

'''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__=32,       UpperCAmelCase__=10,       UpperCAmelCase__=1_00,       UpperCAmelCase__=10_26,       UpperCAmelCase__=True,       UpperCAmelCase__="data/tokenized_stories_train_wikitext103.jbl",       UpperCAmelCase__="igf_context_pairs.jbl",       )			->			List[str]:
							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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=15,       UpperCAmelCase__=1_28,       UpperCAmelCase__=1_00,       UpperCAmelCase__="igf_model.pt",       )			->			List[Any]:
							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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=32,       UpperCAmelCase__=10_00,       UpperCAmelCase__=16,       UpperCAmelCase__=1.0,       UpperCAmelCase__=recopy_gpta,       UpperCAmelCase__=None,       UpperCAmelCase__=10,       UpperCAmelCase__="gpt2_finetuned.pt",       )			->			Optional[Any]:
							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       UpperCAmelCase__    (      )			->			Optional[Any]:
							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()



'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowerCamelCase		       =    imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowerCamelCase		       =    cvtColor(img, COLOR_BGR2GRAY)
def       UpperCAmelCase__    (      )			->			Dict:
       A_						=      cn.convert_to_negative(UpperCAmelCase__				)
       # assert negative_img array for at least one True
       assert negative_img.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
              # Work around assertion for response
              assert str(cc.change_contrast(UpperCAmelCase__,       1_10				)				).startswith(
                  """<PIL.Image.Image image mode=RGB size=100x100 at"""				)
def       UpperCAmelCase__    (      )			->			str:
       A_						=      canny.gen_gaussian_kernel(9,       sigma=1.4				)
       # Assert ambiguous array
       assert resp.all()
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      imread("""digital_image_processing/image_data/lena_small.jpg""",       0				)
       # assert ambiguous array for all == True
       assert canny_img.all()
       A_						=      canny.canny(UpperCAmelCase__				)
       # assert canny array for at least one True
       assert canny_array.any()
def       UpperCAmelCase__    (      )			->			Dict:
       assert gg.gaussian_filter(UpperCAmelCase__,       5,       sigma=0.9				).all()
def       UpperCAmelCase__    (      )			->			int:
       # laplace diagonals
       A_						=      array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
       A_						=      conv.img_convolve(UpperCAmelCase__,       UpperCAmelCase__				).astype(UpperCAmelCase__				)
       assert res.any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       assert med.median_filter(UpperCAmelCase__,       3				).any()
def       UpperCAmelCase__    (      )			->			List[Any]:
       A_					,			A_						=      sob.sobel_filter(UpperCAmelCase__				)
       assert grad.any() and theta.any()
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      sp.make_sepia(UpperCAmelCase__,       20				)
       assert sepia.all()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg"				)			->			List[Any]:
       A_						=      bs.Burkes(imread(UpperCAmelCase__,       1				),       1_20				)
       burkes.process()
       assert burkes.output_img.any()
def       UpperCAmelCase__    (      UpperCAmelCase__ = "digital_image_processing/image_data/lena_small.jpg",       )			->			Optional[int]:
       A_						=      rs.NearestNeighbour(imread(UpperCAmelCase__,       1				),       4_00,       2_00				)
       nn.process()
       assert nn.output.any()







def       UpperCAmelCase__    (      )			->			Optional[int]:
       A_						=      """digital_image_processing/image_data/lena.jpg"""

       # Reading the image and converting it to grayscale.
       A_						=      imread(UpperCAmelCase__,       0				)

       # Test for get_neighbors_pixel function() return not None
       A_						=      0
       A_						=      0
       A_						=      image[x_coordinate][y_coordinate]

       A_						=      lbp.get_neighbors_pixel(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert neighbors_pixels is not None

       # Test for local_binary_pattern function()
       # Create a numpy array as the same height and width of read image
       A_						=      np.zeros((image.shape[0], image.shape[1])				)

       # Iterating through the image and calculating the local binary pattern value
       # for each pixel.
       for i in range(0,       image.shape[0]				):
              for j in range(0,       image.shape[1]				):
                     A_						=      lbp.local_binary_value(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       assert lbp_image.any()
