






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      snake_case		(							UpperCAmelCase	:       List[str]=32,						UpperCAmelCase	:       List[Any]=10,						UpperCAmelCase	:       Dict=1_00,						UpperCAmelCase	:       Tuple=10_26,						UpperCAmelCase	:       int=True,						UpperCAmelCase	:       Optional[int]="data/tokenized_stories_train_wikitext103.jbl",						UpperCAmelCase	:       int="igf_context_pairs.jbl",						):
				set_seed(3   )
				# generate train_data and objective_set
				A ,		A	 =      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?
				A	 =      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'   )

				# load pretrained model
				A	 =      load_gpta('gpt2'   ).to(UpperCAmelCase   )
				print('computing perplexity on objective set'   )
				A	 =      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      snake_case		(							UpperCAmelCase	:       Dict,						UpperCAmelCase	:       List[str]=15,						UpperCAmelCase	:       List[Any]=1_28,						UpperCAmelCase	:       List[Any]=1_00,						UpperCAmelCase	:       List[Any]="igf_model.pt",						):
				set_seed(42   )

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

				# Initialize secondary learner to use embedding weights of model
				A	 =      SecondaryLearner(UpperCAmelCase   )

				# Train secondary learner
				A	 =      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      snake_case		(							UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Dict=32,						UpperCAmelCase	:       List[Any]=10_00,						UpperCAmelCase	:       List[Any]=16,						UpperCAmelCase	:       Optional[Any]=1.0,						UpperCAmelCase	:       List[str]=recopy_gpta,						UpperCAmelCase	:       Optional[Any]=None,						UpperCAmelCase	:       Tuple=10,						UpperCAmelCase	:       Any="gpt2_finetuned.pt",						):
				A	 =      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'   )
				A	 =      RandomSampler(UpperCAmelCase   )
				A	 =      DataLoader(UpperCAmelCase,						sampler=UpperCAmelCase   )

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

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

				A	 =      []
				A	 =      []

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

												if secondary_learner is not None:
																A	 =      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:
																				A	 =      -1
																if predicted_q < threshold:
																				A	 =      False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()   )   )
																A	 =      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()
																A	 =      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:
																				A	 =      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      snake_case		(							):
				A	 =      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
				A	 =      joblib.load('data/IGF_values.jbl'   )

				# Train secondary learner
				A	 =      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
				A	 =      GPTaLMHeadModel.from_pretrained('gpt2'   )
				set_seed(42   )

				# Generate train and test data to train and evaluate gpt2 model
				A ,		A	 =      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()













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





