optional[Any]				= [int(item) for item in user_input.split(',')]
  print(stooge_sort(unsorted))
