'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase_			:			Union[str, Any]    	=   logging.get_logger(__name__)





class 				lowercase    (    __snake_case			):
   lowerCamelCase_            =['pixel_values']


   def __init__( self					:						Tuple ,		__lowerCAmelCase					:						Any = True ,		__lowerCAmelCase					:						int = None ,		__lowerCAmelCase					:						Optional[Any] = PILImageResampling.BILINEAR ,		__lowerCAmelCase					:						Any = True ,		__lowerCAmelCase					:						Dict = 1 / 255 ,		__lowerCAmelCase					:						Any = True ,		__lowerCAmelCase					:						Optional[Any] = None ,		__lowerCAmelCase					:						Optional[Any] = None ,		**__lowerCAmelCase					:						Optional[int] ,		)   ->       None:
         super().__init__(**lowerCAmelCase__)
         lowercase_     			=   size if size is not None else {"""height""": 224, """width""": 224}
         lowercase_     			=   get_size_dict(lowerCAmelCase__)
         lowercase_     			=   do_resize
         lowercase_     			=   do_rescale
         lowercase_     			=   do_normalize
         lowercase_     			=   size
         lowercase_     			=   resample
         lowercase_     			=   rescale_factor
         lowercase_     			=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase_     			=   image_std if image_std is not None else IMAGENET_STANDARD_STD


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Union[str, Any] = PILImageResampling.BILINEAR ,		__lowerCAmelCase					:						Dict = None ,		**__lowerCAmelCase					:						Optional[int] ,		)   ->       np.ndarray:
         lowercase_     			=   get_size_dict(lowerCAmelCase__)
         if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}')
         return resize(
             lowerCAmelCase__ ,		size=(size["height"], size["width"]) ,		resample=lowerCAmelCase__ ,		data_format=lowerCAmelCase__ ,		**lowerCAmelCase__)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Dict = None ,		**__lowerCAmelCase					:						int)   ->       np.ndarray:
         return rescale(lowerCAmelCase__ ,		scale=lowerCAmelCase__ ,		data_format=lowerCAmelCase__ ,		**lowerCAmelCase__)


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						Optional[Any] = None ,		**__lowerCAmelCase					:						Union[str, Any] ,		)   ->       np.ndarray:
         return normalize(lowerCAmelCase__ ,		mean=lowerCAmelCase__ ,		std=lowerCAmelCase__ ,		data_format=lowerCAmelCase__ ,		**lowerCAmelCase__)


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						List[Any] = None ,		__lowerCAmelCase					:						Dict = None ,		__lowerCAmelCase					:						List[str] = None ,		__lowerCAmelCase					:						Dict = None ,		__lowerCAmelCase					:						List[str] = None ,		__lowerCAmelCase					:						int = None ,		__lowerCAmelCase					:						Optional[int] = None ,		__lowerCAmelCase					:						Optional[Any] = None ,		__lowerCAmelCase					:						Tuple = None ,		__lowerCAmelCase					:						Optional[int] = ChannelDimension.FIRST ,		**__lowerCAmelCase					:						Tuple ,		)   ->       Optional[Any]:
         lowercase_     			=   do_resize if do_resize is not None else self.do_resize
         lowercase_     			=   do_rescale if do_rescale is not None else self.do_rescale
         lowercase_     			=   do_normalize if do_normalize is not None else self.do_normalize
         lowercase_     			=   resample if resample is not None else self.resample
         lowercase_     			=   rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase_     			=   image_mean if image_mean is not None else self.image_mean
         lowercase_     			=   image_std if image_std is not None else self.image_std

         lowercase_     			=   size if size is not None else self.size
         lowercase_     			=   get_size_dict(lowerCAmelCase__)

         lowercase_     			=   make_list_of_images(lowerCAmelCase__)

         if not valid_images(lowerCAmelCase__):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray.")

         if do_resize and size is None:
               raise ValueError("Size must be specified if do_resize is True.")

         if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True.")

         # All transformations expect numpy arrays.
         lowercase_     			=   [to_numpy_array(lowerCAmelCase__) for image in images]

         if do_resize:
               lowercase_     			=   [self.resize(image=lowerCAmelCase__ ,		size=lowerCAmelCase__ ,		resample=lowerCAmelCase__) for image in images]

         if do_rescale:
               lowercase_     			=   [self.rescale(image=lowerCAmelCase__ ,		scale=lowerCAmelCase__) for image in images]

         if do_normalize:
               lowercase_     			=   [self.normalize(image=lowerCAmelCase__ ,		mean=lowerCAmelCase__ ,		std=lowerCAmelCase__) for image in images]

         lowercase_     			=   [to_channel_dimension_format(lowerCAmelCase__ ,		lowerCAmelCase__) for image in images]

         lowercase_     			=   {"""pixel_values""": images}
         return BatchFeature(data=lowerCAmelCase__ ,		tensor_type=lowerCAmelCase__)







'''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 :				Any														= logging.getLogger(__name__)



def  _lowerCAmelCase				(				__a      )  ->      Tuple:
     '''simple docstring'''







     random.seed(__a      )
     np.random.seed(__a      )
     torch.manual_seed(__a      )
     torch.cuda.manual_seed_all(__a      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      Union[str, Any]:
     '''simple docstring'''







     model.eval()
     _UpperCamelCase  :Any       =next(model.parameters()      ).device
     _UpperCamelCase  :Tuple       =1
     _UpperCamelCase  :Union[str, Any]       =torch.zeros((eval_batch_size, context_len)			,      dtype=torch.long			,      device=__a      )
     _UpperCamelCase  :Optional[Any]       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )
     _UpperCamelCase  :List[Any]       =torch.zeros(1			,      device=__a      )
     _UpperCamelCase  :Any       =0
     for batch in eval_dataloader:
          batch.to(__a      )
          # pad
          context.zero_()
          for i in range(__a      ):
               _UpperCamelCase  :Tuple       =batch[i]
          _UpperCamelCase  :Dict       =model(__a			,      labels=__a      )
          eval_loss += outputs[0].sum().item()
          nb_eval_examples += batch.size(0      )
     _UpperCamelCase  :Dict       =eval_loss / nb_eval_examples
     _UpperCamelCase  :Any       =torch.exp(__a      )
     model.train()
     return perplexity



def  _lowerCAmelCase				(				__a="gpt2"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :Tuple       =GPTaLMHeadModel.from_pretrained(__a			,      output_hidden_states=__a      )
     torch.save(model.state_dict()			,      model_name + """local.pt"""      )
     return model



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     model.to(__a      )

     _UpperCamelCase  :List[Any]       =["""bias""", """LayerNorm.weight"""]
     _UpperCamelCase  :Dict       =[
         {
             """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[str]       =AdamW(__a			,      lr=5e-5			,      eps=1e-8      )
     _UpperCamelCase  :List[Any]       =get_linear_schedule_with_warmup(__a			,      0			,      __a      )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =np.array(__a      ).mean()
     _UpperCamelCase  :Dict       =np.array(__a      ).std()
     _UpperCamelCase  :Tuple       =(real_perps - avg) / std
     _UpperCamelCase  :Optional[Any]       =list(zip(__a			,      __a			,      __a      )      )
     joblib.dump(__a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a="dev.jbl"			,      __a=recopy_gpta			,      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :List[Any]       =[]
     _UpperCamelCase  :List[str]       =[]
     _UpperCamelCase  :Dict       =[]

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

     # Compute perplexity of initial transformer model for comparison
     model.train()
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =recopy_model(__a			,      __a			,      __a      )

     for step in tqdm(range(__a      )      ):
          _UpperCamelCase  :List[Any]       =torch.zeros((1, context_len)			,      dtype=torch.long			,      device=__a      )
          _UpperCamelCase  :str       =random.choice(__a      )
          _UpperCamelCase  :List[Any]       =random.randint(0			,      len(story[0]      ) - context_len - 1      )
          _UpperCamelCase  :str       =story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          _UpperCamelCase  :Optional[Any]       =model(__a			,      labels=__a      )
          _UpperCamelCase  :Union[str, Any]       =outputs[0]
          _UpperCamelCase  :Optional[Any]       =compute_perplexity(__a			,      __a			,      __a      )
          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  :str       =compute_perplexity(__a			,      __a			,      __a      )

          # Periodically save the stored (X, IG(X)) pairs
          if step % 10_00 == 0 and step > 1:
               intermittent_save(__a			,      __a			,      __a			,      __a      )

          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =recopy_model(__a			,      __a			,      __a      )

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

     intermittent_save(__a			,      __a			,      __a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a="data/tokenized_stories_train_wikitext103.jbl"			,      __a=1_00			,      __a=10_26			,      __a=True      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =joblib.load(__a      )
     print("""data loaded"""      )
     _UpperCamelCase  :List[str]       =[]
     if trim:
          for i, example in enumerate(__a      ):
               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(__a      ) >= number:
                    break
          _UpperCamelCase  :str       =[]
          for j in range(i + 1			,      len(__a      )      ):
               if len(data[j][0]      ) > min_len:
                    train_data.append(data[j]      )
     else:
          _UpperCamelCase  :int       =data[0:number]
          _UpperCamelCase  :Tuple       =data[number:]

     joblib.dump(__a			,      """objective_set.jbl"""      )
     print("""objective set saved"""      )
     return train_data, objective_set



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a=50			,      __a="secondary_learner.pt"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =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  :Any       =train_dataset[:5_12]
     _UpperCamelCase  :List[str]       =train_dataset[5_12:]
     _UpperCamelCase  :int       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )
     _UpperCamelCase  :Tuple       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )

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

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

               secondary_learner.train()
     return secondary_learner




class    lowerCamelCase__  (   nn.Module     ):



   def __init__(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        super(lowerCAmelCase__					,      self     ).__init__()
        _UpperCamelCase  :Optional[Any]       =model.transformer.wte
        _UpperCamelCase  :Any       =copy.deepcopy(model.transformer.wte.weight     )
        _UpperCamelCase  :int       =nn.Convad(self.embeddings.weight.size(1     )					,      256					,      3					,      padding=1     )
        _UpperCamelCase  :int       =nn.Sequential(nn.Linear(256					,      32     )					,      nn.Dropout(p=0.1     )					,      nn.Linear(32					,      32     )					,      nn.Linear(32					,      1     )     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =torch.max(self.conv(self.embeddings(lowerCAmelCase__     ).squeeze(1     ).transpose(1					,      2     )     )					,      2     )[0]
        _UpperCamelCase  :List[Any]       =self.fc(lowerCAmelCase__     )
        return qs.squeeze(1     )




   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =cls(lowerCAmelCase__     )  # this calls __init__
        _UpperCamelCase  :Optional[Any]       =torch.load(lowerCAmelCase__     )
        secondary_learner.load_state_dict(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =model.transformer.wte
        _UpperCamelCase  :Tuple       =copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner