



'''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 lowerCAmelCase  (__A=32  ,	__A=10  ,	__A=100  ,	__A=1_026  ,	__A=True  ,	__A="data/tokenized_stories_train_wikitext103.jbl"  ,	__A="igf_context_pairs.jbl"  ,	):



     """simple docstring"""



     set_seed(3)
     # generate train_data and objective_set
     _a  ,    _a      =  generate_datasets(
         __A  ,	__A  ,	number=__A  ,	min_len=1_026  ,	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?
     _a      =  torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')

     # load pretrained model
     _a      =  load_gpta('''gpt2''').to(__A)
     print('''computing perplexity on objective set''')
     _a      =  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 lowerCAmelCase  (__A  ,	__A=15  ,	__A=128  ,	__A=100  ,	__A="igf_model.pt"  ,	):



     """simple docstring"""



     set_seed(42)

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

     # Initialize secondary learner to use embedding weights of model
     _a      =  SecondaryLearner(__A)

     # Train secondary learner
     _a      =  train_secondary_learner(
         __A  ,	__A  ,	max_epochs=__A  ,	batch_size=__A  ,	eval_freq=100  ,	igf_model_path=__A  ,	)

     del model, secondary_learner_train_data
     torch.cuda.empty_cache()

     return secondary_learner




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A=32  ,	__A=1_000  ,	__A=16  ,	__A=1.0  ,	__A=recopy_gpta  ,	__A=None  ,	__A=10  ,	__A="gpt2_finetuned.pt"  ,	):



     """simple docstring"""



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

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

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

     _a      =  []
     _a      =  []

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

               if secondary_learner is not None:
                    _a      =  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:
                         _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(__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 lowerCAmelCase  ():



     """simple docstring"""



     _a      =  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=100  ,	type=__A  ,	help='''number of articles that are long enough to be used as our objective set'''  ,	)
     parser.add_argument(
         '''--eval_freq'''  ,	default=100  ,	type=__A  ,	help='''secondary model evaluation is triggered at eval_freq''')

     parser.add_argument('''--max_steps'''  ,	default=1_000  ,	type=__A  ,	help='''To calculate training epochs''')

     parser.add_argument(
         '''--secondary_learner_batch_size'''  ,	default=128  ,	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=100  ,	type=__A  ,	help='''The number of examples split to be used as objective_set/test_data''')

     parser.add_argument(
         '''--min_len'''  ,	default=1_026  ,	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=100  ,	min_len=1_026  ,	trim=__A  ,	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(
         __A  ,	secondary_learner_max_epochs=15  ,	secondary_learner_batch_size=128  ,	eval_freq=100  ,	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=100  ,	min_len=1_026  ,	trim=__A)

     # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
     finetune(
         __A  ,	__A  ,	__A  ,	context_len=32  ,	max_steps=1_000  ,	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()








'''simple docstring'''





from typing import Dict, Optional

import numpy as np

import datasets


lowercase_		       =  "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

lowercase_		       =  "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

lowercase_		       =  "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     if label_map is not None:
          for old_id, new_id in label_map.items():
               _a      =  new_id

    # turn into Numpy arrays
     _a      =  np.array(__A)
     _a      =  np.array(__A)

     if reduce_labels:
          _a      =  255
          _a      =  label - 1
          _a      =  255

     _a      =  label != ignore_index
     _a      =  np.not_equal(__A  ,	__A)
     _a      =  pred_label[mask]
     _a      =  np.array(__A)[mask]

     _a      =  pred_label[pred_label == label]

     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]
     _a      =  np.histogram(__A  ,	bins=__A  ,	range=(0, num_labels - 1))[0]

     _a      =  area_pred_label + area_label - area_intersect

     return area_intersect, area_union, area_pred_label, area_label




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     _a      =  np.zeros((num_labels,)  ,	dtype=np.floataa)
     for result, gt_seg_map in zip(__A  ,	__A):
          _a  ,    _a  ,    _a  ,    _a      =  intersect_and_union(
              __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)
          total_area_intersect += area_intersect
          total_area_union += area_union
          total_area_pred_label += area_pred_label
          total_area_label += area_label
     return total_area_intersect, total_area_union, total_area_pred_label, total_area_label




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A = None  ,	__A = None  ,	__A = False  ,	):



     """simple docstring"""



     _a  ,    _a  ,    _a  ,    _a      =  total_intersect_and_union(
         __A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)

     # compute metrics
     _a      =  {}

     _a      =  total_area_intersect.sum() / total_area_label.sum()
     _a      =  total_area_intersect / total_area_union
     _a      =  total_area_intersect / total_area_label

     _a      =  np.nanmean(__A)
     _a      =  np.nanmean(__A)
     _a      =  all_acc
     _a      =  iou
     _a      =  acc

     if nan_to_num is not None:
          _a      =  {metric: np.nan_to_num(__A  ,	nan=__A) for metric, metric_value in metrics.items()}

     return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,   _KWARGS_DESCRIPTION						)
class __A			(  datasets.Metric						):



 '''simple docstring'''







 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
              # 1st Seq - height dim, 2nd - width dim
              {
                  '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
                  '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''       )       )       ),
              }       ) ,   reference_urls=[
              '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
          ] ,   )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = None ,   A = False ,   )    ->     List[Any]:


      """simple docstring"""



      _a      =  mean_iou(
          results=A ,   gt_seg_maps=A ,   num_labels=A ,   ignore_index=A ,   nan_to_num=A ,   label_map=A ,   reduce_labels=A ,   )
      return iou_result



