'''simple docstring'''

import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          CpmAntConfig,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntTokenizer,
      )




@require_torch
class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=2					,      lowerCAmelCase__=8					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__=99					,      lowerCAmelCase__=32					,      lowerCAmelCase__=3					,      lowerCAmelCase__=4					,      lowerCAmelCase__=37					,      lowerCAmelCase__=32					,      lowerCAmelCase__=128					,      lowerCAmelCase__=8					,      lowerCAmelCase__=8					,      lowerCAmelCase__=8					,      lowerCAmelCase__=1.0					,      lowerCAmelCase__=True					,      )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Dict       =parent
        _UpperCamelCase  :Optional[int]       =batch_size
        _UpperCamelCase  :int       =seq_length
        _UpperCamelCase  :Union[str, Any]       =is_training
        _UpperCamelCase  :Dict       =use_token_type_ids
        _UpperCamelCase  :Any       =use_input_mask
        _UpperCamelCase  :int       =use_labels
        _UpperCamelCase  :Optional[Any]       =use_mc_token_ids
        _UpperCamelCase  :Any       =vocab_size
        _UpperCamelCase  :Dict       =hidden_size
        _UpperCamelCase  :List[str]       =num_hidden_layers
        _UpperCamelCase  :Tuple       =num_attention_heads
        _UpperCamelCase  :Dict       =intermediate_size
        _UpperCamelCase  :str       =num_buckets
        _UpperCamelCase  :Optional[Any]       =max_distance
        _UpperCamelCase  :str       =prompt_length
        _UpperCamelCase  :Optional[int]       =prompt_types
        _UpperCamelCase  :int       =segment_types
        _UpperCamelCase  :List[Any]       =init_std
        _UpperCamelCase  :Any       =return_dict



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       ={}
        _UpperCamelCase  :List[Any]       =ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     ).type(torch.intaa     )
        _UpperCamelCase  :Tuple       =False

        _UpperCamelCase  :Dict       =self.get_config()

        return (config, input_ids)



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        return CpmAntConfig(
            vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      dim_ff=self.intermediate_size					,      position_bias_num_buckets=self.num_buckets					,      position_bias_max_distance=self.max_distance					,      prompt_types=self.prompt_types					,      prompt_length=self.prompt_length					,      segment_types=self.segment_types					,      use_cache=lowerCAmelCase__					,      init_std=self.init_std					,      return_dict=self.return_dict					,      )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      *lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Dict       =CpmAntModel(config=lowerCAmelCase__     )
        model.to(lowerCAmelCase__     )
        model.eval()

        _UpperCamelCase  :List[str]       =model(**lowerCAmelCase__     ).last_hidden_state

        self.parent.assertEqual(hidden_states.shape					,      (self.batch_size, self.seq_length, config.hidden_size)     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      *lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =CpmAntForCausalLM(lowerCAmelCase__     )
        model.to(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =input_ids["""input_ids"""].to(lowerCAmelCase__     )
        model.eval()

        _UpperCamelCase  :Optional[int]       =model(**lowerCAmelCase__     )
        self.parent.assertEqual(
            model_output.logits.shape					,      (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)					,      )




   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.prepare_config_and_inputs()
        return config, inputs_dict




@require_torch
class    lowerCamelCase__  (   __snake_case       ,				__snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
   __UpperCAmelCase           =						(
       {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
   )

   __UpperCAmelCase           =						False
   __UpperCAmelCase           =						False
   __UpperCAmelCase           =						False
   __UpperCAmelCase           =						False
   __UpperCAmelCase           =						False



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =CpmAntModelTester(self     )
        _UpperCamelCase  :Dict       =ConfigTester(self					,      config_class=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        unittest.skip("""CPMAnt doesn't support input_embeds."""     )(self.test_inputs_embeds     )



   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        unittest.skip(
            """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."""     )(self.test_retain_grad_hidden_states_attentions     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(lowerCAmelCase__					,      lowerCAmelCase__     )




@require_torch
class    lowerCamelCase__  (   unittest.TestCase     ):



   @tooslow
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Dict       ="""今天天气真好！"""
        _UpperCamelCase  :Tuple       ="""openbmb/cpm-ant-10b"""
        _UpperCamelCase  :List[str]       =CpmAntModel.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =CpmAntTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =tokenizer(lowerCAmelCase__					,      return_tensors="""pt"""     )
        _UpperCamelCase  :Optional[Any]       =model(**lowerCAmelCase__     ).last_hidden_state

        _UpperCamelCase  :Optional[int]       =torch.tensor(
            [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]]					,      )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]					,      lowerCAmelCase__					,      atol=1e-2     )     )




@require_torch
class    lowerCamelCase__  (   unittest.TestCase     ):



   @tooslow
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       ="""今天天气真好！"""
        _UpperCamelCase  :int       ="""openbmb/cpm-ant-10b"""
        _UpperCamelCase  :Optional[Any]       =CpmAntForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =CpmAntTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =tokenizer(lowerCAmelCase__					,      return_tensors="""pt"""     )
        _UpperCamelCase  :Any       =model(**lowerCAmelCase__     ).logits

        _UpperCamelCase  :Optional[Any]       =torch.tensor(
            [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]]					,      )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]					,      lowerCAmelCase__					,      atol=1e-2     )     )



   @tooslow
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       ="""openbmb/cpm-ant-10b"""
        _UpperCamelCase  :str       =CpmAntForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :int       =CpmAntTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :str       ="""今天天气不错，"""
        _UpperCamelCase  :str       ="""今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
        _UpperCamelCase  :Dict       =tokenizer(lowerCAmelCase__					,      return_tensors="""pt"""     )
        _UpperCamelCase  :Dict       =model.generate(**lowerCAmelCase__     )
        _UpperCamelCase  :int       =tokenizer.batch_decode(lowerCAmelCase__     )
        self.assertEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   @tooslow
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       ="""openbmb/cpm-ant-10b"""
        _UpperCamelCase  :List[str]       =CpmAntForCausalLM.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =CpmAntTokenizer.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =["""今天天气不错，""", """新年快乐，万事如意！"""]
        _UpperCamelCase  :Optional[Any]       =[
            """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
            """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
        ]
        _UpperCamelCase  :Tuple       =tokenizer(lowerCAmelCase__					,      return_tensors="""pt"""					,      padding=lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =model.generate(**lowerCAmelCase__     )
        _UpperCamelCase  :str       =tokenizer.batch_decode(lowerCAmelCase__     )
        self.assertEqual(lowerCAmelCase__					,      lowerCAmelCase__     )
'''simple docstring'''

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 :				Any														= logging.getLogger(__name__)



def  _lowerCAmelCase				(				__a      )  ->      Tuple:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      Union[str, Any]:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a="gpt2"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :Tuple       =GPTaLMHeadModel.from_pretrained(__a			,      output_hidden_states=__a      )
     torch.save(model.state_dict()			,      model_name + """local.pt"""      )
     return model



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     model.to(__a      )

     _UpperCamelCase  :List[Any]       =["""bias""", """LayerNorm.weight"""]
     _UpperCamelCase  :Dict       =[
         {
             """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[str]       =AdamW(__a			,      lr=5e-5			,      eps=1e-8      )
     _UpperCamelCase  :List[Any]       =get_linear_schedule_with_warmup(__a			,      0			,      __a      )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =np.array(__a      ).mean()
     _UpperCamelCase  :Dict       =np.array(__a      ).std()
     _UpperCamelCase  :Tuple       =(real_perps - avg) / std
     _UpperCamelCase  :Optional[Any]       =list(zip(__a			,      __a			,      __a      )      )
     joblib.dump(__a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a="dev.jbl"			,      __a=recopy_gpta			,      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :List[Any]       =[]
     _UpperCamelCase  :List[str]       =[]
     _UpperCamelCase  :Dict       =[]

     # Initialize the transformer model
     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     orig_model.to(device="""cpu"""      )
     torch.cuda.empty_cache()

     # Compute perplexity of initial transformer model for comparison
     model.train()
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =recopy_model(__a			,      __a			,      __a      )

     for step in tqdm(range(__a      )      ):
          _UpperCamelCase  :List[Any]       =torch.zeros((1, context_len)			,      dtype=torch.long			,      device=__a      )
          _UpperCamelCase  :str       =random.choice(__a      )
          _UpperCamelCase  :List[Any]       =random.randint(0			,      len(story[0]      ) - context_len - 1      )
          _UpperCamelCase  :str       =story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          _UpperCamelCase  :Optional[Any]       =model(__a			,      labels=__a      )
          _UpperCamelCase  :Union[str, Any]       =outputs[0]
          _UpperCamelCase  :Optional[Any]       =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
          _UpperCamelCase  :str       =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
          _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =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  _lowerCAmelCase				(				__a			,      __a="data/tokenized_stories_train_wikitext103.jbl"			,      __a=1_00			,      __a=10_26			,      __a=True      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =joblib.load(__a      )
     print("""data loaded"""      )
     _UpperCamelCase  :List[str]       =[]
     if trim:
          for i, example in enumerate(__a      ):
               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(__a      ) >= number:
                    break
          _UpperCamelCase  :str       =[]
          for j in range(i + 1			,      len(__a      )      ):
               if len(data[j][0]      ) > min_len:
                    train_data.append(data[j]      )
     else:
          _UpperCamelCase  :int       =data[0:number]
          _UpperCamelCase  :Tuple       =data[number:]

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



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a=50			,      __a="secondary_learner.pt"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =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  :Any       =train_dataset[:5_12]
     _UpperCamelCase  :List[str]       =train_dataset[5_12:]
     _UpperCamelCase  :int       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )
     _UpperCamelCase  :Tuple       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )

     # secondary learner model set up
     _UpperCamelCase  :Any       =nn.MSELoss()
     _UpperCamelCase  :List[Any]       =nn.MSELoss(reduction="""sum"""      )
     secondary_learner.to(__a      )
     _UpperCamelCase  :int       =torch.optim.Adam(secondary_learner.parameters()			,      lr=0.0_00_01      )
     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  :Union[str, Any]       =float("""inf"""      )
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(__a      )      ):
          _UpperCamelCase  :int       =0.0
          secondary_learner.train()
          for step, batch in enumerate(__a      ):
               _UpperCamelCase  :List[Any]       =batch[0].to(__a      )
               _UpperCamelCase  :List[str]       =batch[1].to(__a      )
               _UpperCamelCase  :str       =secondary_learner(__a      )
               q_optimizer.zero_grad()
               _UpperCamelCase  :Tuple       =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      )):
                    _UpperCamelCase  :List[Any]       =tr_q_loss / (step + 1)

                    secondary_learner.eval()
                    _UpperCamelCase  :str       =0.0
                    _UpperCamelCase  :List[Any]       =0.0
                    _UpperCamelCase  :Optional[int]       =[]
                    _UpperCamelCase  :str       =[]
                    # Compute performance of the secondary learner after this batch
                    for stepa, batcha in enumerate(__a      ):
                         _UpperCamelCase  :str       =batcha[0].to(__a      )
                         _UpperCamelCase  :List[Any]       =batcha[1].to(__a      )
                         _UpperCamelCase  :Tuple       =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      )
                         _UpperCamelCase  :List[str]       =q_lossa

               secondary_learner.train()
     return secondary_learner




class    lowerCamelCase__  (   nn.Module     ):



   def __init__(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        super(lowerCAmelCase__					,      self     ).__init__()
        _UpperCamelCase  :Optional[Any]       =model.transformer.wte
        _UpperCamelCase  :Any       =copy.deepcopy(model.transformer.wte.weight     )
        _UpperCamelCase  :int       =nn.Convad(self.embeddings.weight.size(1     )					,      256					,      3					,      padding=1     )
        _UpperCamelCase  :int       =nn.Sequential(nn.Linear(256					,      32     )					,      nn.Dropout(p=0.1     )					,      nn.Linear(32					,      32     )					,      nn.Linear(32					,      1     )     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =torch.max(self.conv(self.embeddings(lowerCAmelCase__     ).squeeze(1     ).transpose(1					,      2     )     )					,      2     )[0]
        _UpperCamelCase  :List[Any]       =self.fc(lowerCAmelCase__     )
        return qs.squeeze(1     )




   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =cls(lowerCAmelCase__     )  # this calls __init__
        _UpperCamelCase  :Optional[Any]       =torch.load(lowerCAmelCase__     )
        secondary_learner.load_state_dict(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =model.transformer.wte
        _UpperCamelCase  :Tuple       =copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner