"""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  _A(		lowerCAmelCase=32			,  lowerCAmelCase=10			,  lowerCAmelCase=100			,  lowerCAmelCase=1026			,  lowerCAmelCase=True			,  lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl"			,  lowerCAmelCase="igf_context_pairs.jbl"			,  ):
							set_seed(3							)
							# generate train_data and objective_set
							A__				,							A__       :     str								=			generate_datasets(
							    lowerCAmelCase			,  lowerCAmelCase			,  number=lowerCAmelCase			,  min_len=1026			,  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?
							A__       :     Optional[int]								=			torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""							)

							# load pretrained model
							A__       :     List[str]								=			load_gpta("""gpt2"""							).to(lowerCAmelCase							)
							print("""computing perplexity on objective set"""							)
							A__       :     Any								=			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  _A(		lowerCAmelCase			,  lowerCAmelCase=15			,  lowerCAmelCase=128			,  lowerCAmelCase=100			,  lowerCAmelCase="igf_model.pt"			,  ):
							set_seed(42							)

							# Load pre-trained model
							A__       :     Any								=			GPTaLMHeadModel.from_pretrained("""gpt2"""							)

							# Initialize secondary learner to use embedding weights of model
							A__       :     List[Any]								=			SecondaryLearner(lowerCAmelCase							)

							# Train secondary learner
							A__       :     Dict								=			train_secondary_learner(
							    lowerCAmelCase			,  lowerCAmelCase			,  max_epochs=lowerCAmelCase			,  batch_size=lowerCAmelCase			,  eval_freq=100			,  igf_model_path=lowerCAmelCase			,  )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=32			,  lowerCAmelCase=1000			,  lowerCAmelCase=16			,  lowerCAmelCase=1.0			,  lowerCAmelCase=recopy_gpta			,  lowerCAmelCase=None			,  lowerCAmelCase=10			,  lowerCAmelCase="gpt2_finetuned.pt"			,  ):
							A__       :     List[Any]								=			torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""							)
							A__       :     Optional[Any]								=			RandomSampler(lowerCAmelCase							)
							A__       :     Dict								=			DataLoader(lowerCAmelCase			,  sampler=lowerCAmelCase							)

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(lowerCAmelCase							)
														secondary_learner.eval()
							A__       :     List[str]								=			[]
							A__       :     Optional[Any]								=			0

							A__       :     Union[str, Any]								=			[]
							A__       :     List[Any]								=			[]

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

																					if secondary_learner is not None:
																												A__       :     Optional[Any]								=			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 == 10:
																																			A__       :     Dict								=			-1
																												if predicted_q < threshold:
																																			A__       :     Tuple								=			False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()							)							)
																												A__       :     List[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()
																												A__       :     List[str]								=			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__       :     Any								=			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 > 60:
																												break
														if max_steps > 0 and global_step > 60:
																					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  _A(		):
							A__       :     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=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=32			,  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=100			,  type=lowerCAmelCase			,  help="""number of articles that are long enough to be used as our objective set"""			,  )
							parser.add_argument(
							    """--eval_freq"""			,  default=100			,  type=lowerCAmelCase			,  help="""secondary model evaluation is triggered at eval_freq"""							)

							parser.add_argument("""--max_steps"""			,  default=1000			,  type=lowerCAmelCase			,  help="""To calculate training epochs"""							)

							parser.add_argument(
							    """--secondary_learner_batch_size"""			,  default=128			,  type=lowerCAmelCase			,  help="""batch size of training data for secondary learner"""			,  )

							parser.add_argument(
							    """--batch_size"""			,  default=16			,  type=lowerCAmelCase			,  help="""batch size of training data of language model(gpt2) """							)

							parser.add_argument(
							    """--eval_interval"""			,  default=10			,  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=100			,  type=lowerCAmelCase			,  help="""The number of examples split to be used as objective_set/test_data"""							)

							parser.add_argument(
							    """--min_len"""			,  default=1026			,  type=lowerCAmelCase			,  help="""The minimum length of the article to be used as objective set"""							)

							parser.add_argument(
							    """--secondary_learner_max_epochs"""			,  default=15			,  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=32			,  max_steps=10			,  size_objective_set=100			,  min_len=1026			,  trim=lowerCAmelCase			,  data_file="""data/tokenized_stories_train_wikitext103.jbl"""			,  igf_data_file="""igf_context_pairs.jbl"""			,  )

							# Load train data for secondary learner
							A__       :     List[Any]								=			joblib.load("""data/IGF_values.jbl"""							)

							# Train secondary learner
							A__       :     Union[str, Any]								=			training_secondary_learner(
							    lowerCAmelCase			,  secondary_learner_max_epochs=15			,  secondary_learner_batch_size=128			,  eval_freq=100			,  igf_model_path="""igf_model.pt"""			,  )

							# load pretrained gpt2 model
							A__       :     List[Any]								=			GPTaLMHeadModel.from_pretrained("""gpt2"""							)
							set_seed(42							)

							# Generate train and test data to train and evaluate gpt2 model
							A__				,							A__       :     Any								=			generate_datasets(
							    context_len=32			,  file="""data/tokenized_stories_train_wikitext103.jbl"""			,  number=100			,  min_len=1026			,  trim=lowerCAmelCase							)

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  context_len=32			,  max_steps=1000			,  batch_size=16			,  threshold=1.0			,  recopy_model=lowerCAmelCase			,  secondary_learner=lowerCAmelCase			,  eval_interval=10			,  finetuned_model_name="""gpt2_finetuned.pt"""			,  )


if __name__ == "__main__":
				main()






"""simple docstring"""



import datasets

from .evaluate import evaluate


_UpperCamelCase								=		"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

_UpperCamelCase								=		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

_UpperCamelCase								=		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,   _KWARGS_DESCRIPTION     )
class 						__UpperCAmelCase    (datasets.Metric     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return datasets.MetricInfo(
										    description=_DESCRIPTION   ,      citation=_CITATION   ,      inputs_description=_KWARGS_DESCRIPTION   ,      features=datasets.Features(
										        {
										            """predictions""": {
										                """id""": datasets.Value("""string"""							),
										                """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""							)							),
										            },
										            """references""": {
										                """id""": datasets.Value("""string"""							),
										                """answers""": datasets.features.Sequence(
										                    {
										                        """text""": datasets.Value("""string"""							),
										                        """answer_start""": datasets.Value("""int32"""							),
										                    }							),
										            },
										        }							)   ,      codebase_urls=["""https://www.atticusprojectai.org/cuad"""]   ,      reference_urls=["""https://www.atticusprojectai.org/cuad"""]   ,      )





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
										A__       :     Optional[Any]								=			[
										    {
										        """paragraphs""": [
										            {
										                """qas""": [
										                    {
										                        """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
										                        """id""": ref["""id"""],
										                    }
										                    for ref in references
										                ]
										            }
										        ]
										    }
										]
										A__       :     Optional[Any]								=			evaluate(dataset=snake_case_   ,      predictions=snake_case_							)
										return score





