

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


lowerCamelCase     =      logging.getLogger(__name__)




def     UpperCAmelCase__       (    _A   :				Optional[int]							):





  '''simple docstring'''







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




def     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				List[Any]			,   _A   :				Union[str, Any]							):





  '''simple docstring'''







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




def     UpperCAmelCase__       (    _A   :				Union[str, Any]="gpt2"							):





  '''simple docstring'''







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




def     UpperCAmelCase__       (    _A   :				List[Any]			,   _A   :				Tuple			,   _A   :				Optional[Any]							):





  '''simple docstring'''







  a__						=copy.deepcopy(_A							)
  model.to(_A							)

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




def     UpperCAmelCase__       (    _A   :				Tuple			,   _A   :				int			,   _A   :				Dict			,   _A   :				Union[str, Any]							):





  '''simple docstring'''







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




def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				int			,   _A   :				str			,   _A   :				Optional[int]			,   _A   :				List[str]			,   _A   :				Optional[int]			,   _A   :				List[str]			,   _A   :				Any="dev.jbl"			,   _A   :				Any=recopy_gpta			,   ):





  '''simple docstring'''







  a__						=[]
  a__						=[]
  a__						=[]

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

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

  for step in tqdm(range(_A							)							):
    a__						=torch.zeros((1, context_len)			,   dtype=torch.long			,   device=_A							)
    a__						=random.choice(_A							)
    a__						=random.randint(0			,   len(story[0]							) - context_len - 1							)
    a__						=story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__						=model(_A			,   labels=_A							)
    a__						=outputs[0]
    a__						=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
    a__						=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
    a__,   a__,   a__						=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     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"			,   _A   :				int=1_00			,   _A   :				int=10_26			,   _A   :				Tuple=True							):





  '''simple docstring'''







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

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




def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[Any]			,   _A   :				Optional[int]			,   _A   :				Optional[Any]			,   _A   :				Union[str, Any]=50			,   _A   :				Dict="secondary_learner.pt"							):





  '''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_12]
  a__						=train_dataset[5_12:]
  a__						=DataLoader(_A			,   shuffle=_A			,   batch_size=_A							)
  a__						=DataLoader(_A			,   shuffle=_A			,   batch_size=_A							)

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

      secondary_learner.train()
  return secondary_learner







class 		__magic_name__    (							nn.Module							):

   '''simple docstring'''




   def __init__(       self,					lowercase_ )							->					Union[str, Any]:






     """simple docstring"""






     super(lowercase_,					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 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[Any]:






     """simple docstring"""






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



   @classmethod
   def 				_UpperCAmelCase				(       cls,					lowercase_,					lowercase_ )							->					Optional[Any]:






     """simple docstring"""






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



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xglm import XGLMTokenizer
else:
       lowerCamelCase     =      None


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase     =      {
    '''facebook/xglm-564M''': 2048,
}







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
   lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
   lowerCamelCase__				:			Any												=     XGLMTokenizer



   def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:






     """simple docstring"""






     a__						=7
     a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

     a__						=kwargs.get('''additional_special_tokens''',					[] )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)

     a__						=vocab_file
     a__						=False if not self.vocab_file else True



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     a__						=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     a__						=[self.sep_token_id]

     if token_ids_a is None:
       return len(sep + token_ids_a ) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:






     """simple docstring"""






     if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.''' )

     if not os.path.isdir(lowercase_ ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
       return
     a__						=os.path.join(
         lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
       copyfile(self.vocab_file,					lowercase_ )

     return (out_vocab_file,)
