




import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


a_										=       logging.getLogger(__name__)




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[Any]:

				"""simple docstring"""


				random.seed(lowercase_ )
				np.random.seed(lowercase_ )
				torch.manual_seed(lowercase_ )
				torch.cuda.manual_seed_all(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		List[str]:

				"""simple docstring"""


				model.eval()
				__UpperCamelCase											=  next(model.parameters() ).device
				__UpperCamelCase											=  1
				__UpperCamelCase											=  torch.zeros((eval_batch_size, context_len)   ,       dtype=torch.long   ,       device=lowercase_ )
				__UpperCamelCase											=  DataLoader(lowercase_   ,       shuffle=lowercase_   ,       batch_size=lowercase_ )
				__UpperCamelCase											=  torch.zeros(1   ,       device=lowercase_ )
				__UpperCamelCase											=  0
				for batch in eval_dataloader:
								batch.to(lowercase_ )
								# pad
								context.zero_()
								for i in range(lowercase_ ):
												__UpperCamelCase											=  batch[i]
								__UpperCamelCase											=  model(lowercase_   ,       labels=lowercase_ )
								eval_loss += outputs[0].sum().item()
								nb_eval_examples += batch.size(0 )
				__UpperCamelCase											=  eval_loss / nb_eval_examples
				__UpperCamelCase											=  torch.exp(lowercase_ )
				model.train()
				return perplexity




def    __SCREAMING_SNAKE_CASE				(					lowercase_="gpt2" )    ->		Optional[Any]:

				"""simple docstring"""


				__UpperCamelCase											=  GPTaLMHeadModel.from_pretrained(lowercase_   ,       output_hidden_states=lowercase_ )
				torch.save(model.state_dict()   ,       model_name + '''local.pt''' )
				return model




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Optional[Any]:

				"""simple docstring"""


				__UpperCamelCase											=  copy.deepcopy(lowercase_ )
				model.to(lowercase_ )

				__UpperCamelCase											=  ['''bias''', '''LayerNorm.weight''']
				__UpperCamelCase											=  [
				    {
				        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
				        '''weight_decay''': 0.0,
				    },
				    {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
				]
				__UpperCamelCase											=  AdamW(lowercase_   ,       lr=5E-5   ,       eps=1E-8 )
				__UpperCamelCase											=  get_linear_schedule_with_warmup(lowercase_   ,       0   ,       lowercase_ )
				torch.cuda.empty_cache()
				return model, lm_optimizer, lm_scheduler




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Optional[int]:

				"""simple docstring"""


				__UpperCamelCase											=  np.array(lowercase_ ).mean()
				__UpperCamelCase											=  np.array(lowercase_ ).std()
				__UpperCamelCase											=  (real_perps - avg) / std
				__UpperCamelCase											=  list(zip(lowercase_   ,       lowercase_   ,       lowercase_ ) )
				joblib.dump(lowercase_   ,       lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_="dev.jbl"   ,       lowercase_=recopy_gpta   ,       )    ->		str:

				"""simple docstring"""


				__UpperCamelCase											=  []
				__UpperCamelCase											=  []
				__UpperCamelCase											=  []

				# Initialize the transformer model
				__UpperCamelCase											=  copy.deepcopy(lowercase_ )
				orig_model.to(device='''cpu''' )
				torch.cuda.empty_cache()

				# Compute perplexity of initial transformer model for comparison
				model.train()
				__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  recopy_model(lowercase_   ,       lowercase_   ,       lowercase_ )

				for step in tqdm(range(lowercase_ ) ):
								__UpperCamelCase											=  torch.zeros((1, context_len)   ,       dtype=torch.long   ,       device=lowercase_ )
								__UpperCamelCase											=  random.choice(lowercase_ )
								__UpperCamelCase											=  random.randint(0   ,       len(story[0] ) - context_len - 1 )
								__UpperCamelCase											=  story[0][start : start + context_len]
								lm_optimizer.zero_grad()
								__UpperCamelCase											=  model(lowercase_   ,       labels=lowercase_ )
								__UpperCamelCase											=  outputs[0]
								__UpperCamelCase											=  compute_perplexity(lowercase_   ,       lowercase_   ,       lowercase_ )
								model.train()
								lm_loss.backward()
								# Do LM backprop
								torch.nn.utils.clip_grad_norm_(model.parameters()   ,       3.0 )
								lm_optimizer.step()
								lm_scheduler.step()  # Update learning rate schedule

								# Compute perplexity after back-propagating on the selected context
								__UpperCamelCase											=  compute_perplexity(lowercase_   ,       lowercase_   ,       lowercase_ )

								# Periodically save the stored (X, IG(X)) pairs
								if step % 10_00 == 0 and step > 1:
												intermittent_save(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )

								# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
								__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  recopy_model(lowercase_   ,       lowercase_   ,       lowercase_ )

								past_perps.append(past_perp.item() )
								real_perps.append(orig_perp - real_perp.item() )
								contexts.append(np.array(context.cpu() ) )

				intermittent_save(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_="data/tokenized_stories_train_wikitext103.jbl"   ,       lowercase_=1_00   ,       lowercase_=10_26   ,       lowercase_=True )    ->		int:

				"""simple docstring"""


				__UpperCamelCase											=  joblib.load(lowercase_ )
				print('''data loaded''' )
				__UpperCamelCase											=  []
				if trim:
								for i, example in enumerate(lowercase_ ):
												if len(example[0] ) > min_len:
																__UpperCamelCase											=  random.randint(0   ,       len(example[0] ) - context_len - 1 )
																objective_set.append(example[0, start : start + context_len] )
												if len(lowercase_ ) >= number:
																break
								__UpperCamelCase											=  []
								for j in range(i + 1   ,       len(lowercase_ ) ):
												if len(data[j][0] ) > min_len:
																train_data.append(data[j] )
				else:
								__UpperCamelCase											=  data[0:number]
								__UpperCamelCase											=  data[number:]

				joblib.dump(lowercase_   ,       '''objective_set.jbl''' )
				print('''objective set saved''' )
				return train_data, objective_set




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_=50   ,       lowercase_="secondary_learner.pt" )    ->		Any:

				"""simple docstring"""


				__UpperCamelCase											=  torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
				# We will use the first 512 pairs from our dataset as a test set for
				# our secondary learner and the rest to train
				__UpperCamelCase											=  train_dataset[:5_12]
				__UpperCamelCase											=  train_dataset[5_12:]
				__UpperCamelCase											=  DataLoader(lowercase_   ,       shuffle=lowercase_   ,       batch_size=lowercase_ )
				__UpperCamelCase											=  DataLoader(lowercase_   ,       shuffle=lowercase_   ,       batch_size=lowercase_ )

				# secondary learner model set up
				__UpperCamelCase											=  nn.MSELoss()
				__UpperCamelCase											=  nn.MSELoss(reduction='''sum''' )
				secondary_learner.to(lowercase_ )
				__UpperCamelCase											=  torch.optim.Adam(secondary_learner.parameters()   ,       lr=0.0_0001 )
				secondary_learner.train()

				# TODO in original code this is written as number of actual batches seen
				# not number of items seen but other places it is number of items instead.
				# improve consistency! changed this to epochs for clarity
				__UpperCamelCase											=  float('''inf''' )
				# Iterate through batches until we've used max_steps batches
				for epoch in range(int(lowercase_ ) ):
								__UpperCamelCase											=  0.0
								secondary_learner.train()
								for step, batch in enumerate(lowercase_ ):
												__UpperCamelCase											=  batch[0].to(lowercase_ )
												__UpperCamelCase											=  batch[1].to(lowercase_ )
												__UpperCamelCase											=  secondary_learner(lowercase_ )
												q_optimizer.zero_grad()
												__UpperCamelCase											=  loss(lowercase_   ,       real_q.float() )
												q_loss.backward()
												q_optimizer.step()
												tr_q_loss += q_loss.item()

												# model trains fairly quickly so we won't wait for a full epoch
												# eval is triggered at eval_freq and end of epochs
												if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowercase_ )):
																__UpperCamelCase											=  tr_q_loss / (step + 1)

																secondary_learner.eval()
																__UpperCamelCase											=  0.0
																__UpperCamelCase											=  0.0
																__UpperCamelCase											=  []
																__UpperCamelCase											=  []
																# Compute performance of the secondary learner after this batch
																for stepa, batcha in enumerate(lowercase_ ):
																				__UpperCamelCase											=  batcha[0].to(lowercase_ )
																				__UpperCamelCase											=  batcha[1].to(lowercase_ )
																				__UpperCamelCase											=  secondary_learner(lowercase_ )
																				q_lossa += test_loss(lowercase_   ,       lowercase_ ).item()
																				sum_qa += torch.sum(lowercase_ ).item()
																				for ei, i in enumerate(predicted_qa.cpu().detach().numpy() ):
																								predicted.append(i.item() )
																				for ei, i in enumerate(real_qa.cpu().detach().numpy() ):
																								actual.append(i.item() )

																q_lossa /= len(lowercase_ )
																print(
																    '''Epoch: '''   ,       lowercase_   ,       '''step: '''   ,       lowercase_   ,       '''Avg. q:'''   ,       sum_qa / len(lowercase_ )   ,       '''Train Loss: '''   ,       lowercase_   ,       '''Test Loss: '''   ,       lowercase_   ,       )
																if q_lossa < best_test_loss:
																				joblib.dump((predicted, actual)   ,       '''pred_vs_actual.jbl''' )
																				torch.save(secondary_learner.state_dict()   ,       lowercase_ )
																				__UpperCamelCase											=  q_lossa

												secondary_learner.train()
				return secondary_learner


class 	_lowerCamelCase  (     nn.Module				):

						"""simple docstring"""

						def __init__(       self   :	Optional[int]	,    snake_case   :	int			):
										super(snake_case	,    self			).__init__()
										__UpperCamelCase											=  model.transformer.wte
										__UpperCamelCase											=  copy.deepcopy(model.transformer.wte.weight			)
										__UpperCamelCase											=  nn.Convad(self.embeddings.weight.size(1			)	,    256	,    3	,    padding=1			)
										__UpperCamelCase											=  nn.Sequential(nn.Linear(256	,    32			)	,    nn.Dropout(p=0.1			)	,    nn.Linear(32	,    32			)	,    nn.Linear(32	,    1			)			)







						def  snake_case		(       self   :	Any	,    snake_case   :	Optional[Any]			):
										__UpperCamelCase											=  torch.max(self.conv(self.embeddings(snake_case			).squeeze(1			).transpose(1	,    2			)			)	,    2			)[0]
										__UpperCamelCase											=  self.fc(snake_case			)
										return qs.squeeze(1			)







						@classmethod
						def  snake_case		(       cls   :	Union[str, Any]	,    snake_case   :	List[Any]	,    snake_case   :	Tuple			):
										__UpperCamelCase											=  cls(snake_case			)  # this calls __init__
										__UpperCamelCase											=  torch.load(snake_case			)
										secondary_learner.load_state_dict(snake_case			)
										__UpperCamelCase											=  model.transformer.wte
										__UpperCamelCase											=  copy.deepcopy(model.transformer.wte.weight			)
										return secondary_learner












import argparse
import json
import re
import string
import sys
from collections import Counter




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

    """simple docstring"""


    def remove_articles(lowercase_ ):
        return re.sub(r'''\b(a|an|the)\b'''   ,       ''' '''   ,       lowercase_ )

    def white_space_fix(lowercase_ ):
        return " ".join(text.split() )

    def remove_punc(lowercase_ ):
        __UpperCamelCase											=  set(string.punctuation )
        return "".join(ch for ch in text if ch not in exclude )

    def lower(lowercase_ ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[str]:

    """simple docstring"""


    __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
    __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
    __UpperCamelCase											=  Counter(lowercase_ ) & Counter(lowercase_ )
    __UpperCamelCase											=  sum(common.values() )
    if num_same == 0:
        return 0
    __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
    __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
    __UpperCamelCase											=  (2 * precision * recall) / (precision + recall)
    return fa




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Any:

    """simple docstring"""


    return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  []
    for ground_truth in ground_truths:
        __UpperCamelCase											=  metric_fn(lowercase_   ,       lowercase_ )
        scores_for_ground_truths.append(lowercase_ )
    return max(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Optional[int]:

    """simple docstring"""


    __UpperCamelCase											=  __UpperCamelCase											=  __UpperCamelCase											=  0
    __UpperCamelCase											=  []
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                __UpperCamelCase											=  F"Unanswered question {qa['id']} will receive score 0."
                print(lowercase_   ,       file=sys.stderr )
                continue

            __UpperCamelCase											=  [x['''text'''] for x in qa['''answers''']]
            __UpperCamelCase											=  predictions[qa['''id''']]

            __UpperCamelCase											=  metric_max_over_ground_truths(lowercase_   ,       lowercase_   ,       lowercase_ )
            if int(_exact_match ) == 1:
                correct_ids.append(qa['''id'''] )
            exact_match += _exact_match

            fa += metric_max_over_ground_truths(lowercase_   ,       lowercase_   ,       lowercase_ )

    __UpperCamelCase											=  exact_match / total
    __UpperCamelCase											=  fa / total

    return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
 a_										=       "1.0"
 a_										=       argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
 parser.add_argument("data_file", help="The dataset file in JSON format.")
 parser.add_argument("pred_file", help="The model prediction file in JSON format.")
 parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
 a_										=       parser.parse_args()

 with open(args.data_file) as data_file:
  a_										=       json.load(data_file)
  if dataset_json["version"] != expected_version:
   print(
       f"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}""",
       file=sys.stderr,
   )
  a_										=       dataset_json["data"]

 with open(args.pred_file) as pred_file:
  a_										=       json.load(pred_file)

 a_						,      a_										=       evaluate(dataset, predictions)

 if args.output_correct_ids:
  print(f"""Output {len(correct_ids)} correctly answered question IDs.""")
  with open("correct_ids.json", "w") as f:
   json.dump(correct_ids, f)






