

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE          = {
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = ['GPT2TokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = [
          'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPT2DoubleHeadsModel',
          'GPT2ForQuestionAnswering',
          'GPT2ForSequenceClassification',
          'GPT2ForTokenClassification',
          'GPT2LMHeadModel',
          'GPT2Model',
          'GPT2PreTrainedModel',
          'load_tf_weights_in_gpt2',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = [
          'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFGPT2DoubleHeadsModel',
          'TFGPT2ForSequenceClassification',
          'TFGPT2LMHeadModel',
          'TFGPT2MainLayer',
          'TFGPT2Model',
          'TFGPT2PreTrainedModel',
      ]

try:
      if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = ['TFGPT2Tokenizer']

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _SCREAMING_SNAKE_CASE          = ['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_fast import GPTaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpta import (
                GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTaDoubleHeadsModel,
                GPTaForQuestionAnswering,
                GPTaForSequenceClassification,
                GPTaForTokenClassification,
                GPTaLMHeadModel,
                GPTaModel,
                GPTaPreTrainedModel,
                load_tf_weights_in_gpta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gpta import (
                TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGPTaDoubleHeadsModel,
                TFGPTaForSequenceClassification,
                TFGPTaLMHeadModel,
                TFGPTaMainLayer,
                TFGPTaModel,
                TFGPTaPreTrainedModel,
            )

      try:
            if not is_keras_nlp_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_tf import TFGPTaTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
      import sys

      _SCREAMING_SNAKE_CASE          = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








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





