
'''simple docstring'''





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__		:   Optional[Any]            =       logging.getLogger(__name__)





def        snake_case							(		UpperCAmelCase	)->			Union[str, Any]:







      """simple docstring"""
      random.seed(UpperCAmelCase	)
      np.random.seed(UpperCAmelCase	)
      torch.manual_seed(UpperCAmelCase	)
      torch.cuda.manual_seed_all(UpperCAmelCase	)





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			List[Any]:







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





def        snake_case							(		UpperCAmelCase="gpt2"	)->			Optional[Any]:







      """simple docstring"""
      __A       	=			GPTaLMHeadModel.from_pretrained(UpperCAmelCase      ,  output_hidden_states=UpperCAmelCase	)
      torch.save(model.state_dict()      ,  model_name + 'local.pt'	)
      return model





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			Dict:







      """simple docstring"""
      __A       	=			copy.deepcopy(UpperCAmelCase	)
      model.to(UpperCAmelCase	)

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





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			List[str]:







      """simple docstring"""
      __A       	=			np.array(UpperCAmelCase	).mean()
      __A       	=			np.array(UpperCAmelCase	).std()
      __A       	=			(real_perps - avg) / std
      __A       	=			list(zip(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)	)
      joblib.dump(UpperCAmelCase      ,  UpperCAmelCase	)





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase="dev.jbl"      ,  UpperCAmelCase=recopy_gpta      ,  )->			Any:







      """simple docstring"""
      __A       	=			[]
      __A       	=			[]
      __A       	=			[]

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

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

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

            # Periodically save the stored (X, IG(X)) pairs
            if step % 1_0_0_0 == 0 and step > 1:
                  intermittent_save(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)

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

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

      intermittent_save(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl"      ,  UpperCAmelCase=1_0_0      ,  UpperCAmelCase=1_0_2_6      ,  UpperCAmelCase=True	)->			Optional[int]:







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

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





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=5_0      ,  UpperCAmelCase="secondary_learner.pt"	)->			Optional[int]:







      """simple docstring"""
      __A       	=			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
      __A       	=			train_dataset[:5_1_2]
      __A       	=			train_dataset[5_1_2:]
      __A       	=			DataLoader(UpperCAmelCase      ,  shuffle=UpperCAmelCase      ,  batch_size=UpperCAmelCase	)
      __A       	=			DataLoader(UpperCAmelCase      ,  shuffle=UpperCAmelCase      ,  batch_size=UpperCAmelCase	)

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

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

                  secondary_learner.train()
      return secondary_learner


class  UpperCamelCase__  (    nn.Module):






 def __init__(			self  :int	,				_A  :List[str]			)				->			Optional[Any]:







       '''simple docstring'''



       super(_A	,				self			).__init__()
       __A       	=			model.transformer.wte
       __A       	=			copy.deepcopy(model.transformer.wte.weight			)
       __A       	=			nn.Convad(self.embeddings.weight.size(1			)	,				256	,				3	,				padding=1			)
       __A       	=			nn.Sequential(nn.Linear(256	,				32			)	,				nn.Dropout(p=0.1			)	,				nn.Linear(32	,				32			)	,				nn.Linear(32	,				1			)			)






 def 	lowercase_						(			self  :List[str]	,				_A  :Any			)				->			List[str]:







       '''simple docstring'''



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






 @classmethod
 def 	lowercase_						(			cls  :List[Any]	,				_A  :Tuple	,				_A  :str			)				->			Optional[int]:







       '''simple docstring'''



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








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a__		:   Optional[Any]            =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Optional[int]            =       [
     "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MobileViTV2ForImageClassification",
     "MobileViTV2ForSemanticSegmentation",
     "MobileViTV2Model",
     "MobileViTV2PreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 a__		:   List[str]            =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






