




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


__SCREAMING_SNAKE_CASE    :   Tuple       =logging.getLogger(__name__)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			str:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple     ,				lowerCamelCase:       Any     ,				lowerCamelCase:       Tuple						)					->			int:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple="gpt2"						)					->			str:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple     ,				lowerCamelCase:       str     ,				lowerCamelCase:       Optional[Any]						)					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				copy.deepcopy(__A						)
      model.to(__A						)

      lowerCamelCase__        =				['bias', 'LayerNorm.weight']
      lowerCamelCase__        =				[
          {
              '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},
      ]
      lowerCamelCase__        =				AdamW(__A     ,				lr=5e-5     ,				eps=1e-8						)
      lowerCamelCase__        =				get_linear_schedule_with_warmup(__A     ,				0     ,				__A						)
      torch.cuda.empty_cache()
      return model, lm_optimizer, lm_scheduler

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple     ,				lowerCamelCase:       Union[str, Any]     ,				lowerCamelCase:       int     ,				lowerCamelCase:       Any						)					->			str:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Any     ,				lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       List[Any]     ,				lowerCamelCase:       str     ,				lowerCamelCase:       Dict     ,				lowerCamelCase:       Dict     ,				lowerCamelCase:       Optional[Any]="dev.jbl"     ,				lowerCamelCase:       Union[str, Any]=recopy_gpta     ,				)					->			int:


      '''simple docstring'''




      lowerCamelCase__        =				[]
      lowerCamelCase__        =				[]
      lowerCamelCase__        =				[]

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

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

      for step in tqdm(range(__A						)						):
            lowerCamelCase__        =				torch.zeros((1, context_len)     ,				dtype=torch.long     ,				device=__A						)
            lowerCamelCase__        =				random.choice(__A						)
            lowerCamelCase__        =				random.randint(0     ,				len(story[0]						) - context_len - 1						)
            lowerCamelCase__        =				story[0][start : start + context_len]
            lm_optimizer.zero_grad()
            lowerCamelCase__        =				model(__A     ,				labels=__A						)
            lowerCamelCase__        =				outputs[0]
            lowerCamelCase__        =				compute_perplexity(__A     ,				__A     ,				__A						)
            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
            lowerCamelCase__        =				compute_perplexity(__A     ,				__A     ,				__A						)

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

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

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

      intermittent_save(__A     ,				__A     ,				__A     ,				__A						)

def 			lowerCAmelCase_			(  lowerCamelCase:       List[Any]     ,				lowerCamelCase:       Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"     ,				lowerCamelCase:       Optional[int]=1_00     ,				lowerCamelCase:       int=10_26     ,				lowerCamelCase:       int=True						)					->			List[Any]:


      '''simple docstring'''




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

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

def 			lowerCAmelCase_			(  lowerCamelCase:       Union[str, Any]     ,				lowerCamelCase:       int     ,				lowerCamelCase:       Any     ,				lowerCamelCase:       str     ,				lowerCamelCase:       Any=50     ,				lowerCamelCase:       Tuple="secondary_learner.pt"						)					->			Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__        =				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
      lowerCamelCase__        =				train_dataset[:5_12]
      lowerCamelCase__        =				train_dataset[5_12:]
      lowerCamelCase__        =				DataLoader(__A     ,				shuffle=__A     ,				batch_size=__A						)
      lowerCamelCase__        =				DataLoader(__A     ,				shuffle=__A     ,				batch_size=__A						)

      # secondary learner model set up
      lowerCamelCase__        =				nn.MSELoss()
      lowerCamelCase__        =				nn.MSELoss(reduction='sum'						)
      secondary_learner.to(__A						)
      lowerCamelCase__        =				torch.optim.Adam(secondary_learner.parameters()     ,				lr=0.00001						)
      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
      lowerCamelCase__        =				float('inf'						)
      # Iterate through batches until we've used max_steps batches
      for epoch in range(int(__A						)						):
            lowerCamelCase__        =				0.0
            secondary_learner.train()
            for step, batch in enumerate(__A						):
                  lowerCamelCase__        =				batch[0].to(__A						)
                  lowerCamelCase__        =				batch[1].to(__A						)
                  lowerCamelCase__        =				secondary_learner(__A						)
                  q_optimizer.zero_grad()
                  lowerCamelCase__        =				loss(__A     ,				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(__A						)):
                        lowerCamelCase__        =				tr_q_loss / (step + 1)

                        secondary_learner.eval()
                        lowerCamelCase__        =				0.0
                        lowerCamelCase__        =				0.0
                        lowerCamelCase__        =				[]
                        lowerCamelCase__        =				[]
                        # Compute performance of the secondary learner after this batch
                        for stepa, batcha in enumerate(__A						):
                              lowerCamelCase__        =				batcha[0].to(__A						)
                              lowerCamelCase__        =				batcha[1].to(__A						)
                              lowerCamelCase__        =				secondary_learner(__A						)
                              q_lossa += test_loss(__A     ,				__A						).item()
                              sum_qa += torch.sum(__A						).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(__A						)
                        print(
                            'Epoch: '     ,				__A     ,				'step: '     ,				__A     ,				'Avg. q:'     ,				sum_qa / len(__A						)     ,				'Train Loss: '     ,				__A     ,				'Test Loss: '     ,				__A     ,				)
                        if q_lossa < best_test_loss:
                              joblib.dump((predicted, actual)     ,				'pred_vs_actual.jbl'						)
                              torch.save(secondary_learner.state_dict()     ,				__A						)
                              lowerCamelCase__        =				q_lossa

                  secondary_learner.train()
      return secondary_learner






class 			A			(		nn.Module		):
     """simple docstring"""

     def __init__(    self  :   int	,  A__  :   int			)				->       List[Any]:
           super(A__	,  self			).__init__()
           lowerCamelCase__        =				model.transformer.wte
           lowerCamelCase__        =				copy.deepcopy(model.transformer.wte.weight			)
           lowerCamelCase__        =				nn.Convad(self.embeddings.weight.size(1			)	,  2_5_6	,  3	,  padding=1			)
           lowerCamelCase__        =				nn.Sequential(nn.Linear(2_5_6	,  3_2			)	,  nn.Dropout(p=0.1			)	,  nn.Linear(3_2	,  3_2			)	,  nn.Linear(3_2	,  1			)			)




     def  __snake_case   (    self  :   int	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				torch.max(self.conv(self.embeddings(A__			).squeeze(1			).transpose(1	,  2			)			)	,  2			)[0]
           lowerCamelCase__        =				self.fc(A__			)
           return qs.squeeze(1			)




     @classmethod
     def  __snake_case   (    cls  :   int	,  A__  :   str	,  A__  :   Optional[int]			)				->       Tuple:
           lowerCamelCase__        =				cls(A__			)  # this calls __init__
           lowerCamelCase__        =				torch.load(A__			)
           secondary_learner.load_state_dict(A__			)
           lowerCamelCase__        =				model.transformer.wte
           lowerCamelCase__        =				copy.deepcopy(model.transformer.wte.weight			)
           return secondary_learner





import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				arg

           assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
           assert outputfile is not None and not os.path.isfile(A__			), F"""{outputfile}"""
           if subset_list is not None:
                 with open(os.path.realpath(A__			)			) as f:
                       lowerCamelCase__        =				{self._vqa_file_split()[0] for x in tryload(A__			)}
           else:
                 lowerCamelCase__        =				None

           lowerCamelCase__        =				CONFIG
           if torch.cuda.is_available():
                 lowerCamelCase__        =				'cuda'
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				os.path.realpath(A__			)
           lowerCamelCase__        =				Preprocess(self.config			)
           lowerCamelCase__        =				GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'	,  config=self.config			)
           lowerCamelCase__        =				batch_size if batch_size != 0 else 1
           lowerCamelCase__        =				DEFAULT_SCHEMA




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           for i, file in enumerate(os.listdir(self.inputdir			)			):
                 if self.subset_list is not None and i not in self.subset_list:
                       continue
                 batch.append(self._vqa_file_split(A__			)			)
                 if len(A__			) == self.batch:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

           for i in range(1			):
                 yield list(map(A__	,  zip(*A__			)			)			)




     def __call__(    self  :   List[Any]			)				->       Optional[Any]:
           # make writer
           if not TEST:
                 lowerCamelCase__        =				datasets.ArrowWriter(features=self.schema	,  path=self.outputfile			)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.preprocess(A__			)
                 lowerCamelCase__        =				self.model(
                     A__	,  A__	,  scales_yx=A__	,  padding='max_detections'	,  max_detections=self.config.MAX_DETECTIONS	,  pad_value=0	,  return_tensors='np'	,  location='cpu'	,  )
                 lowerCamelCase__        =				output_dict.pop('normalized_boxes'			)
                 if not TEST:
                       lowerCamelCase__        =				np.array(A__			)
                       lowerCamelCase__        =				self.schema.encode_batch(A__			)
                       writer.write_batch(A__			)
                 if TEST:
                       break
                 # finalizer the writer
           if not TEST:
                 lowerCamelCase__  ,		lowerCamelCase__        =				writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




      try:
            lowerCamelCase__        =				json.load(lowerCamelCase						)
            try:
                  lowerCamelCase__        =				list(data.keys()						)
            except Exception:
                  lowerCamelCase__        =				[d['img_id'] for d in data]
      except Exception:
            try:
                  lowerCamelCase__        =				eval(stream.read()						)
            except Exception:
                  lowerCamelCase__        =				stream.read().split('\n'						)
      return data


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Tuple       =Extract(sys.argv[1:])
      extract()
      if not TEST:
            __SCREAMING_SNAKE_CASE    :   Any       =datasets.Dataset.from_file(extract.outputfile)
            # wala!
            # print(np.array(dataset[0:2]["roi_features"]).shape)