
'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class     a (					__magic_name__     ):
				_snake_case		     =			'''AutoFeatureExtractor'''
				_snake_case		     =			'''Speech2Text2Tokenizer'''




				def __init__(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Dict   ):
										super().__init__(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     self.feature_extractor
										snake_case							:  int			=     False




				def __call__(				self						:					Dict,     *SCREAMING_SNAKE_CASE_						:					Any,     **SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										# For backward compatibility
										if self._in_target_context_manager:
																return self.current_processor(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										if "raw_speech" in kwargs:
																warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''   )
																snake_case							:  List[str]			=     kwargs.pop('''raw_speech'''   )
										else:
																snake_case							:  int			=     kwargs.pop('''audio''',     SCREAMING_SNAKE_CASE_   )
										snake_case							:  str			=     kwargs.pop('''sampling_rate''',     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     kwargs.pop('''text''',     SCREAMING_SNAKE_CASE_   )
										if len(SCREAMING_SNAKE_CASE_   ) > 0:
																snake_case							:  str			=     args[0]
																snake_case							:  List[Any]			=     args[1:]

										if audio is None and text is None:
																raise ValueError('''You need to specify either an `audio` or `text` input to process.'''   )

										if audio is not None:
																snake_case							:  Optional[int]			=     self.feature_extractor(SCREAMING_SNAKE_CASE_,     *SCREAMING_SNAKE_CASE_,     sampling_rate=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										if text is not None:
																snake_case							:  Any			=     self.tokenizer(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										if text is None:
																return inputs
										elif audio is None:
																return encodings
										else:
																snake_case							:  List[Any]			=     encodings['''input_ids''']
																return inputs




				def 						__snake_case				(				self						:					List[str],     *SCREAMING_SNAKE_CASE_						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Dict   ):
										return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     *SCREAMING_SNAKE_CASE_						:					Optional[int],     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				@contextmanager
				def 						__snake_case				(				self						:					Tuple   ):
										warnings.warn(
										    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
										    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
										    '''your audio inputs, or in a separate call.'''   )
										snake_case							:  Union[str, Any]			=     True
										snake_case							:  List[str]			=     self.tokenizer
										yield
										snake_case							:  Optional[int]			=     self.feature_extractor
										snake_case							:  Tuple			=     False


'''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