
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 				lowerCamelCase__				(						a__  :  Optional[Any]     )		->	Union[str, Any]:
       random.seed(a__     )
       np.random.seed(a__     )
       torch.manual_seed(a__     )
       torch.cuda.manual_seed_all(a__     )




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




def 				lowerCamelCase__				(						a__  :  List[str]="gpt2"     )		->	Tuple:
       UpperCamelCase_							       =       GPTaLMHeadModel.from_pretrained(a__	,      output_hidden_states=a__     )
       torch.save(model.state_dict()	,      model_name + """local.pt"""     )
       return model




def 				lowerCamelCase__				(						a__  :  Tuple	,      a__  :  int	,      a__  :  str     )		->	Optional[Any]:
       UpperCamelCase_							       =       copy.deepcopy(a__     )
       model.to(a__     )

       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(a__	,      lr=5e-5	,      eps=1e-8     )
       UpperCamelCase_							       =       get_linear_schedule_with_warmup(a__	,      0	,      a__     )
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler




def 				lowerCamelCase__				(						a__  :  Any	,      a__  :  List[Any]	,      a__  :  Optional[Any]	,      a__  :  int     )		->	Union[str, Any]:
       UpperCamelCase_							       =       np.array(a__     ).mean()
       UpperCamelCase_							       =       np.array(a__     ).std()
       UpperCamelCase_							       =       (real_perps - avg) / std
       UpperCamelCase_							       =       list(zip(a__	,      a__	,      a__     )     )
       joblib.dump(a__	,      a__     )




def 				lowerCamelCase__				(						a__  :  Optional[int]	,      a__  :  str	,      a__  :  int	,      a__  :  List[str]	,      a__  :  Any	,      a__  :  Dict	,      a__  :  int	,      a__  :  List[Any]="dev.jbl"	,      a__  :  Optional[int]=recopy_gpta	,      )		->	List[str]:
       UpperCamelCase_							       =       []
       UpperCamelCase_							       =       []
       UpperCamelCase_							       =       []

       # Initialize the transformer model
       UpperCamelCase_							       =       copy.deepcopy(a__     )
       orig_model.to(device="""cpu"""     )
       torch.cuda.empty_cache()

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

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

              # Periodically save the stored (X, IG(X)) pairs
              if step % 1000 == 0 and step > 1:
                     intermittent_save(a__	,      a__	,      a__	,      a__     )

              # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
              UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       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__				(						a__  :  Tuple	,      a__  :  List[str]="data/tokenized_stories_train_wikitext103.jbl"	,      a__  :  Tuple=100	,      a__  :  Optional[int]=1026	,      a__  :  Any=True     )		->	str:
       UpperCamelCase_							       =       joblib.load(a__     )
       print("""data loaded"""     )
       UpperCamelCase_							       =       []
       if trim:
              for i, example in enumerate(a__     ):
                     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(a__     ) >= number:
                            break
              UpperCamelCase_							       =       []
              for j in range(i + 1	,      len(a__     )     ):
                     if len(data[j][0]     ) > min_len:
                            train_data.append(data[j]     )
       else:
              UpperCamelCase_							       =       data[0:number]
              UpperCamelCase_							       =       data[number:]

       joblib.dump(a__	,      """objective_set.jbl"""     )
       print("""objective set saved"""     )
       return train_data, objective_set




def 				lowerCamelCase__				(						a__  :  Union[str, Any]	,      a__  :  int	,      a__  :  Dict	,      a__  :  str	,      a__  :  int=50	,      a__  :  int="secondary_learner.pt"     )		->	Any:
       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[:512]
       UpperCamelCase_							       =       train_dataset[512:]
       UpperCamelCase_							       =       DataLoader(a__	,      shuffle=a__	,      batch_size=a__     )
       UpperCamelCase_							       =       DataLoader(a__	,      shuffle=a__	,      batch_size=a__     )

       # secondary learner model set up
       UpperCamelCase_							       =       nn.MSELoss()
       UpperCamelCase_							       =       nn.MSELoss(reduction="""sum"""     )
       secondary_learner.to(a__     )
       UpperCamelCase_							       =       torch.optim.Adam(secondary_learner.parameters()	,      lr=0.00_001     )
       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(a__     )     ):
              UpperCamelCase_							       =       0.0
              secondary_learner.train()
              for step, batch in enumerate(a__     ):
                     UpperCamelCase_							       =       batch[0].to(a__     )
                     UpperCamelCase_							       =       batch[1].to(a__     )
                     UpperCamelCase_							       =       secondary_learner(a__     )
                     q_optimizer.zero_grad()
                     UpperCamelCase_							       =       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__     )):
                            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(a__     ):
                                   UpperCamelCase_							       =       batcha[0].to(a__     )
                                   UpperCamelCase_							       =       batcha[1].to(a__     )
                                   UpperCamelCase_							       =       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__     )
                                   UpperCamelCase_							       =       q_lossa

                     secondary_learner.train()
       return secondary_learner







class 		lowercase_       (       nn.Module				):

 def __init__(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        super(__UpperCamelCase     ,	self     ).__init__()
        UpperCamelCase_							       =       model.transformer.wte
        UpperCamelCase_							       =       copy.deepcopy(model.transformer.wte.weight     )
        UpperCamelCase_							       =       nn.Convad(self.embeddings.weight.size(1     )     ,	2_5_6     ,	3     ,	padding=1     )
        UpperCamelCase_							       =       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 lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       torch.max(self.conv(self.embeddings(__UpperCamelCase     ).squeeze(1     ).transpose(1     ,	2     )     )     ,	2     )[0]
        UpperCamelCase_							       =       self.fc(__UpperCamelCase     )
        return qs.squeeze(1     )

 @classmethod
 def lowerCamelCase_				(      cls     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       cls(__UpperCamelCase     )  # this calls __init__
        UpperCamelCase_							       =       torch.load(__UpperCamelCase     )
        secondary_learner.load_state_dict(__UpperCamelCase     )
        UpperCamelCase_							       =       model.transformer.wte
        UpperCamelCase_							       =       copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor

class 		lowercase_       (       unittest.TestCase				):

 def __init__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=7     ,	__UpperCamelCase=3     ,	__UpperCamelCase=1_8     ,	__UpperCamelCase=3_0     ,	__UpperCamelCase=4_0_0     ,	__UpperCamelCase=True     ,	__UpperCamelCase=None     ,	__UpperCamelCase=2     ,	__UpperCamelCase=True     ,	__UpperCamelCase=[0.5, 0.5, 0.5]     ,	__UpperCamelCase=[0.5, 0.5, 0.5]     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       size if size is not None else {"""shortest_edge""": 3_0}
        UpperCamelCase_							       =       parent
        UpperCamelCase_							       =       batch_size
        UpperCamelCase_							       =       num_channels
        UpperCamelCase_							       =       image_size
        UpperCamelCase_							       =       min_resolution
        UpperCamelCase_							       =       max_resolution
        UpperCamelCase_							       =       do_resize
        UpperCamelCase_							       =       size
        UpperCamelCase_							       =       size_divisor
        UpperCamelCase_							       =       do_normalize
        UpperCamelCase_							       =       image_mean
        UpperCamelCase_							       =       image_std

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase=False     ):




        """simple docstring"""


        if not batched:
               UpperCamelCase_							       =       self.size["""shortest_edge"""]
               UpperCamelCase_							       =       image_inputs[0]
               if isinstance(__UpperCamelCase     ,	Image.Image     ):
                      UpperCamelCase_       ,				UpperCamelCase_							       =       image.size
               else:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape[1], image.shape[2]
               UpperCamelCase_							       =       size / min(__UpperCamelCase     ,	__UpperCamelCase     )
               if h < w:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       size, scale * w
               else:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       scale * h, size

               UpperCamelCase_							       =       int((1_3_3_3 / 8_0_0) * size     )
               if max(__UpperCamelCase     ,	__UpperCamelCase     ) > max_size:
                      UpperCamelCase_							       =       max_size / max(__UpperCamelCase     ,	__UpperCamelCase     )
                      UpperCamelCase_							       =       newh * scale
                      UpperCamelCase_							       =       neww * scale

               UpperCamelCase_       ,				UpperCamelCase_							       =       int(newh + 0.5     ), int(neww + 0.5     )
               UpperCamelCase_       ,				UpperCamelCase_							       =       (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

        else:
               UpperCamelCase_							       =       []
               for image in image_inputs:
                      UpperCamelCase_       ,				UpperCamelCase_							       =       self.get_expected_values([image]     )
                      expected_values.append((expected_height, expected_width)     )
               UpperCamelCase_							       =       max(__UpperCamelCase     ,	key=lambda __UpperCamelCase     : item[0]     )[0]
               UpperCamelCase_							       =       max(__UpperCamelCase     ,	key=lambda __UpperCamelCase     : item[1]     )[1]

        return expected_height, expected_width



@require_torch
@require_vision
class 		lowercase_       (       __SCREAMING_SNAKE_CASE							,	unittest.TestCase				):
 A__						:				Optional[int]         =    ViltImageProcessor if is_vision_available() else None

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       ViltImageProcessingTester(self     )

 @property
 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""image_mean"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""image_std"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""do_normalize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""do_resize"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""size"""     )     )
        self.assertTrue(hasattr(__UpperCamelCase     ,	"""size_divisor"""     )     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 3_0}     )

        UpperCamelCase_							       =       self.image_processing_class.from_dict(self.image_processor_dict     ,	size=4_2     )
        self.assertEqual(image_processor.size     ,	{"""shortest_edge""": 4_2}     )

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        pass

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        UpperCamelCase_							       =       prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	Image.Image     )

        # Test not batched input
        UpperCamelCase_							       =       image_processing(image_inputs[0]     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        UpperCamelCase_							       =       prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__UpperCamelCase     ,	numpify=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	np.ndarray     )

        # Test not batched input
        UpperCamelCase_							       =       image_processing(image_inputs[0]     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)

 def lowerCamelCase_				(      self     ):




        """simple docstring"""


        UpperCamelCase_							       =       self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        UpperCamelCase_							       =       prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__UpperCamelCase     ,	torchify=__UpperCamelCase     )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase     ,	torch.Tensor     )

        # Test not batched input
        UpperCamelCase_							       =       image_processing(image_inputs[0]     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,	)

        # Test batched
        UpperCamelCase_							       =       image_processing(__UpperCamelCase     ,	return_tensors="""pt"""     ).pixel_values

        UpperCamelCase_       ,				UpperCamelCase_							       =       self.image_processor_tester.get_expected_values(__UpperCamelCase     ,	batched=__UpperCamelCase     )
        self.assertEqual(
            encoded_images.shape     ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,	)



