


import pickle

import numpy as np
from matplotlib import pyplot as plt





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Optional[Any]		,				__a				:				Optional[int]		,				__a				:				Tuple		,				__a				:				Union[str, Any]		,				__a				:				str		,				__a				:				Union[str, Any]		,				__a				:				Tuple=0.2		,				__a				:				List[str]=0.2							)					->List[Any]:
          lowerCamelCase_						:   str		= bp_numa
          lowerCamelCase_						:   Tuple		= bp_numa
          lowerCamelCase_						:   Optional[int]		= bp_numa
          lowerCamelCase_						:   Optional[Any]		= conva_get[:2]
          lowerCamelCase_						:   Dict		= conva_get[2]
          lowerCamelCase_						:   Union[str, Any]		= size_pa
          lowerCamelCase_						:   Optional[Any]		= rate_w
          lowerCamelCase_						:   Any		= rate_t
          lowerCamelCase_						:   Optional[Any]		= [
              np.mat(-1 * np.random.rand(self.conva[0]		,				self.conva[0]							) + 0.5							)
              for i in range(self.conva[1]							)
          ]
          lowerCamelCase_						:   int		= np.mat(-1 * np.random.rand(self.num_bpa		,				self.num_bpa							) + 0.5							)
          lowerCamelCase_						:   List[Any]		= np.mat(-1 * np.random.rand(self.num_bpa		,				self.num_bpa							) + 0.5							)
          lowerCamelCase_						:   str		= -2 * np.random.rand(self.conva[1]							) + 1
          lowerCamelCase_						:   List[str]		= -2 * np.random.rand(self.num_bpa							) + 1
          lowerCamelCase_						:   Union[str, Any]		= -2 * np.random.rand(self.num_bpa							) + 1



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Tuple							)					->int:
          # save model dict with pickle
          lowerCamelCase_						:   Any		= {
              """num_bp1""": self.num_bpa,
              """num_bp2""": self.num_bpa,
              """num_bp3""": self.num_bpa,
              """conv1""": self.conva,
              """step_conv1""": self.step_conva,
              """size_pooling1""": self.size_poolinga,
              """rate_weight""": self.rate_weight,
              """rate_thre""": self.rate_thre,
              """w_conv1""": self.w_conva,
              """wkj""": self.wkj,
              """vji""": self.vji,
              """thre_conv1""": self.thre_conva,
              """thre_bp2""": self.thre_bpa,
              """thre_bp3""": self.thre_bpa,
          }
          with open(_UpperCAmelCase		,				"""wb"""							) as f:
             pickle.dump(_UpperCAmelCase		,				_UpperCAmelCase							)

          print(F'''Model saved： {save_path}'''							)



       @classmethod
       def 				_lowerCAmelCase							(						cls				:				List[str]		,				__a				:				Union[str, Any]							)					->Optional[Any]:
          # read saved model
          with open(_UpperCAmelCase		,				"""rb"""							) as f:
             lowerCamelCase_						:   Optional[Any]		= pickle.load(_UpperCAmelCase							)  # noqa: S301

          lowerCamelCase_						:   Tuple		= model_dic.get("""conv1"""							)
          conv_get.append(model_dic.get("""step_conv1"""							)							)
          lowerCamelCase_						:   Union[str, Any]		= model_dic.get("""size_pooling1"""							)
          lowerCamelCase_						:   Optional[int]		= model_dic.get("""num_bp1"""							)
          lowerCamelCase_						:   str		= model_dic.get("""num_bp2"""							)
          lowerCamelCase_						:   Optional[Any]		= model_dic.get("""num_bp3"""							)
          lowerCamelCase_						:   Any		= model_dic.get("""rate_weight"""							)
          lowerCamelCase_						:   str		= model_dic.get("""rate_thre"""							)
          # create model instance
          lowerCamelCase_						:   str		= CNN(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase							)
          # modify model parameter
          lowerCamelCase_						:   str		= model_dic.get("""w_conv1"""							)
          lowerCamelCase_						:   Optional[Any]		= model_dic.get("""wkj"""							)
          lowerCamelCase_						:   int		= model_dic.get("""vji"""							)
          lowerCamelCase_						:   Any		= model_dic.get("""thre_conv1"""							)
          lowerCamelCase_						:   Tuple		= model_dic.get("""thre_bp2"""							)
          lowerCamelCase_						:   int		= model_dic.get("""thre_bp3"""							)
          return conv_ins



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				List[str]							)					->Tuple:
          return 1 / (1 + np.exp(-1 * x							))



       def 				_lowerCAmelCase							(						self				:				str		,				__a				:				Optional[int]							)					->str:
          return round(_UpperCAmelCase		,				3							)



       def 				_lowerCAmelCase							(						self				:				Dict		,				__a				:				Optional[Any]		,				__a				:				Any		,				__a				:				int		,				__a				:				List[str]		,				__a				:				int							)					->Optional[int]:
          # convolution process
          lowerCamelCase_						:   Optional[Any]		= convs[0]
          lowerCamelCase_						:   List[str]		= convs[1]
          lowerCamelCase_						:   Any		= np.shape(_UpperCAmelCase							)[0]
          # get the data slice of original image data, data_focus
          lowerCamelCase_						:   Union[str, Any]		= []
          for i_focus in range(0		,				size_data - size_conv + 1		,				_UpperCAmelCase							):
             for j_focus in range(0		,				size_data - size_conv + 1		,				_UpperCAmelCase							):
                lowerCamelCase_						:   Any		= data[
                    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
                ]
                data_focus.append(_UpperCAmelCase							)
             # calculate the feature map of every single kernel, and saved as list of matrix
          lowerCamelCase_						:   Any		= []
          lowerCamelCase_						:   Union[str, Any]		= int((size_data - size_conv) / conv_step + 1							)
          for i_map in range(_UpperCAmelCase							):
             lowerCamelCase_						:   List[str]		= []
             for i_focus in range(len(_UpperCAmelCase							)							):
                lowerCamelCase_						:   Union[str, Any]		= (
                    np.sum(np.multiply(data_focus[i_focus]		,				w_convs[i_map]							)							)
                    - thre_convs[i_map]
                )
                featuremap.append(self.sig(_UpperCAmelCase							)							)
             lowerCamelCase_						:   Union[str, Any]		= np.asmatrix(_UpperCAmelCase							).reshape(
                 _UpperCAmelCase		,				_UpperCAmelCase							)
             data_featuremap.append(_UpperCAmelCase							)

          # expanding the data slice to One dimenssion
          lowerCamelCase_						:   Union[str, Any]		= []
          for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(_UpperCAmelCase							)							)
          lowerCamelCase_						:   Dict		= np.asarray(_UpperCAmelCase							)
          return focus_list, data_featuremap



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				Optional[int]		,				__a				:				Union[str, Any]		,				__a				:				List[str]="average_pool"							)					->Tuple:
          # pooling process
          lowerCamelCase_						:   Optional[int]		= len(featuremaps[0]							)
          lowerCamelCase_						:   Union[str, Any]		= int(size_map / size_pooling							)
          lowerCamelCase_						:   Any		= []
          for i_map in range(len(_UpperCAmelCase							)							):
             lowerCamelCase_						:   List[Any]		= featuremaps[i_map]
             lowerCamelCase_						:   int		= []
             for i_focus in range(0		,				_UpperCAmelCase		,				_UpperCAmelCase							):
                for j_focus in range(0		,				_UpperCAmelCase		,				_UpperCAmelCase							):
                   lowerCamelCase_						:   int		= feature_map[
                       i_focus : i_focus + size_pooling,
                       j_focus : j_focus + size_pooling,
                   ]
                   if pooling_type == "average_pool":
                      # average pooling
                      map_pooled.append(np.average(_UpperCAmelCase							)							)
                   elif pooling_type == "max_pooling":
                      # max pooling
                      map_pooled.append(np.max(_UpperCAmelCase							)							)
             lowerCamelCase_						:   List[str]		= np.asmatrix(_UpperCAmelCase							).reshape(_UpperCAmelCase		,				_UpperCAmelCase							)
             featuremap_pooled.append(_UpperCAmelCase							)
          return featuremap_pooled



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				Tuple							)					->Union[str, Any]:
          # expanding three dimension data to one dimension list
          lowerCamelCase_						:   Optional[int]		= []
          for i in range(len(_UpperCAmelCase							)							):
             lowerCamelCase_						:   Dict		= np.shape(data[i]							)
             lowerCamelCase_						:   Any		= data[i].reshape(1		,				shapes[0] * shapes[1]							)
             lowerCamelCase_						:   Union[str, Any]		= data_listed.getA().tolist()[0]
             data_expanded.extend(_UpperCAmelCase							)
          lowerCamelCase_						:   List[str]		= np.asarray(_UpperCAmelCase							)
          return data_expanded



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				__a				:				str							)					->List[str]:
          # expanding matrix to one dimension list
          lowerCamelCase_						:   List[Any]		= np.asarray(_UpperCAmelCase							)
          lowerCamelCase_						:   Dict		= np.shape(_UpperCAmelCase							)
          lowerCamelCase_						:   List[Any]		= data_mat.reshape(1		,				shapes[0] * shapes[1]							)
          return data_expanded



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				Optional[Any]		,				__a				:				str		,				__a				:				Dict		,				__a				:				Optional[Any]		,				__a				:				Any							)					->Union[str, Any]:
          lowerCamelCase_						:   str		= []
          lowerCamelCase_						:   Tuple		= 0
          for i_map in range(_UpperCAmelCase							):
             lowerCamelCase_						:   Optional[int]		= np.ones((size_map, size_map)							)
             for i in range(0		,				_UpperCAmelCase		,				_UpperCAmelCase							):
                for j in range(0		,				_UpperCAmelCase		,				_UpperCAmelCase							):
                   lowerCamelCase_						:   int		= pd_pool[
                       i_pool
                   ]
                   lowerCamelCase_						:   List[Any]		= i_pool + 1
             lowerCamelCase_						:   Optional[Any]		= np.multiply(
                 _UpperCAmelCase		,				np.multiply(out_map[i_map]		,				(1 - out_map[i_map])							)							)
             pd_all.append(_UpperCAmelCase							)
          return pd_all



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				Optional[int]		,				__a				:				str		,				__a				:				int		,				__a				:				Optional[Any]		,				__a				:				Any		,				__a				:				Tuple=bool							)					->Any:
          # model traning
          print("""----------------------Start Training-------------------------"""							)
          print((""" - - Shape: Train_Data  """, np.shape(_UpperCAmelCase							))							)
          print((""" - - Shape: Teach_Data  """, np.shape(_UpperCAmelCase							))							)
          lowerCamelCase_						:   List[str]		= 0
          lowerCamelCase_						:   Union[str, Any]		= []
          lowerCamelCase_						:   Any		= 10_000
          while rp < n_repeat and mse >= error_accuracy:
             lowerCamelCase_						:   List[str]		= 0
             print(F'''-------------Learning Time {rp}--------------'''							)
             for p in range(len(_UpperCAmelCase							)							):
                # print('------------Learning Image: %d--------------'%p)
                lowerCamelCase_						:   str		= np.asmatrix(datas_train[p]							)
                lowerCamelCase_						:   Tuple		= np.asarray(datas_teach[p]							)
                lowerCamelCase_,	lowerCamelCase_						:   int		= self.convolute(
                    _UpperCAmelCase		,				self.conva		,				self.w_conva		,				self.thre_conva		,				conv_step=self.step_conva		,				)
                lowerCamelCase_						:   str		= self.pooling(_UpperCAmelCase		,				self.size_poolinga							)
                lowerCamelCase_						:   int		= np.shape(_UpperCAmelCase							)
                lowerCamelCase_						:   List[Any]		= self._expand(_UpperCAmelCase							)
                lowerCamelCase_						:   Any		= data_bp_input

                lowerCamelCase_						:   Union[str, Any]		= np.dot(_UpperCAmelCase		,				self.vji.T							) - self.thre_bpa
                lowerCamelCase_						:   Union[str, Any]		= self.sig(_UpperCAmelCase							)
                lowerCamelCase_						:   Union[str, Any]		= np.dot(_UpperCAmelCase		,				self.wkj.T							) - self.thre_bpa
                lowerCamelCase_						:   str		= self.sig(_UpperCAmelCase							)

                # --------------Model Leaning ------------------------
                # calculate error and gradient---------------
                lowerCamelCase_						:   Optional[Any]		= np.multiply(
                    (data_teach - bp_outa)		,				np.multiply(_UpperCAmelCase		,				(1 - bp_outa)							)							)
                lowerCamelCase_						:   str		= np.multiply(
                    np.dot(_UpperCAmelCase		,				self.wkj							)		,				np.multiply(_UpperCAmelCase		,				(1 - bp_outa)							)							)
                lowerCamelCase_						:   Any		= np.dot(_UpperCAmelCase		,				self.vji							)

                lowerCamelCase_						:   Any		= pd_i_all / (self.size_poolinga * self.size_poolinga)
                lowerCamelCase_						:   Dict		= pd_conva_pooled.T.getA().tolist()
                lowerCamelCase_						:   int		= self._calculate_gradient_from_pool(
                    _UpperCAmelCase		,				_UpperCAmelCase		,				shape_featuremapa[0]		,				shape_featuremapa[1]		,				self.size_poolinga		,				)
                # weight and threshold learning process---------
                # convolution layer
                for k_conv in range(self.conva[1]							):
                   lowerCamelCase_						:   Tuple		= self._expand_mat(pd_conva_all[k_conv]							)
                   lowerCamelCase_						:   Dict		= self.rate_weight * np.dot(_UpperCAmelCase		,				_UpperCAmelCase							)

                   lowerCamelCase_						:   Optional[int]		= self.w_conva[k_conv] + delta_w.reshape(
                       (self.conva[0], self.conva[0])							)

                   lowerCamelCase_						:   List[Any]		= (
                       self.thre_conva[k_conv]
                       - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
                   )
                # all connected layer
                lowerCamelCase_						:   Union[str, Any]		= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                lowerCamelCase_						:   Any		= self.vji + pd_j_all.T * bp_outa * self.rate_weight
                lowerCamelCase_						:   Dict		= self.thre_bpa - pd_k_all * self.rate_thre
                lowerCamelCase_						:   Any		= self.thre_bpa - pd_j_all * self.rate_thre
                # calculate the sum error of all single image
                lowerCamelCase_						:   Union[str, Any]		= np.sum(abs(data_teach - bp_outa							)							)
                error_count += errors
                # print('   ----Teach      ',data_teach)
                # print('   ----BP_output  ',bp_out3)
             lowerCamelCase_						:   Optional[int]		= rp + 1
             lowerCamelCase_						:   List[Any]		= error_count / patterns
             all_mse.append(_UpperCAmelCase							)

          def draw_error():
             lowerCamelCase_						:   Union[str, Any]		= [error_accuracy for i in range(int(n_repeat * 1.2							)							)]
             plt.plot(_UpperCAmelCase		,				"""+-"""							)
             plt.plot(_UpperCAmelCase		,				"""r--"""							)
             plt.xlabel("""Learning Times"""							)
             plt.ylabel("""All_mse"""							)
             plt.grid(_UpperCAmelCase		,				alpha=0.5							)
             plt.show()

          print("""------------------Training Complished---------------------"""							)
          print((""" - - Training epoch: """, rp, F'''     - - Mse: {mse:.6f}''')							)
          if draw_e:
             draw_error()
          return mse



       def 				_lowerCAmelCase							(						self				:				Any		,				__a				:				Any							)					->Optional[Any]:
          # model predict
          lowerCamelCase_						:   int		= []
          print("""-------------------Start Testing-------------------------"""							)
          print((""" - - Shape: Test_Data  """, np.shape(_UpperCAmelCase							))							)
          for p in range(len(_UpperCAmelCase							)							):
             lowerCamelCase_						:   Any		= np.asmatrix(datas_test[p]							)
             lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= self.convolute(
                 _UpperCAmelCase		,				self.conva		,				self.w_conva		,				self.thre_conva		,				conv_step=self.step_conva		,				)
             lowerCamelCase_						:   Union[str, Any]		= self.pooling(_UpperCAmelCase		,				self.size_poolinga							)
             lowerCamelCase_						:   Optional[Any]		= self._expand(_UpperCAmelCase							)

             lowerCamelCase_						:   Union[str, Any]		= data_bp_input
             lowerCamelCase_						:   Tuple		= bp_outa * self.vji.T - self.thre_bpa
             lowerCamelCase_						:   Tuple		= self.sig(_UpperCAmelCase							)
             lowerCamelCase_						:   Dict		= bp_outa * self.wkj.T - self.thre_bpa
             lowerCamelCase_						:   Union[str, Any]		= self.sig(_UpperCAmelCase							)
             produce_out.extend(bp_outa.getA().tolist()							)
          lowerCamelCase_						:   str		= [list(map(self.do_round		,				_UpperCAmelCase							)							) for each in produce_out]
          return np.asarray(_UpperCAmelCase							)




       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				Optional[int]							)					->Union[str, Any]:
          # return the data of image after convoluting process so we can check it out
          lowerCamelCase_						:   Any		= np.asmatrix(_UpperCAmelCase							)
          lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= self.convolute(
              _UpperCAmelCase		,				self.conva		,				self.w_conva		,				self.thre_conva		,				conv_step=self.step_conva		,				)
          lowerCamelCase_						:   int		= self.pooling(_UpperCAmelCase		,				self.size_poolinga							)

          return data_conveda, data_pooleda


if __name__ == "__main__":
      pass













import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel






def      _snake_case     (__lowercase=32     ,   __lowercase=10     ,   __lowercase=100     ,   __lowercase=1026     ,   __lowercase=True     ,   __lowercase="data/tokenized_stories_train_wikitext103.jbl"     ,   __lowercase="igf_context_pairs.jbl"     ,   ):
   set_seed(3)
   # generate train_data and objective_set
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       __lowercase     ,   __lowercase     ,   number=__lowercase     ,   min_len=1026     ,   trim=__lowercase)
   # keeps model same across runs
   set_seed(4)
   # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
   # can we train on GPU?
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

   # load pretrained model
   UpperCamelCase_						=  load_gpta('gpt2').to(__lowercase)
   print('computing perplexity on objective set')
   UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase).item()
   print('perplexity on objective set:'     ,   __lowercase)

   # collect igf pairs and save to file demo.jbl
   collect_objective_set(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase)

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()






def      _snake_case     (__lowercase     ,   __lowercase=15     ,   __lowercase=128     ,   __lowercase=100     ,   __lowercase="igf_model.pt"     ,   ):
   set_seed(42)

   # Load pre-trained model
   UpperCamelCase_						=  GPTaLMHeadModel.from_pretrained('gpt2')

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase_						=  SecondaryLearner(__lowercase)

   # Train secondary learner
   UpperCamelCase_						=  train_secondary_learner(
       __lowercase     ,   __lowercase     ,   max_epochs=__lowercase     ,   batch_size=__lowercase     ,   eval_freq=100     ,   igf_model_path=__lowercase     ,   )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=32     ,   __lowercase=1000     ,   __lowercase=16     ,   __lowercase=1.0     ,   __lowercase=recopy_gpta     ,   __lowercase=None     ,   __lowercase=10     ,   __lowercase="gpt2_finetuned.pt"     ,   ):
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
   UpperCamelCase_						=  RandomSampler(__lowercase)
   UpperCamelCase_						=  DataLoader(__lowercase     ,   sampler=__lowercase)

   UpperCamelCase_						=  max_steps // (len(__lowercase)) + 1
   UpperCamelCase_						=  0
   UpperCamelCase_						=  torch.zeros((1, context_len)     ,   dtype=torch.long     ,   device=__lowercase)
   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  recopy_model(__lowercase     ,   __lowercase     ,   __lowercase)

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(__lowercase)
      secondary_learner.eval()
   UpperCamelCase_						=  []
   UpperCamelCase_						=  0

   UpperCamelCase_						=  []
   UpperCamelCase_						=  []

   # Compute the performance of the transformer model at the beginning
   UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase)
   test_perps.append(__lowercase)
   print('Test perplexity, step'     ,   __lowercase     ,   ':'     ,   __lowercase)
   for epoch in range(int(__lowercase)):
      for step, example in enumerate(__lowercase):
         torch.cuda.empty_cache()
         UpperCamelCase_						=  random.randint(0     ,   example.size(2) - context_len - 1)
         UpperCamelCase_						=  example[0, 0, start : start + context_len]
         lm_optimizer.zero_grad()
         UpperCamelCase_						=  model(__lowercase     ,   labels=__lowercase)
         UpperCamelCase_						=  True

         if secondary_learner is not None:
            UpperCamelCase_						=  secondary_learner.forward(
                torch.tensor(__lowercase     ,   dtype=torch.long     ,   device=__lowercase).unsqueeze(0))[0].item()
            observed_qs.append(float(__lowercase))

            # Here we implement the simple non-constant threshold for the predicted IG(X) value
            # We will decay the selectivity of our secondary learner filter from
            # 1 standard deviation above average to 1 below average after 10 batches.

            if global_step == 10:
               UpperCamelCase_						=  -1
            if predicted_q < threshold:
               UpperCamelCase_						=  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()))
            UpperCamelCase_						=  outputs[0]
            lm_loss.backward()
            examples += 1

         del outputs

         # Once the batch is filled with enough contexts, backprop on the batch.
         if examples == batch_size:
            torch.cuda.empty_cache()
            UpperCamelCase_						=  0
            # Do LM backprop
            torch.nn.utils.clip_grad_norm_(model.parameters()     ,   3.0)
            lm_optimizer.step()
            lm_scheduler.step()  # Update learning rate schedule
            global_step += 1
            # Compute the performance of the transformer model at this batch
            if global_step % eval_interval == 0:
               UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase)
               test_perps.append(__lowercase)

               print('Test perplexity, step'     ,   __lowercase     ,   ':'     ,   __lowercase)
            # Break out of the loop after 60 batches
         if max_steps > 0 and global_step > 60:
            break
      if max_steps > 0 and global_step > 60:
         break

    # save finetuned transformer model
   torch.save(model.state_dict()     ,   __lowercase)
   torch.cuda.empty_cache()
   # Do some cleaning up so we can reinitialize for the next run of this function
   del lm_optimizer
   del lm_scheduler
   return model






def      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

   # Required parameters
   parser.add_argument(
       '--data_dir'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='The input data dir. Should contain data files for WikiText.'     ,   )
   parser.add_argument(
       '--model_name_or_path'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
   parser.add_argument(
       '--data_file'     ,   type=__lowercase     ,   default=__lowercase     ,   help=(
           'A jbl file containing tokenized data which can be split as objective dataset, '
           'train_dataset and test_dataset.'
       )     ,   )

   parser.add_argument(
       '--igf_data_file'     ,   type=__lowercase     ,   default=__lowercase     ,   help='A jbl file containing the context and information gain pairs to train secondary learner.'     ,   )

   parser.add_argument(
       '--output_dir'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='The output directory where the final fine-tuned model is stored.'     ,   )

   parser.add_argument(
       '--tokenizer_name'     ,   default=__lowercase     ,   type=__lowercase     ,   help='Pretrained tokenizer name or path if not the same as model_name'     ,   )
   parser.add_argument('--seed'     ,   type=__lowercase     ,   default=__lowercase     ,   help='A seed for reproducible training.')

   parser.add_argument(
       '--context_len'     ,   default=32     ,   type=__lowercase     ,   help=(
           'The maximum total input sequence length after tokenization. Sequences longer '
           'than this will be truncated, sequences shorter will be padded.'
       )     ,   )

   parser.add_argument(
       '--size_objective_set'     ,   default=100     ,   type=__lowercase     ,   help='number of articles that are long enough to be used as our objective set'     ,   )
   parser.add_argument(
       '--eval_freq'     ,   default=100     ,   type=__lowercase     ,   help='secondary model evaluation is triggered at eval_freq')

   parser.add_argument('--max_steps'     ,   default=1000     ,   type=__lowercase     ,   help='To calculate training epochs')

   parser.add_argument(
       '--secondary_learner_batch_size'     ,   default=128     ,   type=__lowercase     ,   help='batch size of training data for secondary learner'     ,   )

   parser.add_argument(
       '--batch_size'     ,   default=16     ,   type=__lowercase     ,   help='batch size of training data of language model(gpt2) ')

   parser.add_argument(
       '--eval_interval'     ,   default=10     ,   type=__lowercase     ,   help=(
           'decay the selectivity of our secondary learner filter from'
           '1 standard deviation above average to 1 below average after 10 batches'
       )     ,   )

   parser.add_argument(
       '--number'     ,   default=100     ,   type=__lowercase     ,   help='The number of examples split to be used as objective_set/test_data')

   parser.add_argument(
       '--min_len'     ,   default=1026     ,   type=__lowercase     ,   help='The minimum length of the article to be used as objective set')

   parser.add_argument(
       '--secondary_learner_max_epochs'     ,   default=15     ,   type=__lowercase     ,   help='number of epochs to train secondary learner')

   parser.add_argument('--trim'     ,   default=__lowercase     ,   type=__lowercase     ,   help='truncate the example if it exceeds context length')

   parser.add_argument(
       '--threshold'     ,   default=1.0     ,   type=__lowercase     ,   help=(
           'The threshold value used by secondary learner to filter the train_data and allow only'
           ' informative data as input to the model'
       )     ,   )

   parser.add_argument('--finetuned_model_name'     ,   default='gpt2_finetuned.pt'     ,   type=__lowercase     ,   help='finetuned_model_name')

   parser.add_argument(
       '--recopy_model'     ,   default=__lowercase     ,   type=__lowercase     ,   help='Reset the model to the original pretrained GPT-2 weights after each iteration'     ,   )

   # function calls
   # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
   generate_n_pairs(
       context_len=32     ,   max_steps=10     ,   size_objective_set=100     ,   min_len=1026     ,   trim=__lowercase     ,   data_file='data/tokenized_stories_train_wikitext103.jbl'     ,   igf_data_file='igf_context_pairs.jbl'     ,   )

   # Load train data for secondary learner
   UpperCamelCase_						=  joblib.load('data/IGF_values.jbl')

   # Train secondary learner
   UpperCamelCase_						=  training_secondary_learner(
       __lowercase     ,   secondary_learner_max_epochs=15     ,   secondary_learner_batch_size=128     ,   eval_freq=100     ,   igf_model_path='igf_model.pt'     ,   )

   # load pretrained gpt2 model
   UpperCamelCase_						=  GPTaLMHeadModel.from_pretrained('gpt2')
   set_seed(42)

   # Generate train and test data to train and evaluate gpt2 model
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       context_len=32     ,   file='data/tokenized_stories_train_wikitext103.jbl'     ,   number=100     ,   min_len=1026     ,   trim=__lowercase)

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       __lowercase     ,   __lowercase     ,   __lowercase     ,   context_len=32     ,   max_steps=1000     ,   batch_size=16     ,   threshold=1.0     ,   recopy_model=__lowercase     ,   secondary_learner=__lowercase     ,   eval_interval=10     ,   finetuned_model_name='gpt2_finetuned.pt'     ,   )


if __name__ == "__main__":
 main()





