


import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class       __lowerCAmelCase       (	a	,					unittest.TestCase   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    RealmTokenizer
  _SCREAMING_SNAKE_CASE						      =    RealmTokenizerFast
  _SCREAMING_SNAKE_CASE						      =    True
  _SCREAMING_SNAKE_CASE						      =    True
  _SCREAMING_SNAKE_CASE						      =    filter_non_english



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Optional[int]:




     """simple docstring"""
     super().setUp()

     snake_case_         =   [
         "[UNK]",
         "[CLS]",
         "[SEP]",
         "[PAD]",
         "[MASK]",
         "want",
         "##want",
         "##ed",
         "wa",
         "un",
         "runn",
         "##ing",
         ",",
         "low",
         "lowest",
     ]
     snake_case_         =   os.path.join(self.tmpdirname      ,			VOCAB_FILES_NAMES["vocab_file"]					)
     with open(self.vocab_file      ,			"w"      ,			encoding="utf-8"					) as vocab_writer:
        vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)



  def    lowerCAmelCase__					(       self    :    List[Any]      ,			_lowerCAmelCase    :    Tuple					)				->	str:




     """simple docstring"""
     snake_case_         =   "UNwant\u00E9d,running"
     snake_case_         =   "unwanted, running"
     return input_text, output_text



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   self.tokenizer_class(self.vocab_file					)

     snake_case_         =   tokenizer.tokenize("UNwant\u00E9d,running"					)
     self.assertListEqual(_lowerCAmelCase      ,			["un", "##want", "##ed", ",", "runn", "##ing"]					)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase					)      ,			[9, 6, 7, 1_2, 1_0, 1_1]					)



  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Union[str, Any]:




     """simple docstring"""
     if not self.test_rust_tokenizer:
        return

     snake_case_         =   self.get_tokenizer()
     snake_case_         =   self.get_rust_tokenizer()

     snake_case_         =   "UNwant\u00E9d,running"

     snake_case_         =   tokenizer.tokenize(_lowerCAmelCase					)
     snake_case_         =   rust_tokenizer.tokenize(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     snake_case_         =   tokenizer.encode(_lowerCAmelCase      ,			add_special_tokens=_lowerCAmelCase					)
     snake_case_         =   rust_tokenizer.encode(_lowerCAmelCase      ,			add_special_tokens=_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     snake_case_         =   self.get_rust_tokenizer()
     snake_case_         =   tokenizer.encode(_lowerCAmelCase					)
     snake_case_         =   rust_tokenizer.encode(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     # With lower casing
     snake_case_         =   self.get_tokenizer(do_lower_case=_lowerCAmelCase					)
     snake_case_         =   self.get_rust_tokenizer(do_lower_case=_lowerCAmelCase					)

     snake_case_         =   "UNwant\u00E9d,running"

     snake_case_         =   tokenizer.tokenize(_lowerCAmelCase					)
     snake_case_         =   rust_tokenizer.tokenize(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     snake_case_         =   tokenizer.encode(_lowerCAmelCase      ,			add_special_tokens=_lowerCAmelCase					)
     snake_case_         =   rust_tokenizer.encode(_lowerCAmelCase      ,			add_special_tokens=_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     snake_case_         =   self.get_rust_tokenizer()
     snake_case_         =   tokenizer.encode(_lowerCAmelCase					)
     snake_case_         =   rust_tokenizer.encode(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Any					)				->	int:




     """simple docstring"""
     snake_case_         =   BasicTokenizer()

     self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"					)      ,			["ah", "\u535A", "\u63A8", "zz"]					)



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




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "					)      ,			["hello", "!", "how", "are", "you", "?"]					)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,			["hello"]					)



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Dict:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase      ,			strip_accents=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,			["hällo", "!", "how", "are", "you", "?"]					)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,			["h\u00E9llo"]					)



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Union[str, Any]:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase      ,			strip_accents=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,			["hallo", "!", "how", "are", "you", "?"]					)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,			["hello"]					)



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,			["hallo", "!", "how", "are", "you", "?"]					)
     self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,			["hello"]					)



  def    lowerCAmelCase__					(       self    :    str					)				->	Dict:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "					)      ,			["HeLLo", "!", "how", "Are", "yoU", "?"]					)



  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase      ,			strip_accents=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,			["HäLLo", "!", "how", "Are", "yoU", "?"]					)



  def    lowerCAmelCase__					(       self    :    str					)				->	Optional[Any]:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase      ,			strip_accents=_lowerCAmelCase					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,			["HaLLo", "!", "how", "Are", "yoU", "?"]					)



  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Dict:




     """simple docstring"""
     snake_case_         =   BasicTokenizer(do_lower_case=_lowerCAmelCase      ,			never_split=["[UNK]"]					)

     self.assertListEqual(
         tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"					)      ,			["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]					)



  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	Union[str, Any]:




     """simple docstring"""
     snake_case_         =   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

     snake_case_         =   {}
     for i, token in enumerate(_lowerCAmelCase					):
        snake_case_         =   i
     snake_case_         =   WordpieceTokenizer(vocab=_lowerCAmelCase      ,			unk_token="[UNK]"					)

     self.assertListEqual(tokenizer.tokenize(""					)      ,			[]					)

     self.assertListEqual(tokenizer.tokenize("unwanted running"					)      ,			["un", "##want", "##ed", "runn", "##ing"]					)

     self.assertListEqual(tokenizer.tokenize("unwantedX running"					)      ,			["[UNK]", "runn", "##ing"]					)



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Optional[Any]:




     """simple docstring"""
     self.assertTrue(_is_whitespace(" "					)					)
     self.assertTrue(_is_whitespace("\t"					)					)
     self.assertTrue(_is_whitespace("\r"					)					)
     self.assertTrue(_is_whitespace("\n"					)					)
     self.assertTrue(_is_whitespace("\u00A0"					)					)

     self.assertFalse(_is_whitespace("A"					)					)
     self.assertFalse(_is_whitespace("-"					)					)



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	str:




     """simple docstring"""
     self.assertTrue(_is_control("\u0005"					)					)

     self.assertFalse(_is_control("A"					)					)
     self.assertFalse(_is_control(" "					)					)
     self.assertFalse(_is_control("\t"					)					)
     self.assertFalse(_is_control("\r"					)					)



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Dict:




     """simple docstring"""
     self.assertTrue(_is_punctuation("-"					)					)
     self.assertTrue(_is_punctuation("$"					)					)
     self.assertTrue(_is_punctuation("`"					)					)
     self.assertTrue(_is_punctuation("."					)					)

     self.assertFalse(_is_punctuation("A"					)					)
     self.assertFalse(_is_punctuation(" "					)					)



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   self.get_tokenizer()

     # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
     self.assertListEqual([tokenizer.tokenize(_lowerCAmelCase					) for t in ["Test", "\xad", "test"]]      ,			[["[UNK]"], [], ["[UNK]"]]					)

     if self.test_rust_tokenizer:
        snake_case_         =   self.get_rust_tokenizer()
        self.assertListEqual(
            [rust_tokenizer.tokenize(_lowerCAmelCase					) for t in ["Test", "\xad", "test"]]      ,			[["[UNK]"], [], ["[UNK]"]]					)



  @slow
  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	Dict:




     """simple docstring"""
     snake_case_         =   self.tokenizer_class.from_pretrained("bert-base-uncased"					)

     snake_case_         =   tokenizer.encode("sequence builders"      ,			add_special_tokens=_lowerCAmelCase					)
     snake_case_         =   tokenizer.encode("multi-sequence build"      ,			add_special_tokens=_lowerCAmelCase					)

     snake_case_         =   tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase					)
     snake_case_         =   tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase      ,			_lowerCAmelCase					)

     assert encoded_sentence == [1_0_1] + text + [1_0_2]
     assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]



  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	List[Any]:




     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
           snake_case_         =   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase      ,			**_lowerCAmelCase					)

           snake_case_         =   F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
           snake_case_         =   tokenizer_r.encode_plus(
               _lowerCAmelCase      ,			return_attention_mask=_lowerCAmelCase      ,			return_token_type_ids=_lowerCAmelCase      ,			return_offsets_mapping=_lowerCAmelCase      ,			add_special_tokens=_lowerCAmelCase      ,			)

           snake_case_         =   tokenizer_r.do_lower_case if hasattr(_lowerCAmelCase      ,			"do_lower_case"					) else False
           snake_case_         =   (
               [
                   ((0, 0), tokenizer_r.cls_token),
                   ((0, 1), "A"),
                   ((1, 2), ","),
                   ((3, 5), "na"),
                   ((5, 6), "##ï"),
                   ((6, 8), "##ve"),
                   ((9, 1_5), tokenizer_r.mask_token),
                   ((1_6, 2_1), "Allen"),
                   ((2_1, 2_3), "##NL"),
                   ((2_3, 2_4), "##P"),
                   ((2_5, 3_3), "sentence"),
                   ((3_3, 3_4), "."),
                   ((0, 0), tokenizer_r.sep_token),
               ]
               if not do_lower_case
               else [
                   ((0, 0), tokenizer_r.cls_token),
                   ((0, 1), "a"),
                   ((1, 2), ","),
                   ((3, 8), "naive"),
                   ((9, 1_5), tokenizer_r.mask_token),
                   ((1_6, 2_1), "allen"),
                   ((2_1, 2_3), "##nl"),
                   ((2_3, 2_4), "##p"),
                   ((2_5, 3_3), "sentence"),
                   ((3_3, 3_4), "."),
                   ((0, 0), tokenizer_r.sep_token),
               ]
           )

           self.assertEqual(
               [e[1] for e in expected_results]      ,			tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]					)					)
           self.assertEqual([e[0] for e in expected_results]      ,			tokens["offset_mapping"]					)



  @slow
  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	Dict:




     """simple docstring"""
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
           snake_case_         =   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase      ,			**_lowerCAmelCase					)
           snake_case_         =   self.tokenizer_class.from_pretrained(_lowerCAmelCase      ,			**_lowerCAmelCase					)
           snake_case_         =   [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]

           snake_case_         =   tokenizer_r.batch_encode_candidates(_lowerCAmelCase      ,			max_length=1_0      ,			return_tensors="np"					)
           snake_case_         =   tokenizer_p.batch_encode_candidates(_lowerCAmelCase      ,			max_length=1_0      ,			return_tensors="np"					)

           snake_case_         =   (2, 2, 1_0)

           self.assertEqual(encoded_sentence_r["input_ids"].shape      ,			_lowerCAmelCase					)
           self.assertEqual(encoded_sentence_r["attention_mask"].shape      ,			_lowerCAmelCase					)
           self.assertEqual(encoded_sentence_r["token_type_ids"].shape      ,			_lowerCAmelCase					)

           self.assertEqual(encoded_sentence_p["input_ids"].shape      ,			_lowerCAmelCase					)
           self.assertEqual(encoded_sentence_p["attention_mask"].shape      ,			_lowerCAmelCase					)
           self.assertEqual(encoded_sentence_p["token_type_ids"].shape      ,			_lowerCAmelCase					)








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		:Dict											=       logging.getLogger(__name__)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->str:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Optional[Any]							)->Dict:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]="gpt2"							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   GPTaLMHeadModel.from_pretrained(lowerCAmelCase_						,  output_hidden_states=lowerCAmelCase_							)
   torch.save(model.state_dict()						,  model_name + "local.pt"							)
   return model



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[Any]							)->int:
   '''simple docstring'''






   snake_case_         =   copy.deepcopy(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:str							)->Tuple:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:str="dev.jbl"						,  lowerCAmelCase_							:int=recopy_gpta						,  )->Tuple:
   '''simple docstring'''






   snake_case_         =   []
   snake_case_         =   []
   snake_case_         =   []

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

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

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

      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_000 == 0 and step > 1:
         intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

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

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

   intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"						,  lowerCAmelCase_							:Tuple=100						,  lowerCAmelCase_							:int=1_026						,  lowerCAmelCase_							:int=True							)->List[Any]:
   '''simple docstring'''






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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Dict=50						,  lowerCAmelCase_							:Dict="secondary_learner.pt"							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   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
   snake_case_         =   train_dataset[:512]
   snake_case_         =   train_dataset[512:]
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)

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

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

         secondary_learner.train()
   return secondary_learner



class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Dict					)				->	List[Any]:




     """simple docstring"""
     super(_lowerCAmelCase      ,			self					).__init__()
     snake_case_         =   model.transformer.wte
     snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
     snake_case_         =   nn.Convad(self.embeddings.weight.size(1					)      ,			2_5_6      ,			3      ,			padding=1					)
     snake_case_         =   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    :    Union[str, Any]      ,			_lowerCAmelCase    :    Optional[int]					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   torch.max(self.conv(self.embeddings(_lowerCAmelCase					).squeeze(1					).transpose(1      ,			2					)					)      ,			2					)[0]
     snake_case_         =   self.fc(_lowerCAmelCase					)
     return qs.squeeze(1					)



  @classmethod
  def    lowerCAmelCase__					(       cls    :    List[Any]      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Dict					)				->	Dict:




     """simple docstring"""
     snake_case_         =   cls(_lowerCAmelCase					)  # this calls __init__
     snake_case_         =   torch.load(_lowerCAmelCase					)
     secondary_learner.load_state_dict(_lowerCAmelCase					)
     snake_case_         =   model.transformer.wte
     snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
     return secondary_learner




