


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, 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.openai.modeling_tf_openai import (
       TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFOpenAIGPTDoubleHeadsModel,
       TFOpenAIGPTForSequenceClassification,
       TFOpenAIGPTLMHeadModel,
       TFOpenAIGPTModel,
   )


class       __lowerCAmelCase       :






  """simple docstring"""





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




     """simple docstring"""
     snake_case_         =   parent
     snake_case_         =   1_3
     snake_case_         =   7
     snake_case_         =   True
     snake_case_         =   True
     snake_case_         =   True
     snake_case_         =   True
     snake_case_         =   True
     snake_case_         =   9_9
     snake_case_         =   3_2
     snake_case_         =   2
     snake_case_         =   4
     snake_case_         =   3_7
     snake_case_         =   "gelu"
     snake_case_         =   0.1
     snake_case_         =   0.1
     snake_case_         =   5_1_2
     snake_case_         =   1_6
     snake_case_         =   2
     snake_case_         =   0.02
     snake_case_         =   3
     snake_case_         =   4
     snake_case_         =   None
     snake_case_         =   self.vocab_size - 1



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




     """simple docstring"""
     snake_case_         =   ids_tensor([self.batch_size, self.seq_length]      ,			self.vocab_size					)

     snake_case_         =   None
     if self.use_input_mask:
        snake_case_         =   random_attention_mask([self.batch_size, self.seq_length]					)

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

     snake_case_         =   None
     if self.use_mc_token_ids:
        snake_case_         =   ids_tensor([self.batch_size, self.num_choices]      ,			self.seq_length					)

     snake_case_         =   None
     snake_case_         =   None
     snake_case_         =   None
     if self.use_labels:
        snake_case_         =   ids_tensor([self.batch_size]      ,			self.type_sequence_label_size					)
        snake_case_         =   ids_tensor([self.batch_size, self.seq_length]      ,			self.num_labels					)
        snake_case_         =   ids_tensor([self.batch_size]      ,			self.num_choices					)

     snake_case_         =   OpenAIGPTConfig(
         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      ,			)

     snake_case_         =   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    lowerCAmelCase__					(       self    :    str      ,			_lowerCAmelCase    :    Optional[Any]      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    Tuple      ,			_lowerCAmelCase    :    int      ,			*_lowerCAmelCase    :    int					)				->	Optional[int]:




     """simple docstring"""
     snake_case_         =   TFOpenAIGPTModel(config=_lowerCAmelCase					)
     snake_case_         =   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     snake_case_         =   model(_lowerCAmelCase					)

     snake_case_         =   [input_ids, input_mask]
     snake_case_         =   model(_lowerCAmelCase					)

     snake_case_         =   model(_lowerCAmelCase					)

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



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




     """simple docstring"""
     snake_case_         =   TFOpenAIGPTLMHeadModel(config=_lowerCAmelCase					)
     snake_case_         =   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
     snake_case_         =   model(_lowerCAmelCase					)
     self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.seq_length, self.vocab_size)					)



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




     """simple docstring"""
     snake_case_         =   TFOpenAIGPTDoubleHeadsModel(config=_lowerCAmelCase					)

     snake_case_         =   tf.tile(tf.expand_dims(_lowerCAmelCase      ,			1					)      ,			(1, self.num_choices, 1)					)
     snake_case_         =   tf.tile(tf.expand_dims(_lowerCAmelCase      ,			1					)      ,			(1, self.num_choices, 1)					)
     snake_case_         =   tf.tile(tf.expand_dims(_lowerCAmelCase      ,			1					)      ,			(1, self.num_choices, 1)					)

     snake_case_         =   {
         "input_ids": multiple_choice_inputs_ids,
         "mc_token_ids": mc_token_ids,
         "attention_mask": multiple_choice_input_mask,
         "token_type_ids": multiple_choice_token_type_ids,
     }
     snake_case_         =   model(_lowerCAmelCase					)
     self.parent.assertEqual(
         result.logits.shape      ,			(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)					)
     self.parent.assertEqual(result.mc_logits.shape      ,			(self.batch_size, self.num_choices)					)



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




     """simple docstring"""
     snake_case_         =   self.num_labels
     snake_case_         =   ids_tensor([self.batch_size]      ,			self.type_sequence_label_size					)
     snake_case_         =   {
         "input_ids": input_ids,
         "attention_mask": input_mask,
         "token_type_ids": token_type_ids,
         "labels": sequence_labels,
     }
     snake_case_         =   TFOpenAIGPTForSequenceClassification(_lowerCAmelCase					)
     snake_case_         =   model(_lowerCAmelCase					)
     self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.num_labels)					)



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




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

     (
         (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      (
         snake_case_
     )      ,      
     )         =   config_and_inputs

     snake_case_         =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






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






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    (
      (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
      if is_tf_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE						      =    (
      (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
  )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  _SCREAMING_SNAKE_CASE						      =    (
      {
          'feature-extraction': TFOpenAIGPTModel,
          'text-classification': TFOpenAIGPTForSequenceClassification,
          'text-generation': TFOpenAIGPTLMHeadModel,
          'zero-shot': TFOpenAIGPTForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE						      =    False
  _SCREAMING_SNAKE_CASE						      =    False



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




     """simple docstring"""
     if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
        # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
        # `OpenAIGPTConfig` 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    lowerCAmelCase__					(       self    :    Any					)				->	List[str]:




     """simple docstring"""
     snake_case_         =   TFOpenAIGPTModelTester(self					)
     snake_case_         =   ConfigTester(self      ,			config_class=_lowerCAmelCase      ,			n_embd=3_7					)



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




     """simple docstring"""
     self.config_tester.run_common_tests()



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




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_model(*_lowerCAmelCase					)



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




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_lm_head(*_lowerCAmelCase					)



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




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_double_head(*_lowerCAmelCase					)



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




     """simple docstring"""
     snake_case_         =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowerCAmelCase					)



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




     """simple docstring"""
     for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        snake_case_         =   TFOpenAIGPTModel.from_pretrained(_lowerCAmelCase					)
        self.assertIsNotNone(_lowerCAmelCase					)






@require_tf
class       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





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




     """simple docstring"""
     snake_case_         =   TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"					)
     snake_case_         =   tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]      ,			dtype=tf.intaa					)  # the president is
     snake_case_         =   [
         4_8_1,
         4_7_3_5,
         5_4_4,
         2_4_6,
         9_6_3,
         8_7_0,
         7_6_2,
         2_3_9,
         2_4_4,
         4_0_4_7_7,
         2_4_4,
         2_4_9,
         7_1_9,
         8_8_1,
         4_8_7,
         5_4_4,
         2_4_0,
         2_4_4,
         6_0_3,
         4_8_1,
     ]  # the president is a very good man. " \n " i\'m sure he is, " said the

     snake_case_         =   model.generate(_lowerCAmelCase      ,			do_sample=_lowerCAmelCase					)
     self.assertListEqual(output_ids[0].numpy().tolist()      ,			_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




