


'''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


__lowercase:		Optional[int]							=     logging.getLogger(__name__)



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]   )		->					Union[str, Any]:





   '''simple docstring'''







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



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				int   )		->					Any:





   '''simple docstring'''







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



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int="gpt2"   )		->					str:





   '''simple docstring'''







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



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				str   )		->					int:





   '''simple docstring'''







   UpperCamelCase__ 						=						copy.deepcopy(_UpperCamelCase   )
   model.to(_UpperCamelCase   )

   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(_UpperCamelCase    ,		lr=5e-5    ,		eps=1e-8   )
   UpperCamelCase__ 						=						get_linear_schedule_with_warmup(_UpperCamelCase    ,		0    ,		_UpperCamelCase   )
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				int   )		->					List[str]:





   '''simple docstring'''







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



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Optional[Any]="dev.jbl"    ,		_UpperCamelCase      :				int=recopy_gpta    ,		)		->					Any:





   '''simple docstring'''







   UpperCamelCase__ 						=						[]
   UpperCamelCase__ 						=						[]
   UpperCamelCase__ 						=						[]

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

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

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

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

      # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
      UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				int="data/tokenized_stories_train_wikitext103.jbl"    ,		_UpperCamelCase      :				Dict=1_00    ,		_UpperCamelCase      :				Dict=10_26    ,		_UpperCamelCase      :				str=True   )		->					str:





   '''simple docstring'''







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

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



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Union[str, Any]=50    ,		_UpperCamelCase      :				int="secondary_learner.pt"   )		->					List[str]:





   '''simple docstring'''







   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[:5_12]
   UpperCamelCase__ 						=						train_dataset[5_12:]
   UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
   UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )

   # secondary learner model set up
   UpperCamelCase__ 						=						nn.MSELoss()
   UpperCamelCase__ 						=						nn.MSELoss(reduction="sum"   )
   secondary_learner.to(_UpperCamelCase   )
   UpperCamelCase__ 						=						torch.optim.Adam(secondary_learner.parameters()    ,		lr=0.0_0_0_0_1   )
   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(_UpperCamelCase   )   ):
      UpperCamelCase__ 						=						0.0
      secondary_learner.train()
      for step, batch in enumerate(_UpperCamelCase   ):
         UpperCamelCase__ 						=						batch[0].to(_UpperCamelCase   )
         UpperCamelCase__ 						=						batch[1].to(_UpperCamelCase   )
         UpperCamelCase__ 						=						secondary_learner(_UpperCamelCase   )
         q_optimizer.zero_grad()
         UpperCamelCase__ 						=						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   )):
            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(_UpperCamelCase   ):
               UpperCamelCase__ 						=						batcha[0].to(_UpperCamelCase   )
               UpperCamelCase__ 						=						batcha[1].to(_UpperCamelCase   )
               UpperCamelCase__ 						=						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   )
               UpperCamelCase__ 						=						q_lossa

         secondary_learner.train()
   return secondary_learner







class  UpperCAmelCase (       nn.Module):

 def __init__( self :	int,					a_ :	Dict					):





    """simple docstring"""



    super(a_,					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 :	Optional[Any],					a_ :	Optional[int]					):





    """simple docstring"""



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

 @classmethod
 def 		lowercase_						( cls :	Tuple,					a_ :	List[str],					a_ :	List[str]					):





    """simple docstring"""



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



'''simple docstring'''

from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch







class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
 _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
 _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):





    """simple docstring"""



    if image_processor is None:
       raise ValueError("You need to specify an `image_processor`."					)
    if tokenizer is None:
       raise ValueError("You need to specify a `tokenizer`."					)

    UpperCamelCase__ 						=						max_seq_length
    UpperCamelCase__ 						=						task_seq_length

    super().__init__(a_,					a_					)

 def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):





    """simple docstring"""



    if text_list is None:
       raise ValueError("tokens cannot be None."					)

    UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)

    UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]

    UpperCamelCase__ 						=						[]
    for attn_mask, input_id in zip(a_,					a_					):
       UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
       token_inputs.append(token.unsqueeze(0					)					)

    UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
    return token_inputs

 def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):





    """simple docstring"""



    if task_inputs is None:
       raise ValueError("You have to specify the task_input. Found None."					)
    elif images is None:
       raise ValueError("You have to specify the image. Found None."					)

    if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
       raise ValueError("task_inputs must be semantic, instance, or panoptic."					)

    UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
    else:
       raise TypeError("Task Inputs should be a string or a list of strings."					)

    if hasattr(a_,					"text_inputs"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

       UpperCamelCase__ 						=						[]
       for texts in texts_list:
          UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
          text_inputs.append(text_input_list.unsqueeze(0					)					)

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):





    """simple docstring"""



    if task_inputs is None:
       raise ValueError("You have to specify the task_input. Found None."					)
    elif images is None:
       raise ValueError("You have to specify the image. Found None."					)

    if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
       raise ValueError("task_inputs must be semantic, instance, or panoptic."					)

    UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
    else:
       raise TypeError("Task Inputs should be a string or a list of strings."					)

    if hasattr(a_,					"text_inputs"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

       UpperCamelCase__ 						=						[]
       for texts in texts_list:
          UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
          text_inputs.append(text_input_list.unsqueeze(0					)					)

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):





    """simple docstring"""



    return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)

 def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):





    """simple docstring"""



    return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)

 def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):





    """simple docstring"""



    return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)