


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__:						Any=3_2     ,    a__:						Tuple=1_0     ,    a__:						List[Any]=1_0_0     ,    a__:						List[str]=1_0_2_6     ,    a__:						int=True     ,    a__:						int="data/tokenized_stories_train_wikitext103.jbl"     ,    a__:						Any="igf_context_pairs.jbl"     ,    )    ->						Dict:


							'''simple docstring'''





							set_seed(3			)
							# generate train_data and objective_set
							_UpperCAmelCase						,			_UpperCAmelCase											=     generate_datasets(
							    lowerCAmelCase__     ,    lowerCAmelCase__     ,    number=lowerCAmelCase__     ,    min_len=1_0_2_6     ,    trim=lowerCAmelCase__			)
							# 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(lowerCAmelCase__			)
							print('computing perplexity on objective set'			)
							_UpperCAmelCase											=     compute_perplexity(lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__			).item()
							print('perplexity on objective set:'     ,    lowerCAmelCase__			)

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

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

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]=1_5     ,    a__:						Optional[Any]=1_2_8     ,    a__:						Optional[int]=1_0_0     ,    a__:						Dict="igf_model.pt"     ,    )    ->						Optional[Any]:


							'''simple docstring'''





							set_seed(4_2			)

							# Load pre-trained model
							_UpperCAmelCase											=     GPTaLMHeadModel.from_pretrained('gpt2'			)

							# Initialize secondary learner to use embedding weights of model
							_UpperCAmelCase											=     SecondaryLearner(lowerCAmelCase__			)

							# Train secondary learner
							_UpperCAmelCase											=     train_secondary_learner(
							    lowerCAmelCase__     ,    lowerCAmelCase__     ,    max_epochs=lowerCAmelCase__     ,    batch_size=lowerCAmelCase__     ,    eval_freq=1_0_0     ,    igf_model_path=lowerCAmelCase__     ,    )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						List[str]     ,    a__:						Tuple     ,    a__:						Any=3_2     ,    a__:						int=1_0_0_0     ,    a__:						Any=1_6     ,    a__:						Tuple=1.0     ,    a__:						List[Any]=recopy_gpta     ,    a__:						Tuple=None     ,    a__:						Tuple=1_0     ,    a__:						List[Any]="gpt2_finetuned.pt"     ,    )    ->						int:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
							_UpperCAmelCase											=     RandomSampler(lowerCAmelCase__			)
							_UpperCAmelCase											=     DataLoader(lowerCAmelCase__     ,    sampler=lowerCAmelCase__			)

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

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

							_UpperCAmelCase											=     []
							_UpperCAmelCase											=     []

							# Compute the performance of the transformer model at the beginning
							_UpperCAmelCase											=     compute_perplexity(lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__			)
							test_perps.append(lowerCAmelCase__			)
							print('Test perplexity, step'     ,    lowerCAmelCase__     ,    ':'     ,    lowerCAmelCase__			)
							for epoch in range(int(lowerCAmelCase__			)			):
														for step, example in enumerate(lowerCAmelCase__			):
																					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(lowerCAmelCase__     ,    labels=lowerCAmelCase__			)
																					_UpperCAmelCase											=     True

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

																												# 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											=     -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(lowerCAmelCase__     ,    lowerCAmelCase__     ,    lowerCAmelCase__			)
																																			test_perps.append(lowerCAmelCase__			)

																																			print('Test perplexity, step'     ,    lowerCAmelCase__     ,    ':'     ,    lowerCAmelCase__			)
																												# 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()     ,    lowerCAmelCase__			)
							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:


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

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

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

							parser.add_argument(
							    '--context_len'     ,    default=3_2     ,    type=lowerCAmelCase__     ,    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=lowerCAmelCase__     ,    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=lowerCAmelCase__     ,    help='secondary model evaluation is triggered at eval_freq'			)

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

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

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

							parser.add_argument(
							    '--eval_interval'     ,    default=1_0     ,    type=lowerCAmelCase__     ,    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=lowerCAmelCase__     ,    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=lowerCAmelCase__     ,    help='The minimum length of the article to be used as objective set'			)

							parser.add_argument(
							    '--secondary_learner_max_epochs'     ,    default=1_5     ,    type=lowerCAmelCase__     ,    help='number of epochs to train secondary learner'			)

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

							parser.add_argument(
							    '--threshold'     ,    default=1.0     ,    type=lowerCAmelCase__     ,    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=lowerCAmelCase__     ,    help='finetuned_model_name'			)

							parser.add_argument(
							    '--recopy_model'     ,    default=lowerCAmelCase__     ,    type=lowerCAmelCase__     ,    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=lowerCAmelCase__     ,    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(
							    lowerCAmelCase__     ,    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											=     GPTaLMHeadModel.from_pretrained('gpt2'			)
							set_seed(4_2			)

							# Generate train and test data to train and evaluate gpt2 model
							_UpperCAmelCase						,			_UpperCAmelCase											=     generate_datasets(
							    context_len=3_2     ,    file='data/tokenized_stories_train_wikitext103.jbl'     ,    number=1_0_0     ,    min_len=1_0_2_6     ,    trim=lowerCAmelCase__			)

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


if __name__ == "__main__":
		main()




def  a   (lowerCAmelCase__   ):
   __a					=  False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __a					=  True
      for i in range(0		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False

      for i in range(1		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False
   return input_list


if __name__ == "__main__":
  print('Enter list to be sorted')
  SCREAMING_SNAKE_CASE							    =							[int(x) for x in input().split()]
  # inputing elements of the list in one line
  SCREAMING_SNAKE_CASE							    =							odd_even_sort(input_list)
  print('The sorted list is')
  print(sorted_list)



