





import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class 		_lowerCAmelCase(		lowerCamelCase__):

  """simple docstring"""







  lowerCamelCase__     	=       CONFIG_NAME

  @register_to_config
  def __init__(   self    ,							UpperCAmelCase = True    ,							UpperCAmelCase = 8    ,							UpperCAmelCase = "lanczos"    ,							UpperCAmelCase = True    ,							UpperCAmelCase = False    ,							)->   Tuple:
     super().__init__()

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   PIL.Image.Image:






     if images.ndim == 3:
        __A    =  images[None, ...]
     __A    =  (images * 2_55).round().astype('''uint8'''      )
     if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        __A    =  [Image.fromarray(image.squeeze()    ,							mode='''L'''      ) for image in images]
     else:
        __A    =  [Image.fromarray(lowercase_      ) for image in images]

     return pil_images

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   np.ndarray:






     if not isinstance(lowercase_    ,							lowercase_      ):
        __A    =  [images]
     __A    =  [np.array(lowercase_      ).astype(np.floataa      ) / 255.0 for image in images]
     __A    =  np.stack(lowercase_    ,							axis=0      )

     return images

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   torch.FloatTensor:






     if images.ndim == 3:
        __A    =  images[..., None]

     __A    =  torch.from_numpy(images.transpose(0    ,							3    ,							1    ,							2      )      )
     return images

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   np.ndarray:
     __A    =  images.cpu().permute(0    ,							2    ,							3    ,							1      ).float().numpy()
     return images

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   str:
     return 2.0 * images - 1.0

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   List[str]:
     return (images / 2 + 0.5).clamp(0    ,							1      )

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   PIL.Image.Image:
     __A    =  image.convert('''RGB'''      )
     return image

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							)->   PIL.Image.Image:






     if height is None:
        __A    =  image.height
     if width is None:
        __A    =  image.width

     __A   ,		__A    =  (
         x - x % self.config.vae_scale_factor for x in (width, height)
     )  # resize to integer multiple of vae_scale_factor
     __A    =  image.resize((width, height)    ,							resample=PIL_INTERPOLATION[self.config.resample]      )
     return image

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None    ,							UpperCAmelCase = None    ,							)->   torch.Tensor:
     __A    =  (PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(lowercase_    ,							lowercase_      ):
        __A    =  [image]
     elif not (isinstance(lowercase_    ,							lowercase_      ) and all(isinstance(lowercase_    ,							lowercase_      ) for i in image      )):
        raise ValueError(
            f"Input is in incorrect format: {[type(lowercase_      ) for i in image]}. Currently, we only support {', '.join(lowercase_      )}"      )

     if isinstance(image[0]    ,							PIL.Image.Image      ):
        if self.config.do_convert_rgb:
           __A    =  [self.convert_to_rgb(lowercase_      ) for i in image]
        if self.config.do_resize:
           __A    =  [self.resize(lowercase_    ,							lowercase_    ,							lowercase_      ) for i in image]
        __A    =  self.pil_to_numpy(lowercase_      )  # to np
        __A    =  self.numpy_to_pt(lowercase_      )  # to pt

     elif isinstance(image[0]    ,							np.ndarray      ):
        __A    =  np.concatenate(lowercase_    ,							axis=0      ) if image[0].ndim == 4 else np.stack(lowercase_    ,							axis=0      )
        __A    =  self.numpy_to_pt(lowercase_      )
        __A   ,		__A   ,		__A   ,		__A    =  image.shape
        if self.config.do_resize and (
            height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
        ):
           raise ValueError(
               f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
               f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"      )

     elif isinstance(image[0]    ,							torch.Tensor      ):
        __A    =  torch.cat(lowercase_    ,							axis=0      ) if image[0].ndim == 4 else torch.stack(lowercase_    ,							axis=0      )
        __A   ,		__A   ,		__A   ,		__A    =  image.shape

        # don't need any preprocess if the image is latents
        if channel == 4:
           return image

        if self.config.do_resize and (
            height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
        ):
           raise ValueError(
               f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
               f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"      )

         # expected range [0,1], normalize to [-1,1]
     __A    =  self.config.do_normalize
     if image.min() < 0:
        warnings.warn(
            '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
            f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"    ,							lowercase_    ,							)
        __A    =  False

     if do_normalize:
        __A    =  self.normalize(lowercase_      )

     return image





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = "pil"    ,							UpperCAmelCase = None    ,							)->   List[Any]:






     if not isinstance(lowercase_    ,							torch.Tensor      ):
        raise ValueError(
            f"Input for postprocessing is in incorrect format: {type(lowercase_      )}. We only support pytorch tensor"      )
     if output_type not in ["latent", "pt", "np", "pil"]:
        __A    =  (
            f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
            '''`pil`, `np`, `pt`, `latent`'''
        )
        deprecate('''Unsupported output_type'''    ,							'''1.0.0'''    ,							lowercase_    ,							standard_warn=lowercase_      )
        __A    =  '''np'''

     if output_type == "latent":
        return image

     if do_denormalize is None:
        __A    =  [self.config.do_normalize] * image.shape[0]

     __A    =  torch.stack(
         [self.denormalize(image[i]      ) if do_denormalize[i] else image[i] for i in range(image.shape[0]      )]      )

     if output_type == "pt":
        return image

     __A    =  self.pt_to_numpy(lowercase_      )

     if output_type == "np":
        return image

     if output_type == "pil":
        return self.numpy_to_pil(lowercase_      )







class 		_lowerCAmelCase(		lowerCamelCase__):

  """simple docstring"""







  lowerCamelCase__     	=       CONFIG_NAME

  @register_to_config
  def __init__(   self    ,							UpperCAmelCase = True    ,							UpperCAmelCase = 8    ,							UpperCAmelCase = "lanczos"    ,							UpperCAmelCase = True    ,							)->   Any:
     super().__init__()

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   Tuple:






     if images.ndim == 3:
        __A    =  images[None, ...]
     __A    =  (images * 2_55).round().astype('''uint8'''      )
     if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        __A    =  [Image.fromarray(image.squeeze()    ,							mode='''L'''      ) for image in images]
     else:
        __A    =  [Image.fromarray(image[:, :, :3]      ) for image in images]

     return pil_images

  @staticmethod
  def 						SCREAMING_SNAKE_CASE__			(   UpperCAmelCase      )->   Optional[int]:
     return image[:, :, 1] * 2**8 + image[:, :, 2]

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   str:






     if images.ndim == 3:
        __A    =  images[None, ...]
     __A    =  images[:, :, :, 3:]
     if images.shape[-1] == 6:
        __A    =  (images_depth * 2_55).round().astype('''uint8'''      )
        __A    =  [
            Image.fromarray(self.rgblike_to_depthmap(lowercase_      )    ,							mode='''I;16'''      ) for image_depth in images_depth
        ]
     elif images.shape[-1] == 4:
        __A    =  (images_depth * 65535.0).astype(np.uintaa      )
        __A    =  [Image.fromarray(lowercase_    ,							mode='''I;16'''      ) for image_depth in images_depth]
     else:
        raise Exception('''Not supported'''      )

     return pil_images





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = "pil"    ,							UpperCAmelCase = None    ,							)->   Union[str, Any]:






     if not isinstance(lowercase_    ,							torch.Tensor      ):
        raise ValueError(
            f"Input for postprocessing is in incorrect format: {type(lowercase_      )}. We only support pytorch tensor"      )
     if output_type not in ["latent", "pt", "np", "pil"]:
        __A    =  (
            f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
            '''`pil`, `np`, `pt`, `latent`'''
        )
        deprecate('''Unsupported output_type'''    ,							'''1.0.0'''    ,							lowercase_    ,							standard_warn=lowercase_      )
        __A    =  '''np'''

     if do_denormalize is None:
        __A    =  [self.config.do_normalize] * image.shape[0]

     __A    =  torch.stack(
         [self.denormalize(image[i]      ) if do_denormalize[i] else image[i] for i in range(image.shape[0]      )]      )

     __A    =  self.pt_to_numpy(lowercase_      )

     if output_type == "np":
        if image.shape[-1] == 6:
           __A    =  np.stack([self.rgblike_to_depthmap(im[:, :, 3:]      ) for im in image]    ,							axis=0      )
        else:
           __A    =  image[:, :, :, 3:]
        return image[:, :, :, :3], image_depth

     if output_type == "pil":
        return self.numpy_to_pil(lowercase_      ), self.numpy_to_depth(lowercase_      )
     else:
        raise Exception(f"This type {output_type} is not supported"      )




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__							(     _A :					Optional[Any]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Tuple      , _A :					Union[str, Any]				):





  '''simple docstring'''







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




def      UpperCAmelCase__							(     _A :					Dict="gpt2"				):





  '''simple docstring'''







  a__    =GPTaLMHeadModel.from_pretrained(_A      , output_hidden_states=_A				)
  torch.save(model.state_dict()      , model_name + '''local.pt'''				)
  return model




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Tuple      , _A :					Dict				):





  '''simple docstring'''







  a__    =copy.deepcopy(_A				)
  model.to(_A				)

  a__    =['''bias''', '''LayerNorm.weight''']
  a__    =[
      {
          '''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__    =AdamW(_A      , lr=5E-5      , eps=1E-8				)
  a__    =get_linear_schedule_with_warmup(_A      , 0      , _A				)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler




def      UpperCAmelCase__							(     _A :					List[str]      , _A :					Optional[Any]      , _A :					Tuple      , _A :					List[str]				):





  '''simple docstring'''







  a__    =np.array(_A				).mean()
  a__    =np.array(_A				).std()
  a__    =(real_perps - avg) / std
  a__    =list(zip(_A      , _A      , _A				)				)
  joblib.dump(_A      , _A				)




def      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					Dict      , _A :					Optional[Any]      , _A :					Union[str, Any]      , _A :					Dict      , _A :					str      , _A :					List[Any]      , _A :					Optional[Any]="dev.jbl"      , _A :					Optional[int]=recopy_gpta      , ):





  '''simple docstring'''







  a__    =[]
  a__    =[]
  a__    =[]

  # Initialize the transformer model
  a__    =copy.deepcopy(_A				)
  orig_model.to(device='''cpu'''				)
  torch.cuda.empty_cache()

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

  for step in tqdm(range(_A				)				):
    a__    =torch.zeros((1, context_len)      , dtype=torch.long      , device=_A				)
    a__    =random.choice(_A				)
    a__    =random.randint(0      , len(story[0]				) - context_len - 1				)
    a__    =story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__    =model(_A      , labels=_A				)
    a__    =outputs[0]
    a__    =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
    a__    =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
    a__, a__, a__    =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      UpperCAmelCase__							(     _A :					Any      , _A :					Optional[int]="data/tokenized_stories_train_wikitext103.jbl"      , _A :					Any=1_00      , _A :					Dict=10_26      , _A :					Any=True				):





  '''simple docstring'''







  a__    =joblib.load(_A				)
  print('''data loaded'''				)
  a__    =[]
  if trim:
    for i, example in enumerate(_A				):
      if len(example[0]				) > min_len:
        a__    =random.randint(0      , len(example[0]				) - context_len - 1				)
        objective_set.append(example[0, start : start + context_len]				)
      if len(_A				) >= number:
        break
    a__    =[]
    for j in range(i + 1      , len(_A				)				):
      if len(data[j][0]				) > min_len:
        train_data.append(data[j]				)
  else:
    a__    =data[0:number]
    a__    =data[number:]

  joblib.dump(_A      , '''objective_set.jbl'''				)
  print('''objective set saved'''				)
  return train_data, objective_set




def      UpperCAmelCase__							(     _A :					str      , _A :					Dict      , _A :					Union[str, Any]      , _A :					Dict      , _A :					Dict=50      , _A :					int="secondary_learner.pt"				):





  '''simple docstring'''







  a__    =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__    =train_dataset[:5_12]
  a__    =train_dataset[5_12:]
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)
  a__    =DataLoader(_A      , shuffle=_A      , batch_size=_A				)

  # secondary learner model set up
  a__    =nn.MSELoss()
  a__    =nn.MSELoss(reduction='''sum'''				)
  secondary_learner.to(_A				)
  a__    =torch.optim.Adam(secondary_learner.parameters()      , lr=0.00_001				)
  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__    =float('''inf'''				)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_A				)				):
    a__    =0.0
    secondary_learner.train()
    for step, batch in enumerate(_A				):
      a__    =batch[0].to(_A				)
      a__    =batch[1].to(_A				)
      a__    =secondary_learner(_A				)
      q_optimizer.zero_grad()
      a__    =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				)):
        a__    =tr_q_loss / (step + 1)

        secondary_learner.eval()
        a__    =0.0
        a__    =0.0
        a__    =[]
        a__    =[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_A				):
          a__    =batcha[0].to(_A				)
          a__    =batcha[1].to(_A				)
          a__    =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				)
          a__    =q_lossa

      secondary_learner.train()
  return secondary_learner







class 		__magic_name__     (				nn.Module				):

   '''simple docstring'''




   def __init__(							self,       lowercase_			)				->       str:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






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



   @classmethod
   def 					_UpperCAmelCase					(							cls,       lowercase_,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






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