

from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.ctrl.modeling_tf_ctrl import (
          TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCTRLForSequenceClassification,
          TFCTRLLMHeadModel,
          TFCTRLModel,
      )


class     lowerCamelCase_   ( _lowerCAmelCase):




 def __init__( self     ,     _UpperCAmelCase     ,     ):
   __UpperCAmelCase  :     Optional[Any]		=    parent
   __UpperCAmelCase  :     str		=    13
   __UpperCAmelCase  :     Optional[Any]		=    7
   __UpperCAmelCase  :     Optional[int]		=    True
   __UpperCAmelCase  :     List[str]		=    True
   __UpperCAmelCase  :     Optional[int]		=    True
   __UpperCAmelCase  :     Tuple		=    True
   __UpperCAmelCase  :     Any		=    True
   __UpperCAmelCase  :     Tuple		=    99
   __UpperCAmelCase  :     List[str]		=    32
   __UpperCAmelCase  :     Dict		=    2
   __UpperCAmelCase  :     List[Any]		=    4
   __UpperCAmelCase  :     Tuple		=    37
   __UpperCAmelCase  :     Tuple		=    'gelu'
   __UpperCAmelCase  :     Tuple		=    0.1
   __UpperCAmelCase  :     List[Any]		=    0.1
   __UpperCAmelCase  :     int		=    512
   __UpperCAmelCase  :     Any		=    16
   __UpperCAmelCase  :     Dict		=    2
   __UpperCAmelCase  :     Optional[int]		=    0.0_2
   __UpperCAmelCase  :     Dict		=    3
   __UpperCAmelCase  :     List[str]		=    4
   __UpperCAmelCase  :     Optional[int]		=    None
   __UpperCAmelCase  :     Optional[int]		=    self.vocab_size - 1




 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size       )

   __UpperCAmelCase  :     Optional[Any]		=    None
   if self.use_input_mask:
     __UpperCAmelCase  :     Union[str, Any]		=    random_attention_mask([self.batch_size, self.seq_length]       )

   __UpperCAmelCase  :     Dict		=    None
   if self.use_token_type_ids:
     __UpperCAmelCase  :     Dict		=    ids_tensor([self.batch_size, self.seq_length]     ,     self.type_vocab_size       )

   __UpperCAmelCase  :     Tuple		=    None
   if self.use_mc_token_ids:
     __UpperCAmelCase  :     Union[str, Any]		=    ids_tensor([self.batch_size, self.num_choices]     ,     self.seq_length       )

   __UpperCAmelCase  :     Union[str, Any]		=    None
   __UpperCAmelCase  :     Tuple		=    None
   __UpperCAmelCase  :     Any		=    None
   if self.use_labels:
     __UpperCAmelCase  :     List[str]		=    ids_tensor([self.batch_size]     ,     self.type_sequence_label_size       )
     __UpperCAmelCase  :     Optional[Any]		=    ids_tensor([self.batch_size, self.seq_length]     ,     self.num_labels       )
     __UpperCAmelCase  :     List[Any]		=    ids_tensor([self.batch_size]     ,     self.num_choices       )

   __UpperCAmelCase  :     int		=    CTRLConfig(
       vocab_size=self.vocab_size     ,     n_embd=self.hidden_size     ,     n_layer=self.num_hidden_layers     ,     n_head=self.num_attention_heads     ,     n_positions=self.max_position_embeddings     ,     pad_token_id=self.pad_token_id     ,     )

   __UpperCAmelCase  :     int		=    ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,     2       )

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
       token_type_ids,
       mc_token_ids,
       sequence_labels,
       token_labels,
       choice_labels,
   )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     *_UpperCAmelCase       ):
   __UpperCAmelCase  :     Any		=    TFCTRLModel(config=_UpperCAmelCase       )
   __UpperCAmelCase  :     Union[str, Any]		=    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
   __UpperCAmelCase  :     Dict		=    model(_UpperCAmelCase       )

   __UpperCAmelCase  :     int		=    [input_ids, None, input_mask]  # None is the input for 'past'
   __UpperCAmelCase  :     List[str]		=    model(_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    model(_UpperCAmelCase       )

   self.parent.assertEqual(result.last_hidden_state.shape     ,     (self.batch_size, self.seq_length, self.hidden_size)       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     *_UpperCAmelCase       ):
   __UpperCAmelCase  :     str		=    TFCTRLLMHeadModel(config=_UpperCAmelCase       )
   __UpperCAmelCase  :     Dict		=    {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
   __UpperCAmelCase  :     Optional[Any]		=    model(_UpperCAmelCase       )
   self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.seq_length, self.vocab_size)       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     *_UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    self.num_labels
   __UpperCAmelCase  :     List[Any]		=    ids_tensor([self.batch_size]     ,     self.type_sequence_label_size       )
   __UpperCAmelCase  :     List[Any]		=    {
       'input_ids': input_ids,
       'token_type_ids': token_type_ids,
       'labels': sequence_labels,
   }
   __UpperCAmelCase  :     Tuple		=    TFCTRLForSequenceClassification(_UpperCAmelCase       )
   __UpperCAmelCase  :     str		=    model(_UpperCAmelCase       )
   self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)       )




 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    self.prepare_config_and_inputs()

   (
       (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), (
       __UpperCAmelCase
   ), 
   )  :     Any		=    config_and_inputs

   __UpperCAmelCase  :     int		=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
   return config, inputs_dict



@require_tf
class     lowerCamelCase_   ( _lowerCAmelCase  ,							_lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
 _lowerCAmelCase       		=  (TFCTRLLMHeadModel,) if is_tf_available() else ()
 _lowerCAmelCase       		=  (
     {
         '''feature-extraction''': TFCTRLModel,
         '''text-classification''': TFCTRLForSequenceClassification,
         '''text-generation''': TFCTRLLMHeadModel,
         '''zero-shot''': TFCTRLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
     # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
     # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
     # config could not be created.
     return True

   return False




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    TFCTRLModelTester(self       )
   __UpperCAmelCase  :     Optional[Any]		=    ConfigTester(self     ,     config_class=_UpperCAmelCase     ,     n_embd=37       )




 def 						a					( self       ):
   self.config_tester.run_common_tests()




 def 						a					( self       ):
   __UpperCAmelCase  :     List[str]		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_model(*_UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_lm_head(*_UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     int		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_for_sequence_classification(*_UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase  :     Union[str, Any]		=    [TFCTRLLMHeadModel]
   __UpperCAmelCase  :     List[str]		=    [TFCTRLForSequenceClassification]

   for model_class in self.all_model_classes:
     __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )
     model.build()  # may be needed for the get_bias() call below
     assert isinstance(model.get_input_embeddings()     ,     tf.keras.layers.Layer       )

     if model_class in list_lm_models:
       __UpperCAmelCase  :     Optional[Any]		=    model.get_output_embeddings()
       assert isinstance(_UpperCAmelCase     ,     tf.keras.layers.Layer       )
       __UpperCAmelCase  :     str		=    model.get_bias()
       assert isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       )
       for k, v in name.items():
         assert isinstance(_UpperCAmelCase     ,     tf.Variable       )
     elif model_class in list_other_models_with_output_ebd:
       __UpperCAmelCase  :     List[Any]		=    model.get_output_embeddings()
       assert isinstance(_UpperCAmelCase     ,     tf.keras.layers.Layer       )
       __UpperCAmelCase  :     str		=    model.get_bias()
       assert name is None
     else:
       __UpperCAmelCase  :     List[str]		=    model.get_output_embeddings()
       assert x is None
       __UpperCAmelCase  :     Union[str, Any]		=    model.get_bias()
       assert name is None




 @slow
 def 						a					( self       ):
   for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase  :     List[str]		=    TFCTRLModel.from_pretrained(_UpperCAmelCase       )
     self.assertIsNotNone(_UpperCAmelCase       )



@require_tf
class     lowerCamelCase_   ( unittest.TestCase):




 @slow
 def 						a					( self       ):
   __UpperCAmelCase  :     List[Any]		=    TFCTRLLMHeadModel.from_pretrained('ctrl'       )
   __UpperCAmelCase  :     int		=    tf.convert_to_tensor([[11_859, 0, 1_611, 8]]     ,     dtype=tf.intaa       )  # Legal the president is
   __UpperCAmelCase  :     Optional[Any]		=    [
       11_859,
       0,
       1_611,
       8,
       5,
       150,
       26_449,
       2,
       19,
       348,
       469,
       3,
       2_595,
       48,
       20_740,
       246_533,
       246_533,
       19,
       30,
       5,
   ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

   __UpperCAmelCase  :     Optional[int]		=    model.generate(_UpperCAmelCase     ,     do_sample=_UpperCAmelCase       )
   self.assertListEqual(output_ids[0].numpy().tolist()     ,     _UpperCAmelCase       )








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    _snake_case		(snake_case_				:    Union[str, Any]     )       ->		str:
  random.seed(snake_case_     )
  np.random.seed(snake_case_     )
  torch.manual_seed(snake_case_     )
  torch.cuda.manual_seed_all(snake_case_     )





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





def    _snake_case		(snake_case_				:    Optional[int]="gpt2"     )       ->		Optional[Any]:
  __UpperCAmelCase  :     Dict		=    GPTaLMHeadModel.from_pretrained(snake_case_						,					output_hidden_states=snake_case_     )
  torch.save(model.state_dict()						,					model_name + 'local.pt'     )
  return model





def    _snake_case		(snake_case_				:    Any						,					snake_case_				:    Optional[int]						,					snake_case_				:    List[str]     )       ->		Dict:
  __UpperCAmelCase  :     List[str]		=    copy.deepcopy(snake_case_     )
  model.to(snake_case_     )

  __UpperCAmelCase  :     Optional[int]		=    ['bias', 'LayerNorm.weight']
  __UpperCAmelCase  :     int		=    [
      {
          '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  :     List[Any]		=    AdamW(snake_case_						,					lr=5e-5						,					eps=1e-8     )
  __UpperCAmelCase  :     int		=    get_linear_schedule_with_warmup(snake_case_						,					0						,					snake_case_     )
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler





def    _snake_case		(snake_case_				:    List[str]						,					snake_case_				:    List[str]						,					snake_case_				:    Union[str, Any]						,					snake_case_				:    Tuple     )       ->		Tuple:
  __UpperCAmelCase  :     Optional[Any]		=    np.array(snake_case_     ).mean()
  __UpperCAmelCase  :     str		=    np.array(snake_case_     ).std()
  __UpperCAmelCase  :     List[str]		=    (real_perps - avg) / std
  __UpperCAmelCase  :     List[str]		=    list(zip(snake_case_						,					snake_case_						,					snake_case_     )     )
  joblib.dump(snake_case_						,					snake_case_     )





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    Optional[Any]						,					snake_case_				:    Optional[int]						,					snake_case_				:    List[str]						,					snake_case_				:    Any						,					snake_case_				:    Dict						,					snake_case_				:    int						,					snake_case_				:    Optional[int]="dev.jbl"						,					snake_case_				:    Tuple=recopy_gpta						,					)       ->		Dict:
  __UpperCAmelCase  :     Dict		=    []
  __UpperCAmelCase  :     int		=    []
  __UpperCAmelCase  :     Optional[Any]		=    []

  # Initialize the transformer model
  __UpperCAmelCase  :     Tuple		=    copy.deepcopy(snake_case_     )
  orig_model.to(device='cpu'     )
  torch.cuda.empty_cache()

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

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

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

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

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

  intermittent_save(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    int="data/tokenized_stories_train_wikitext103.jbl"						,					snake_case_				:    Tuple=100						,					snake_case_				:    str=1026						,					snake_case_				:    Union[str, Any]=True     )       ->		List[str]:
  __UpperCAmelCase  :     Union[str, Any]		=    joblib.load(snake_case_     )
  print('data loaded'     )
  __UpperCAmelCase  :     Any		=    []
  if trim:
    for i, example in enumerate(snake_case_     ):
      if len(example[0]     ) > min_len:
        __UpperCAmelCase  :     Optional[int]		=    random.randint(0						,					len(example[0]     ) - context_len - 1     )
        objective_set.append(example[0, start : start + context_len]     )
      if len(snake_case_     ) >= number:
        break
    __UpperCAmelCase  :     Union[str, Any]		=    []
    for j in range(i + 1						,					len(snake_case_     )     ):
      if len(data[j][0]     ) > min_len:
        train_data.append(data[j]     )
  else:
    __UpperCAmelCase  :     Any		=    data[0:number]
    __UpperCAmelCase  :     int		=    data[number:]

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





def    _snake_case		(snake_case_				:    Optional[Any]						,					snake_case_				:    Tuple						,					snake_case_				:    Optional[Any]						,					snake_case_				:    str						,					snake_case_				:    Dict=50						,					snake_case_				:    Any="secondary_learner.pt"     )       ->		Tuple:
  __UpperCAmelCase  :     int		=    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  :     int		=    train_dataset[:512]
  __UpperCAmelCase  :     Tuple		=    train_dataset[512:]
  __UpperCAmelCase  :     Any		=    DataLoader(snake_case_						,					shuffle=snake_case_						,					batch_size=snake_case_     )
  __UpperCAmelCase  :     str		=    DataLoader(snake_case_						,					shuffle=snake_case_						,					batch_size=snake_case_     )

  # secondary learner model set up
  __UpperCAmelCase  :     Any		=    nn.MSELoss()
  __UpperCAmelCase  :     Any		=    nn.MSELoss(reduction='sum'     )
  secondary_learner.to(snake_case_     )
  __UpperCAmelCase  :     Union[str, Any]		=    torch.optim.Adam(secondary_learner.parameters()						,					lr=0.00001     )
  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  :     List[Any]		=    float('inf'     )
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(snake_case_     )     ):
    __UpperCAmelCase  :     int		=    0.0
    secondary_learner.train()
    for step, batch in enumerate(snake_case_     ):
      __UpperCAmelCase  :     Any		=    batch[0].to(snake_case_     )
      __UpperCAmelCase  :     Union[str, Any]		=    batch[1].to(snake_case_     )
      __UpperCAmelCase  :     List[Any]		=    secondary_learner(snake_case_     )
      q_optimizer.zero_grad()
      __UpperCAmelCase  :     Union[str, Any]		=    loss(snake_case_						,					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(snake_case_     )):
        __UpperCAmelCase  :     Optional[int]		=    tr_q_loss / (step + 1)

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

      secondary_learner.train()
  return secondary_learner







class     lowerCamelCase_   ( nn.Module):




 def __init__( self     ,     _UpperCAmelCase       ):
   super(_UpperCAmelCase     ,     self       ).__init__()
   __UpperCAmelCase  :     List[str]		=    model.transformer.wte
   __UpperCAmelCase  :     Optional[int]		=    copy.deepcopy(model.transformer.wte.weight       )
   __UpperCAmelCase  :     str		=    nn.Convad(self.embeddings.weight.size(1       )     ,     256     ,     3     ,     padding=1       )
   __UpperCAmelCase  :     Dict		=    nn.Sequential(nn.Linear(256     ,     32       )     ,     nn.Dropout(p=0.1       )     ,     nn.Linear(32     ,     32       )     ,     nn.Linear(32     ,     1       )       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    torch.max(self.conv(self.embeddings(_UpperCAmelCase       ).squeeze(1       ).transpose(1     ,     2       )       )     ,     2       )[0]
   __UpperCAmelCase  :     Dict		=    self.fc(_UpperCAmelCase       )
   return qs.squeeze(1       )




 @classmethod
 def 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     List[Any]		=    cls(_UpperCAmelCase       )  # this calls __init__
   __UpperCAmelCase  :     int		=    torch.load(_UpperCAmelCase       )
   secondary_learner.load_state_dict(_UpperCAmelCase       )
   __UpperCAmelCase  :     List[Any]		=    model.transformer.wte
   __UpperCAmelCase  :     List[Any]		=    copy.deepcopy(model.transformer.wte.weight       )
   return secondary_learner





