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   		=			logging.getLogger(__name__)





def  lowercase(  UpperCamelCase_					)		->  Tuple:







 '''simple docstring'''
 random.seed(UpperCamelCase_					)
 np.random.seed(UpperCamelCase_					)
 torch.manual_seed(UpperCamelCase_					)
 torch.cuda.manual_seed_all(UpperCamelCase_					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[Any]:







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





def  lowercase(  UpperCamelCase_="gpt2"					)		->  Optional[int]:







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





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  Union[str, Any]:







 '''simple docstring'''
 UpperCamelCase      =			copy.deepcopy(UpperCamelCase_					)
 model.to(UpperCamelCase_					)

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





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase      =			np.array(UpperCamelCase_					).mean()
 UpperCamelCase      =			np.array(UpperCamelCase_					).std()
 UpperCamelCase      =			(real_perps - avg) / std
 UpperCamelCase      =			list(zip(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)					)
 joblib.dump(UpperCamelCase_       ,				UpperCamelCase_					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_="dev.jbl"       ,				UpperCamelCase_=recopy_gpta       ,				)		->  Tuple:







 '''simple docstring'''
 UpperCamelCase      =			[]
 UpperCamelCase      =			[]
 UpperCamelCase      =			[]

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

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

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

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

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase      =			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  lowercase(  UpperCamelCase_       ,				UpperCamelCase_="data/tokenized_stories_train_wikitext103.jbl"       ,				UpperCamelCase_=100       ,				UpperCamelCase_=1026       ,				UpperCamelCase_=True					)		->  int:







 '''simple docstring'''
 UpperCamelCase      =			joblib.load(UpperCamelCase_					)
 print("""data loaded"""					)
 UpperCamelCase      =			[]
 if trim:
  for i, example in enumerate(UpperCamelCase_					):
   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(UpperCamelCase_					) >= number:
    break
  UpperCamelCase      =			[]
  for j in range(i + 1       ,				len(UpperCamelCase_					)					):
   if len(data[j][0]					) > min_len:
    train_data.append(data[j]					)
 else:
  UpperCamelCase      =			data[0:number]
  UpperCamelCase      =			data[number:]

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





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=50       ,				UpperCamelCase_="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[:512]
 UpperCamelCase      =			train_dataset[512:]
 UpperCamelCase      =			DataLoader(UpperCamelCase_       ,				shuffle=UpperCamelCase_       ,				batch_size=UpperCamelCase_					)
 UpperCamelCase      =			DataLoader(UpperCamelCase_       ,				shuffle=UpperCamelCase_       ,				batch_size=UpperCamelCase_					)

 # secondary learner model set up
 UpperCamelCase      =			nn.MSELoss()
 UpperCamelCase      =			nn.MSELoss(reduction="""sum"""					)
 secondary_learner.to(UpperCamelCase_					)
 UpperCamelCase      =			torch.optim.Adam(secondary_learner.parameters()       ,				lr=0.0_0_0_0_1					)
 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(UpperCamelCase_					)					):
  UpperCamelCase      =			0.0
  secondary_learner.train()
  for step, batch in enumerate(UpperCamelCase_					):
   UpperCamelCase      =			batch[0].to(UpperCamelCase_					)
   UpperCamelCase      =			batch[1].to(UpperCamelCase_					)
   UpperCamelCase      =			secondary_learner(UpperCamelCase_					)
   q_optimizer.zero_grad()
   UpperCamelCase      =			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_					)):
    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(UpperCamelCase_					):
     UpperCamelCase      =			batcha[0].to(UpperCamelCase_					)
     UpperCamelCase      =			batcha[1].to(UpperCamelCase_					)
     UpperCamelCase      =			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_					)
     UpperCamelCase      =			q_lossa

   secondary_learner.train()
 return secondary_learner


class SCREAMING_SNAKE_CASE_		(			nn.Module		):







   def __init__(    self	:	int      ,      lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    super(lowerCamelCase_      ,      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 	lowerCamelCase_  (    self	:	int      ,      lowerCamelCase_	:	str							):





    """simple docstring"""


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







   @classmethod
   def 	lowerCamelCase_  (    cls	:	Any      ,      lowerCamelCase_	:	Any      ,      lowerCamelCase_	:	Optional[int]							):





    """simple docstring"""


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

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
