

import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
lowerCamelCase 	=   logging.getLogger(__name__)

lowerCamelCase 	=   int(1_0000)  # Hardcoded max length to avoid infinite loop

lowerCamelCase 	=   {
    '''gpt2''': (GPTaLMHeadModel, GPTaTokenizer),
    '''ctrl''': (CTRLLMHeadModel, CTRLTokenizer),
    '''openai-gpt''': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    '''xlnet''': (XLNetLMHeadModel, XLNetTokenizer),
    '''transfo-xl''': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    '''xlm''': (XLMWithLMHeadModel, XLMTokenizer),
    '''gptj''': (GPTJForCausalLM, AutoTokenizer),
    '''bloom''': (BloomForCausalLM, BloomTokenizerFast),
    '''llama''': (LlamaForCausalLM, LlamaTokenizer),
    '''opt''': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
lowerCamelCase 	=   '''In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>'''




def      UpperCAmelCase__							(     _A :					str				):





  '''simple docstring'''







  np.random.seed(args.seed				)
  torch.manual_seed(args.seed				)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed				)




def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Dict      , _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  if args.temperature > 0.7:
    logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''				)

  a__    =tokenizer.encode(_A      , add_special_tokens=_A				)
  if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()				):
    logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''				)
  return prompt_text




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Tuple      , _A :					List[str]      , _A :					Dict				):





  '''simple docstring'''







  a__    =hasattr(model.config      , '''use_lang_emb'''				) and model.config.use_lang_emb
  if hasattr(model.config      , '''lang2id'''				) and use_lang_emb:
    a__    =model.config.langaid.keys()
    if args.xlm_language in available_languages:
      a__    =args.xlm_language
    else:
      a__    =None
      while language not in available_languages:
        a__    =input('''Using XLM. Select language in ''' + str(list(_A				)				) + ''' >>> '''				)

    a__    =model.config.langaid[language]
    # kwargs["language"] = tokenizer.lang2id[language]

  # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
  # XLM masked-language modeling (MLM) models need masked token
  # is_xlm_mlm = "mlm" in args.model_name_or_path
  # if is_xlm_mlm:
  #     kwargs["mask_token_id"] = tokenizer.mask_token_id

  return prompt_text




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





  '''simple docstring'''







  a__    =args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  a__    =prefix + prompt_text
  return prompt_text




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





  '''simple docstring'''







  a__    =args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  a__    =prefix + prompt_text
  return prompt_text


lowerCamelCase 	=   {
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}




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





  '''simple docstring'''







  if length < 0 and max_sequence_length > 0:
    a__    =max_sequence_length
  elif 0 < max_sequence_length < length:
    a__    =max_sequence_length  # No generation bigger than model size
  elif length < 0:
    a__    =MAX_LENGTH  # avoid infinite loop
  return length




def      UpperCAmelCase__							(     _A :					Tuple				):





  '''simple docstring'''







  a__    =None
  if hasattr(_A      , '''hidden_size'''				):
    a__    =model_config.hidden_size
  elif hasattr(_A      , '''n_embed'''				):
    a__    =model_config.n_embed
  elif hasattr(_A      , '''n_embd'''				):
    a__    =model_config.n_embd

  a__    =None
  if hasattr(_A      , '''num_attention_heads'''				):
    a__    =model_config.num_attention_heads
  elif hasattr(_A      , '''n_head'''				):
    a__    =model_config.n_head

  if embedding_size is None or num_head is None or num_head == 0:
    raise ValueError('''Check the model config'''				)

  a__    =int(embedding_size / num_head				)
  if hasattr(_A      , '''n_layer'''				):
    a__    =model_config.n_layer
  elif hasattr(_A      , '''num_hidden_layers'''				):
    a__    =model_config.num_hidden_layers
  else:
    raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''				)

  return num_layer, num_head, num_embedding_size_per_head




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





  '''simple docstring'''







  a__, a__, a__    =sparse_model_config(model.config				)
  if model.config.model_type == "bloom":
    a__    =tuple(
        (
            torch.empty(int(num_attention_heads * batch_size				)      , _A      , _A				)
            .to(model.dtype				)
            .to(model.device				),
            torch.empty(int(num_attention_heads * batch_size				)      , _A      , _A				)
            .to(model.dtype				)
            .to(model.device				),
        )
        for _ in range(_A				)				)
  else:
    a__    =tuple(
        (
            torch.empty(_A      , _A      , _A      , _A				)
            .to(model.dtype				)
            .to(model.device				),
            torch.empty(_A      , _A      , _A      , _A				)
            .to(model.dtype				)
            .to(model.device				),
        )
        for _ in range(_A				)				)
  return past_key_values




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





  '''simple docstring'''







  a__    =len(_A				)
  a__    =tokenizer.batch_encode_plus(_A      , return_tensors='''pt'''				)
  a__    =dummy_input.to(model.device				)
  if model.config.use_cache:
    a__    =generate_past_key_values(_A      , _A      , 1				)
  a__    =torch.cat(
      [
          torch.zeros(dummy_input['''attention_mask'''].shape[0]      , 1				)
          .to(dummy_input['''attention_mask'''].dtype				)
          .to(model.device				),
          dummy_input['''attention_mask'''],
      ]      , -1      , )
  return dummy_input







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  ('_optimized', '_default')



   def __init__(							self,       lowercase_,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






     a__    =optimized
     a__    =default



   def __call__(							self,       *lowercase_,       **lowercase_			)				->       int:






     """simple docstring"""






     if kwargs["past_key_values"] is None and self._default.config.use_cache:
       a__    =generate_past_key_values(self._default,       kwargs['''input_ids'''].shape[0],       0			)
     kwargs.pop('''position_ids''',       lowercase_			)
     for k in list(kwargs.keys()			):
       if kwargs[k] is None or isinstance(kwargs[k],       lowercase_			):
         kwargs.pop(lowercase_			)
     a__    =self._optimized(**lowercase_			)
     a__    =outputs[0]
     a__    =outputs[1]
     a__    =CausalLMOutputWithPast(
         loss=lowercase_,       logits=lowercase_,       past_key_values=lowercase_,       hidden_states=lowercase_,       attentions=lowercase_,       )
     return fixed_output



   def __getattr__(							self,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






     return getattr(self._default,       lowercase_			)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_=None,       lowercase_=None,       lowercase_=None,       **lowercase_			)				->       Dict:






     """simple docstring"""






     return self._default.prepare_inputs_for_generation(
         lowercase_,       past_key_values=lowercase_,       inputs_embeds=lowercase_,       use_cache=lowercase_,       **lowercase_			)



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_			)				->       Tuple[Tuple[torch.Tensor]]:






     """simple docstring"""






     return self._default._reorder_cache(lowercase_,       lowercase_			)





def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =argparse.ArgumentParser()
  parser.add_argument(
      '''--model_type'''      , default=_A      , type=_A      , required=_A      , help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()				)      , )
  parser.add_argument(
      '''--model_name_or_path'''      , default=_A      , type=_A      , required=_A      , help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()				)      , )

  parser.add_argument('''--prompt'''      , type=_A      , default=''''''				)
  parser.add_argument('''--length'''      , type=_A      , default=20				)
  parser.add_argument('''--stop_token'''      , type=_A      , default=_A      , help='''Token at which text generation is stopped'''				)

  parser.add_argument(
      '''--temperature'''      , type=_A      , default=1.0      , help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''      , )
  parser.add_argument(
      '''--repetition_penalty'''      , type=_A      , default=1.0      , help='''primarily useful for CTRL model; in that case, use 1.2'''				)
  parser.add_argument('''--k'''      , type=_A      , default=0				)
  parser.add_argument('''--p'''      , type=_A      , default=0.9				)

  parser.add_argument('''--prefix'''      , type=_A      , default=''''''      , help='''Text added prior to input.'''				)
  parser.add_argument('''--padding_text'''      , type=_A      , default=''''''      , help='''Deprecated, the use of `--prefix` is preferred.'''				)
  parser.add_argument('''--xlm_language'''      , type=_A      , default=''''''      , help='''Optional language when used with the XLM model.'''				)

  parser.add_argument('''--seed'''      , type=_A      , default=42      , help='''random seed for initialization'''				)
  parser.add_argument('''--no_cuda'''      , action='''store_true'''      , help='''Avoid using CUDA when available'''				)
  parser.add_argument('''--num_return_sequences'''      , type=_A      , default=1      , help='''The number of samples to generate.'''				)
  parser.add_argument(
      '''--fp16'''      , action='''store_true'''      , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''      , )
  parser.add_argument('''--jit'''      , action='''store_true'''      , help='''Whether or not to use jit trace to accelerate inference'''				)
  a__    =parser.parse_args()

  a__    =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''				)
  a__    =0 if args.no_cuda else torch.cuda.device_count()

  logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""				)

  set_seed(_A				)

  # Initialize the model and tokenizer
  try:
    a__    =args.model_type.lower()
    a__, a__    =MODEL_CLASSES[args.model_type]
  except KeyError:
    raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''				)

  a__    =tokenizer_class.from_pretrained(args.model_name_or_path				)
  if tokenizer.pad_token is None:
    a__    =tokenizer.eos_token
  a__    =model_class.from_pretrained(args.model_name_or_path				)
  model.to(args.device				)

  if args.fpaa:
    model.half()
  a__    =getattr(model.config      , '''max_position_embeddings'''      , 0				)
  a__    =adjust_length_to_model(args.length      , max_sequence_length=_A				)
  logger.info(_A				)

  a__    =args.prompt if args.prompt else input('''Model prompt >>> '''				)

  # Different models need different input formatting and/or extra arguments
  a__    =args.model_type in PREPROCESSING_FUNCTIONS.keys()
  if requires_preprocessing:
    a__    =PREPROCESSING_FUNCTIONS.get(args.model_type				)
    a__    =prepare_input(_A      , _A      , _A      , _A				)

    if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
      a__    ={'''add_space_before_punct_symbol''': True}
    else:
      a__    ={}

    a__    =tokenizer.encode(
        _A      , add_special_tokens=_A      , return_tensors='''pt'''      , **_A				)
  else:
    a__    =args.prefix if args.prefix else args.padding_text
    a__    =tokenizer.encode(prefix + prompt_text      , add_special_tokens=_A      , return_tensors='''pt'''				)
  a__    =encoded_prompt.to(args.device				)

  if encoded_prompt.size()[-1] == 0:
    a__    =None
  else:
    a__    =encoded_prompt

  if args.jit:
    a__    =['''enable jit''']
    a__    =prepare_jit_inputs(_A      , _A      , _A				)
    torch._C._jit_set_texpr_fuser_enabled(_A				)
    a__    =False
    if hasattr(_A      , '''forward'''				):
      a__    =inspect.signature(model.forward				)
    else:
      a__    =inspect.signature(model.__call__				)
    a__    =tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(_A      , _A				) is not None				)
    a__    =torch.jit.trace(_A      , _A      , strict=_A				)
    a__    =torch.jit.freeze(traced_model.eval()				)
    traced_model(*_A				)
    traced_model(*_A				)

    a__    =_ModelFallbackWrapper(_A      , _A				)

  a__    =model.generate(
      input_ids=_A      , max_length=args.length + len(encoded_prompt[0]				)      , temperature=args.temperature      , top_k=args.k      , top_p=args.p      , repetition_penalty=args.repetition_penalty      , do_sample=_A      , num_return_sequences=args.num_return_sequences      , )

  # Remove the batch dimension when returning multiple sequences
  if len(output_sequences.shape				) > 2:
    output_sequences.squeeze_()

  a__    =[]

  for generated_sequence_idx, generated_sequence in enumerate(_A				):
    print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""				)
    a__    =generated_sequence.tolist()

    # Decode text
    a__    =tokenizer.decode(_A      , clean_up_tokenization_spaces=_A				)

    # Remove all text after the stop token
    a__    =text[: text.find(args.stop_token				) if args.stop_token else None]

    # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
    a__    =(
        prompt_text + text[len(tokenizer.decode(encoded_prompt[0]      , clean_up_tokenization_spaces=_A				)				) :]
    )

    generated_sequences.append(_A				)
    print(_A				)

  return generated_sequences


if __name__ == "__main__":
       main()



import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


lowerCamelCase 	=   logging.getLogger(__name__)




def      UpperCAmelCase__							(     _A :					Optional[Any]				):





  '''simple docstring'''







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




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





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Dict="gpt2"				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Tuple      , _A :					Dict				):





  '''simple docstring'''







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

  a__    =['''bias''', '''LayerNorm.weight''']
  a__    =[
      {
          '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay				)],
          '''weight_decay''': 0.0,
      },
      {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay				)], '''weight_decay''': 0.0},
  ]
  a__    =AdamW(_A      , lr=5E-5      , eps=1E-8				)
  a__    =get_linear_schedule_with_warmup(_A      , 0      , _A				)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler




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





  '''simple docstring'''







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




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





  '''simple docstring'''







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

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

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

  for step in tqdm(range(_A				)				):
    a__    =torch.zeros((1, context_len)      , dtype=torch.long      , device=_A				)
    a__    =random.choice(_A				)
    a__    =random.randint(0      , len(story[0]				) - context_len - 1				)
    a__    =story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__    =model(_A      , labels=_A				)
    a__    =outputs[0]
    a__    =compute_perplexity(_A      , _A      , _A				)
    model.train()
    lm_loss.backward()
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()      , 3.0				)
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule

    # Compute perplexity after back-propagating on the selected context
    a__    =compute_perplexity(_A      , _A      , _A				)

    # Periodically save the stored (X, IG(X)) pairs
    if step % 10_00 == 0 and step > 1:
      intermittent_save(_A      , _A      , _A      , _A				)

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

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

  intermittent_save(_A      , _A      , _A      , _A				)




def      UpperCAmelCase__							(     _A :					Any      , _A :					Optional[int]="data/tokenized_stories_train_wikitext103.jbl"      , _A :					Any=1_00      , _A :					Dict=10_26      , _A :					Any=True				):





  '''simple docstring'''







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

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




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





  '''simple docstring'''







  a__    =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''				)
  # We will use the first 512 pairs from our dataset as a test set for
  # our secondary learner and the rest to train
  a__    =train_dataset[:5_12]
  a__    =train_dataset[5_12:]
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)

  # secondary learner model set up
  a__    =nn.MSELoss()
  a__    =nn.MSELoss(reduction='''sum'''				)
  secondary_learner.to(_A				)
  a__    =torch.optim.Adam(secondary_learner.parameters()      , lr=0.00_001				)
  secondary_learner.train()

  # TODO in original code this is written as number of actual batches seen
  # not number of items seen but other places it is number of items instead.
  # improve consistency! changed this to epochs for clarity
  a__    =float('''inf'''				)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_A				)				):
    a__    =0.0
    secondary_learner.train()
    for step, batch in enumerate(_A				):
      a__    =batch[0].to(_A				)
      a__    =batch[1].to(_A				)
      a__    =secondary_learner(_A				)
      q_optimizer.zero_grad()
      a__    =loss(_A      , real_q.float()				)
      q_loss.backward()
      q_optimizer.step()
      tr_q_loss += q_loss.item()

      # model trains fairly quickly so we won't wait for a full epoch
      # eval is triggered at eval_freq and end of epochs
      if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_A				)):
        a__    =tr_q_loss / (step + 1)

        secondary_learner.eval()
        a__    =0.0
        a__    =0.0
        a__    =[]
        a__    =[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_A				):
          a__    =batcha[0].to(_A				)
          a__    =batcha[1].to(_A				)
          a__    =secondary_learner(_A				)
          q_lossa += test_loss(_A      , _A				).item()
          sum_qa += torch.sum(_A				).item()
          for ei, i in enumerate(predicted_qa.cpu().detach().numpy()				):
            predicted.append(i.item()				)
          for ei, i in enumerate(real_qa.cpu().detach().numpy()				):
            actual.append(i.item()				)

        q_lossa /= len(_A				)
        print(
            '''Epoch: '''      , _A      , '''step: '''      , _A      , '''Avg. q:'''      , sum_qa / len(_A				)      , '''Train Loss: '''      , _A      , '''Test Loss: '''      , _A      , )
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)      , '''pred_vs_actual.jbl'''				)
          torch.save(secondary_learner.state_dict()      , _A				)
          a__    =q_lossa

      secondary_learner.train()
  return secondary_learner







class 		__magic_name__     (				nn.Module				):

   '''simple docstring'''




   def __init__(							self,       lowercase_			)				->       str:






     """simple docstring"""






     super(lowercase_,       self			).__init__()
     a__    =model.transformer.wte
     a__    =copy.deepcopy(model.transformer.wte.weight			)
     a__    =nn.Convad(self.embeddings.weight.size(1			),       256,       3,       padding=1			)
     a__    =nn.Sequential(nn.Linear(256,       32			),       nn.Dropout(p=0.1			),       nn.Linear(32,       32			),       nn.Linear(32,       1			)			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






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



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






     """simple docstring"""






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