

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch


class     lowerCamelCase_   :




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=14     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=99     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=4     ,     _UpperCAmelCase=4     ,     _UpperCAmelCase=[[["global", "local"], 2]]     ,     _UpperCAmelCase=37     ,     _UpperCAmelCase="gelu"     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=0.0_2     ,     ):
   __UpperCAmelCase  :     Tuple		=    parent
   __UpperCAmelCase  :     List[Any]		=    batch_size
   __UpperCAmelCase  :     int		=    seq_length
   __UpperCAmelCase  :     Any		=    is_training
   __UpperCAmelCase  :     str		=    use_input_mask
   __UpperCAmelCase  :     int		=    use_token_type_ids
   __UpperCAmelCase  :     Tuple		=    use_labels
   __UpperCAmelCase  :     List[Any]		=    vocab_size
   __UpperCAmelCase  :     Dict		=    hidden_size
   __UpperCAmelCase  :     int		=    num_hidden_layers
   __UpperCAmelCase  :     Union[str, Any]		=    num_attention_heads
   __UpperCAmelCase  :     Any		=    attention_types
   __UpperCAmelCase  :     Any		=    intermediate_size
   __UpperCAmelCase  :     Dict		=    hidden_act
   __UpperCAmelCase  :     int		=    hidden_dropout_prob
   __UpperCAmelCase  :     Tuple		=    attention_probs_dropout_prob
   __UpperCAmelCase  :     Union[str, Any]		=    max_position_embeddings
   __UpperCAmelCase  :     Optional[int]		=    window_size
   __UpperCAmelCase  :     List[str]		=    initializer_range
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     Tuple		=    vocab_size - 1
   __UpperCAmelCase  :     Dict		=    vocab_size - 1
   __UpperCAmelCase  :     int		=    vocab_size - 1




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

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

   __UpperCAmelCase  :     str		=    GPTNeoConfig(
       vocab_size=self.vocab_size     ,     hidden_size=self.hidden_size     ,     num_layers=self.num_hidden_layers     ,     num_heads=self.num_attention_heads     ,     max_position_embeddings=self.max_position_embeddings     ,     use_cache=_UpperCAmelCase     ,     bos_token_id=self.bos_token_id     ,     eos_token_id=self.eos_token_id     ,     pad_token_id=self.pad_token_id     ,     window_size=self.window_size     ,     attention_types=self.attention_types     ,     )

   return (config, input_ids, input_mask)




 def 						a					( self       ):
   __UpperCAmelCase  :     List[Any]		=    self.prepare_config_and_inputs()
   __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Any		=    config_and_inputs
   __UpperCAmelCase  :     Any		=    {'input_ids': input_ids, 'attention_mask': attention_mask}
   return config, inputs_dict




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    20
   __UpperCAmelCase  :     List[str]		=    model_class_name(_UpperCAmelCase       )

   __UpperCAmelCase  :     Optional[Any]		=    model.init_cache(input_ids.shape[0]     ,     _UpperCAmelCase       )
   __UpperCAmelCase  :     Union[str, Any]		=    jnp.ones((input_ids.shape[0], max_decoder_length)     ,     dtype='i4'       )

   __UpperCAmelCase  :     Dict		=    jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1       )[None, :]     ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
   __UpperCAmelCase  :     Optional[Any]		=    model(
       input_ids[:, :-1]     ,     attention_mask=_UpperCAmelCase     ,     past_key_values=_UpperCAmelCase     ,     position_ids=_UpperCAmelCase     ,     )

   __UpperCAmelCase  :     Optional[int]		=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,     dtype='i4'       )
   __UpperCAmelCase  :     str		=    model(
       input_ids[:, -1:]     ,     attention_mask=_UpperCAmelCase     ,     past_key_values=outputs_cache.past_key_values     ,     position_ids=_UpperCAmelCase     ,     )

   __UpperCAmelCase  :     Union[str, Any]		=    model(_UpperCAmelCase       )

   __UpperCAmelCase  :     List[Any]		=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
   self.parent.assertTrue(diff < 1E-3     ,     msg=F"Max diff is {diff}"       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Tuple		=    20
   __UpperCAmelCase  :     Optional[Any]		=    model_class_name(_UpperCAmelCase       )

   __UpperCAmelCase  :     Dict		=    jnp.concatenate(
       [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]     ,     axis=-1     ,     )

   __UpperCAmelCase  :     List[Any]		=    model.init_cache(input_ids.shape[0]     ,     _UpperCAmelCase       )
   __UpperCAmelCase  :     Dict		=    jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1       )[None, :]     ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )

   __UpperCAmelCase  :     Tuple		=    model(
       input_ids[:, :-1]     ,     attention_mask=_UpperCAmelCase     ,     past_key_values=_UpperCAmelCase     ,     position_ids=_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Tuple		=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,     dtype='i4'       )
   __UpperCAmelCase  :     Union[str, Any]		=    model(
       input_ids[:, -1:]     ,     past_key_values=outputs_cache.past_key_values     ,     attention_mask=_UpperCAmelCase     ,     position_ids=_UpperCAmelCase     ,     )

   __UpperCAmelCase  :     Optional[int]		=    model(_UpperCAmelCase     ,     attention_mask=_UpperCAmelCase       )

   __UpperCAmelCase  :     str		=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
   self.parent.assertTrue(diff < 1E-3     ,     msg=F"Max diff is {diff}"       )



@require_flax
class     lowerCamelCase_   ( _lowerCAmelCase  ,							_lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 _lowerCAmelCase       		=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    FlaxGPTNeoModelTester(self       )




 def 						a					( self       ):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Any		=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Tuple		=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )




 @slow
 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    GPTaTokenizer.from_pretrained('gpt2'     ,     pad_token='<|endoftext|>'     ,     padding_side='left'       )
   __UpperCAmelCase  :     Optional[Any]		=    tokenizer(['Hello this is a long string', 'Hey']     ,     return_tensors='np'     ,     padding=_UpperCAmelCase     ,     truncation=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'       )
   __UpperCAmelCase  :     int		=    False
   __UpperCAmelCase  :     Dict		=    model.config.eos_token_id

   __UpperCAmelCase  :     int		=    jax.jit(model.generate       )

   __UpperCAmelCase  :     List[str]		=    jit_generate(
       inputs['input_ids']     ,     attention_mask=inputs['attention_mask']     ,     pad_token_id=tokenizer.pad_token_id       ).sequences

   __UpperCAmelCase  :     Union[str, Any]		=    tokenizer.batch_decode(_UpperCAmelCase     ,     skip_special_tokens=_UpperCAmelCase       )

   __UpperCAmelCase  :     List[Any]		=    [
       'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
       'Hey, I\'m a little late to the party. I\'m going to',
   ]

   self.assertListEqual(_UpperCAmelCase     ,     _UpperCAmelCase       )




 @is_pt_flax_cross_test
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__       ):
       # prepare inputs
       __UpperCAmelCase  :     Union[str, Any]		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     List[str]		=    {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       __UpperCAmelCase  :     Union[str, Any]		=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
       __UpperCAmelCase  :     Union[str, Any]		=    getattr(_UpperCAmelCase     ,     _UpperCAmelCase       )

       __UpperCAmelCase, __UpperCAmelCase  :     List[Any]		=    pt_inputs['input_ids'].shape
       __UpperCAmelCase  :     Any		=    np.random.randint(0     ,     seq_length - 1     ,     size=(batch_size,)       )
       for batch_idx, start_index in enumerate(_UpperCAmelCase       ):
         __UpperCAmelCase  :     Any		=    0
         __UpperCAmelCase  :     Optional[Any]		=    1
         __UpperCAmelCase  :     Union[str, Any]		=    0
         __UpperCAmelCase  :     List[str]		=    1
       __UpperCAmelCase  :     Optional[Any]		=    pt_model_class(_UpperCAmelCase       ).eval()
       __UpperCAmelCase  :     int		=    model_class(_UpperCAmelCase     ,     dtype=jnp.floataa       )

       __UpperCAmelCase  :     Optional[int]		=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     List[str]		=    fx_state

       with torch.no_grad():
         __UpperCAmelCase  :     Optional[int]		=    pt_model(**_UpperCAmelCase       ).to_tuple()

       __UpperCAmelCase  :     Optional[Any]		=    fx_model(**_UpperCAmelCase       ).to_tuple()
       self.assertEqual(len(_UpperCAmelCase       )     ,     len(_UpperCAmelCase       )     ,     'Output lengths differ between Flax and PyTorch'       )
       for fx_output, pt_output in zip(_UpperCAmelCase     ,     _UpperCAmelCase       ):
         self.assert_almost_equals(fx_output[:, -1]     ,     pt_output[:, -1].numpy()     ,     4E-2       )

       with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(_UpperCAmelCase       )
         __UpperCAmelCase  :     Union[str, Any]		=    model_class.from_pretrained(_UpperCAmelCase     ,     from_pt=_UpperCAmelCase       )

       __UpperCAmelCase  :     Any		=    fx_model_loaded(**_UpperCAmelCase       ).to_tuple()
       self.assertEqual(
           len(_UpperCAmelCase       )     ,     len(_UpperCAmelCase       )     ,     'Output lengths differ between Flax and PyTorch'       )
       for fx_output_loaded, pt_output in zip(_UpperCAmelCase     ,     _UpperCAmelCase       ):
         self.assert_almost_equals(fx_output_loaded[:, -1]     ,     pt_output[:, -1].numpy()     ,     4E-2       )




 @is_pt_flax_cross_test
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     str		=    self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__       ):
       # prepare inputs
       __UpperCAmelCase  :     int		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     str		=    {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       __UpperCAmelCase  :     List[Any]		=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
       __UpperCAmelCase  :     List[str]		=    getattr(_UpperCAmelCase     ,     _UpperCAmelCase       )

       __UpperCAmelCase  :     Tuple		=    pt_model_class(_UpperCAmelCase       ).eval()
       __UpperCAmelCase  :     str		=    model_class(_UpperCAmelCase     ,     dtype=jnp.floataa       )

       __UpperCAmelCase  :     List[str]		=    load_flax_weights_in_pytorch_model(_UpperCAmelCase     ,     fx_model.params       )
       __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    pt_inputs['input_ids'].shape
       __UpperCAmelCase  :     Tuple		=    np.random.randint(0     ,     seq_length - 1     ,     size=(batch_size,)       )
       for batch_idx, start_index in enumerate(_UpperCAmelCase       ):
         __UpperCAmelCase  :     List[str]		=    0
         __UpperCAmelCase  :     Optional[int]		=    1
         __UpperCAmelCase  :     Optional[Any]		=    0
         __UpperCAmelCase  :     int		=    1

       # make sure weights are tied in PyTorch
       pt_model.tie_weights()

       with torch.no_grad():
         __UpperCAmelCase  :     Any		=    pt_model(**_UpperCAmelCase       ).to_tuple()

       __UpperCAmelCase  :     int		=    fx_model(**_UpperCAmelCase       ).to_tuple()
       self.assertEqual(len(_UpperCAmelCase       )     ,     len(_UpperCAmelCase       )     ,     'Output lengths differ between Flax and PyTorch'       )
       for fx_output, pt_output in zip(_UpperCAmelCase     ,     _UpperCAmelCase       ):
         self.assert_almost_equals(fx_output[:, -1]     ,     pt_output[:, -1].numpy()     ,     4E-2       )

       with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(_UpperCAmelCase       )
         __UpperCAmelCase  :     Any		=    pt_model_class.from_pretrained(_UpperCAmelCase     ,     from_flax=_UpperCAmelCase       )

       with torch.no_grad():
         __UpperCAmelCase  :     str		=    pt_model_loaded(**_UpperCAmelCase       ).to_tuple()

       self.assertEqual(
           len(_UpperCAmelCase       )     ,     len(_UpperCAmelCase       )     ,     'Output lengths differ between Flax and PyTorch'       )
       for fx_output, pt_output in zip(_UpperCAmelCase     ,     _UpperCAmelCase       ):
         self.assert_almost_equals(fx_output[:, -1]     ,     pt_output[:, -1].numpy()     ,     4E-2       )




 @slow
 def 						a					( self       ):
   for model_class_name in self.all_model_classes:
     __UpperCAmelCase  :     List[Any]		=    model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'       )
     __UpperCAmelCase  :     Dict		=    model(np.ones((1, 1)       )       )
     self.assertIsNotNone(_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





