




import copy
import logging
import random

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

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


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

       _A				:	Optional[Any]       =			["bias", "LayerNorm.weight"]
       _A				:	Optional[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},
       ]
       _A				:	Union[str, Any]       =			AdamW(UpperCamelCase__    ,							lr=5E-5    ,							eps=1E-8						)
       _A				:	Union[str, Any]       =			get_linear_schedule_with_warmup(UpperCamelCase__    ,							0    ,							UpperCamelCase__						)
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  List[str]    ,							UpperCamelCase__					:  Optional[int]						):
       _A				:	Any       =			np.array(UpperCamelCase__						).mean()
       _A				:	Tuple       =			np.array(UpperCamelCase__						).std()
       _A				:	Any       =			(real_perps - avg) / std
       _A				:	int       =			list(zip(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)						)
       joblib.dump(UpperCamelCase__    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Tuple="dev.jbl"    ,							UpperCamelCase__					:  Tuple=recopy_gpta    ,							):
       _A				:	str       =			[]
       _A				:	Optional[int]       =			[]
       _A				:	Tuple       =			[]

       # Initialize the transformer model
       _A				:	Any       =			copy.deepcopy(UpperCamelCase__						)
       orig_model.to(device="cpu"						)
       torch.cuda.empty_cache()

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

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

              # Compute perplexity after back-propagating on the selected context
              _A				:	Optional[int]       =			compute_perplexity(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

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

              # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
              _A				:	List[str]       =			recopy_model(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

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

       intermittent_save(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Dict="data/tokenized_stories_train_wikitext103.jbl"    ,							UpperCamelCase__					:  str=100    ,							UpperCamelCase__					:  Optional[int]=1026    ,							UpperCamelCase__					:  Optional[int]=True						):
       _A				:	List[Any]       =			joblib.load(UpperCamelCase__						)
       print("data loaded"						)
       _A				:	List[str]       =			[]
       if trim:
              for i, example in enumerate(UpperCamelCase__						):
                     if len(example[0]						) > min_len:
                            _A				:	Union[str, Any]       =			random.randint(0    ,							len(example[0]						) - context_len - 1						)
                            objective_set.append(example[0, start : start + context_len]						)
                     if len(UpperCamelCase__						) >= number:
                            break
              _A				:	Dict       =			[]
              for j in range(i + 1    ,							len(UpperCamelCase__						)						):
                     if len(data[j][0]						) > min_len:
                            train_data.append(data[j]						)
       else:
              _A				:	Optional[int]       =			data[0:number]
              _A				:	List[str]       =			data[number:]

       joblib.dump(UpperCamelCase__    ,							"objective_set.jbl"						)
       print("objective set saved"						)
       return train_data, objective_set
def 							_UpperCAmelCase    (UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Union[str, Any]=50    ,							UpperCamelCase__					:  List[str]="secondary_learner.pt"						):
       _A				:	Tuple       =			torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)
       # We will use the first 512 pairs from our dataset as a test set for
       # our secondary learner and the rest to train
       _A				:	List[str]       =			train_dataset[:512]
       _A				:	str       =			train_dataset[512:]
       _A				:	List[str]       =			DataLoader(UpperCamelCase__    ,							shuffle=UpperCamelCase__    ,							batch_size=UpperCamelCase__						)
       _A				:	List[Any]       =			DataLoader(UpperCamelCase__    ,							shuffle=UpperCamelCase__    ,							batch_size=UpperCamelCase__						)

       # secondary learner model set up
       _A				:	Tuple       =			nn.MSELoss()
       _A				:	Union[str, Any]       =			nn.MSELoss(reduction="sum"						)
       secondary_learner.to(UpperCamelCase__						)
       _A				:	Dict       =			torch.optim.Adam(secondary_learner.parameters()    ,							lr=0.0_00_01						)
       secondary_learner.train()

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

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

                     secondary_learner.train()
       return secondary_learner







class 					lowerCAmelCase__	(    nn.Module):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase)      ->    List[str]:
            super(__lowerCamelCase			,						self).__init__()
            _A				:	Dict       =			model.transformer.wte
            _A				:	List[Any]       =			copy.deepcopy(model.transformer.wte.weight)
            _A				:	int       =			nn.Convad(self.embeddings.weight.size(1)			,						2_5_6			,						3			,						padding=1)
            _A				:	Optional[Any]       =			nn.Sequential(nn.Linear(2_5_6			,						3_2)			,						nn.Dropout(p=0.1)			,						nn.Linear(3_2			,						3_2)			,						nn.Linear(3_2			,						1))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[str]:
            _A				:	str       =			torch.max(self.conv(self.embeddings(__lowerCamelCase).squeeze(1).transpose(1			,						2))			,						2)[0]
            _A				:	List[Any]       =			self.fc(__lowerCamelCase)
            return qs.squeeze(1)


     @classmethod
     def 			_lowerCamelCase			(						cls			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Tuple:
            _A				:	Dict       =			cls(__lowerCamelCase)  # this calls __init__
            _A				:	Tuple       =			torch.load(__lowerCamelCase)
            secondary_learner.load_state_dict(__lowerCamelCase)
            _A				:	Tuple       =			model.transformer.wte
            _A				:	Union[str, Any]       =			copy.deepcopy(model.transformer.wte.weight)
            return secondary_learner




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCAmelCase__						=			datasets.utils.logging.get_logger(__name__)







class 					lowerCAmelCase__	(    folder_based_builder.FolderBasedBuilderConfig):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None







class 					lowerCAmelCase__	(    folder_based_builder.FolderBasedBuilder):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			datasets.Image()
     __SCREAMING_SNAKE_CASE        =			"image"
     __SCREAMING_SNAKE_CASE        =			ImageFolderConfig
     __SCREAMING_SNAKE_CASE        =			42  # definition at the bottom of the script
     __SCREAMING_SNAKE_CASE        =			ImageClassification(image_column="image"					,   label_column="label")




lowerCAmelCase__						=			[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
lowerCAmelCase__						=			IMAGE_EXTENSIONS
