



"""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          =						logging.getLogger(__name__)
def    a__	(      lowerCAmelCase__      ):
      random.seed(__lowercase      )
      np.random.seed(__lowercase      )
      torch.manual_seed(__lowercase      )
      torch.cuda.manual_seed_all(__lowercase      )
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__      ):
      model.eval()
      UpperCAmelCase_			 =							next(model.parameters()      ).device
      UpperCAmelCase_			 =							1
      UpperCAmelCase_			 =							torch.zeros((eval_batch_size, context_len)	,			dtype=torch.long	,			device=__lowercase      )
      UpperCAmelCase_			 =							DataLoader(__lowercase	,			shuffle=__lowercase	,			batch_size=__lowercase      )
      UpperCAmelCase_			 =							torch.zeros(1	,			device=__lowercase      )
      UpperCAmelCase_			 =							0
      for batch in eval_dataloader:
            batch.to(__lowercase      )
            # pad
            context.zero_()
            for i in range(__lowercase      ):
                  UpperCAmelCase_			 =							batch[i]
            UpperCAmelCase_			 =							model(__lowercase	,			labels=__lowercase      )
            eval_loss += outputs[0].sum().item()
            nb_eval_examples += batch.size(0      )
      UpperCAmelCase_			 =							eval_loss / nb_eval_examples
      UpperCAmelCase_			 =							torch.exp(__lowercase      )
      model.train()
      return perplexity
def    a__	(      lowerCAmelCase__="gpt2"      ):
      UpperCAmelCase_			 =							GPTaLMHeadModel.from_pretrained(__lowercase	,			output_hidden_states=__lowercase      )
      torch.save(model.state_dict()	,			model_name + "local.pt"      )
      return model
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__      ):
      UpperCAmelCase_			 =							copy.deepcopy(__lowercase      )
      model.to(__lowercase      )

      UpperCAmelCase_			 =							["bias", "LayerNorm.weight"]
      UpperCAmelCase_			 =							[
          {
              "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_			 =							AdamW(__lowercase	,			lr=5e-5	,			eps=1e-8      )
      UpperCAmelCase_			 =							get_linear_schedule_with_warmup(__lowercase	,			0	,			__lowercase      )
      torch.cuda.empty_cache()
      return model, lm_optimizer, lm_scheduler
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__      ):
      UpperCAmelCase_			 =							np.array(__lowercase      ).mean()
      UpperCAmelCase_			 =							np.array(__lowercase      ).std()
      UpperCAmelCase_			 =							(real_perps - avg) / std
      UpperCAmelCase_			 =							list(zip(__lowercase	,			__lowercase	,			__lowercase      )      )
      joblib.dump(__lowercase	,			__lowercase      )
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__="dev.jbl"	,			lowerCAmelCase__=recopy_gpta	,			):
      UpperCAmelCase_			 =							[]
      UpperCAmelCase_			 =							[]
      UpperCAmelCase_			 =							[]

      # Initialize the transformer model
      UpperCAmelCase_			 =							copy.deepcopy(__lowercase      )
      orig_model.to(device="cpu"      )
      torch.cuda.empty_cache()

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

      for step in tqdm(range(__lowercase      )      ):
            UpperCAmelCase_			 =							torch.zeros((1, context_len)	,			dtype=torch.long	,			device=__lowercase      )
            UpperCAmelCase_			 =							random.choice(__lowercase      )
            UpperCAmelCase_			 =							random.randint(0	,			len(story[0]      ) - context_len - 1      )
            UpperCAmelCase_			 =							story[0][start : start + context_len]
            lm_optimizer.zero_grad()
            UpperCAmelCase_			 =							model(__lowercase	,			labels=__lowercase      )
            UpperCAmelCase_			 =							outputs[0]
            UpperCAmelCase_			 =							compute_perplexity(__lowercase	,			__lowercase	,			__lowercase      )
            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_			 =							compute_perplexity(__lowercase	,			__lowercase	,			__lowercase      )

            # Periodically save the stored (X, IG(X)) pairs
            if step % 1000 == 0 and step > 1:
                  intermittent_save(__lowercase	,			__lowercase	,			__lowercase	,			__lowercase      )

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

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

      intermittent_save(__lowercase	,			__lowercase	,			__lowercase	,			__lowercase      )
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"	,			lowerCAmelCase__=100	,			lowerCAmelCase__=1026	,			lowerCAmelCase__=True      ):
      UpperCAmelCase_			 =							joblib.load(__lowercase      )
      print("data loaded"      )
      UpperCAmelCase_			 =							[]
      if trim:
            for i, example in enumerate(__lowercase      ):
                  if len(example[0]      ) > min_len:
                        UpperCAmelCase_			 =							random.randint(0	,			len(example[0]      ) - context_len - 1      )
                        objective_set.append(example[0, start : start + context_len]      )
                  if len(__lowercase      ) >= number:
                        break
            UpperCAmelCase_			 =							[]
            for j in range(i + 1	,			len(__lowercase      )      ):
                  if len(data[j][0]      ) > min_len:
                        train_data.append(data[j]      )
      else:
            UpperCAmelCase_			 =							data[0:number]
            UpperCAmelCase_			 =							data[number:]

      joblib.dump(__lowercase	,			"objective_set.jbl"      )
      print("objective set saved"      )
      return train_data, objective_set
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__=50	,			lowerCAmelCase__="secondary_learner.pt"      ):
      UpperCAmelCase_			 =							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_			 =							train_dataset[:512]
      UpperCAmelCase_			 =							train_dataset[512:]
      UpperCAmelCase_			 =							DataLoader(__lowercase	,			shuffle=__lowercase	,			batch_size=__lowercase      )
      UpperCAmelCase_			 =							DataLoader(__lowercase	,			shuffle=__lowercase	,			batch_size=__lowercase      )

      # secondary learner model set up
      UpperCAmelCase_			 =							nn.MSELoss()
      UpperCAmelCase_			 =							nn.MSELoss(reduction="sum"      )
      secondary_learner.to(__lowercase      )
      UpperCAmelCase_			 =							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_			 =							float("inf"      )
      # Iterate through batches until we've used max_steps batches
      for epoch in range(int(__lowercase      )      ):
            UpperCAmelCase_			 =							0.0
            secondary_learner.train()
            for step, batch in enumerate(__lowercase      ):
                  UpperCAmelCase_			 =							batch[0].to(__lowercase      )
                  UpperCAmelCase_			 =							batch[1].to(__lowercase      )
                  UpperCAmelCase_			 =							secondary_learner(__lowercase      )
                  q_optimizer.zero_grad()
                  UpperCAmelCase_			 =							loss(__lowercase	,			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(__lowercase      )):
                        UpperCAmelCase_			 =							tr_q_loss / (step + 1)

                        secondary_learner.eval()
                        UpperCAmelCase_			 =							0.0
                        UpperCAmelCase_			 =							0.0
                        UpperCAmelCase_			 =							[]
                        UpperCAmelCase_			 =							[]
                        # Compute performance of the secondary learner after this batch
                        for stepa, batcha in enumerate(__lowercase      ):
                              UpperCAmelCase_			 =							batcha[0].to(__lowercase      )
                              UpperCAmelCase_			 =							batcha[1].to(__lowercase      )
                              UpperCAmelCase_			 =							secondary_learner(__lowercase      )
                              q_lossa += test_loss(__lowercase	,			__lowercase      ).item()
                              sum_qa += torch.sum(__lowercase      ).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(__lowercase      )
                        print(
                            "Epoch: "	,			__lowercase	,			"step: "	,			__lowercase	,			"Avg. q:"	,			sum_qa / len(__lowercase      )	,			"Train Loss: "	,			__lowercase	,			"Test Loss: "	,			__lowercase	,			)
                        if q_lossa < best_test_loss:
                              joblib.dump((predicted, actual)	,			"pred_vs_actual.jbl"      )
                              torch.save(secondary_learner.state_dict()	,			__lowercase      )
                              UpperCAmelCase_			 =							q_lossa

                  secondary_learner.train()
      return secondary_learner


class       lowercase__   (    nn.Module      ):







       '''simple docstring'''


       def __init__(							self			:  Optional[int]		,				_UpperCAmelCase			:  Dict    ) ->						Any:


             '''simple docstring'''




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



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


             '''simple docstring'''




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







       @classmethod
       def      lowercase__				(							cls			:  Tuple		,				_UpperCAmelCase			:  Optional[int]		,				_UpperCAmelCase			:  Optional[int]    ) ->						Union[str, Any]:


             '''simple docstring'''




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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE						:    str     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}



class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'ernie'



							def __init__(						self      ,				lowercase_=30_522      ,				lowercase_=768      ,				lowercase_=12      ,				lowercase_=12      ,				lowercase_=3_072      ,				lowercase_="gelu"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=512      ,				lowercase_=2      ,				lowercase_=3      ,				lowercase_=False      ,				lowercase_=0.02      ,				lowercase_=1e-12      ,				lowercase_=0      ,				lowercase_="absolute"      ,				lowercase_=True      ,				lowercase_=None      ,				**lowercase_      ,				):
													super().__init__(pad_token_id=lowercase_      ,				**lowercase_				)

													_snake_case						:     List[Any]				=    vocab_size
													_snake_case						:     Dict				=    hidden_size
													_snake_case						:     Any				=    num_hidden_layers
													_snake_case						:     List[str]				=    num_attention_heads
													_snake_case						:     int				=    hidden_act
													_snake_case						:     Tuple				=    intermediate_size
													_snake_case						:     Tuple				=    hidden_dropout_prob
													_snake_case						:     Optional[int]				=    attention_probs_dropout_prob
													_snake_case						:     Any				=    max_position_embeddings
													_snake_case						:     List[str]				=    type_vocab_size
													_snake_case						:     List[Any]				=    task_type_vocab_size
													_snake_case						:     Optional[int]				=    use_task_id
													_snake_case						:     Tuple				=    initializer_range
													_snake_case						:     Union[str, Any]				=    layer_norm_eps
													_snake_case						:     Optional[Any]				=    position_embedding_type
													_snake_case						:     int				=    use_cache
													_snake_case						:     Optional[Any]				=    classifier_dropout




class    lowercase_    (     __snake_case		):



							@property
							def        UpperCamelCase       (						self				):
													if self.task == "multiple-choice":
																			_snake_case						:     str				=    {0: "batch", 1: "choice", 2: "sequence"}
													else:
																			_snake_case						:     Optional[int]				=    {0: "batch", 1: "sequence"}
													return OrderedDict(
													    [
													        ("input_ids", dynamic_axis),
													        ("attention_mask", dynamic_axis),
													        ("token_type_ids", dynamic_axis),
													        ("task_type_ids", dynamic_axis),
													    ]				)