




'''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


a_   :	List[str]							   = logging.getLogger(__name__)





def   a_							(   __snake_case							:    Any     )							->		Optional[int]:



       """simple docstring"""
       random.seed(__snake_case     )
       np.random.seed(__snake_case     )
       torch.manual_seed(__snake_case     )
       torch.cuda.manual_seed_all(__snake_case     )





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    Any     , __snake_case							:    int     )							->		Any:



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





def   a_							(   __snake_case							:    Dict="gpt2"     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=GPTaLMHeadModel.from_pretrained(__snake_case     , output_hidden_states=__snake_case     )
       torch.save(model.state_dict()     , model_name + '''local.pt'''     )
       return model





def   a_							(   __snake_case							:    str     , __snake_case							:    str     , __snake_case							:    Any     )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=copy.deepcopy(__snake_case     )
       model.to(__snake_case     )

       lowerCamelCase_							=['''bias''', '''LayerNorm.weight''']
       lowerCamelCase_							=[
           {
               '''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},
       ]
       lowerCamelCase_							=AdamW(__snake_case     , lr=5e-5     , eps=1e-8     )
       lowerCamelCase_							=get_linear_schedule_with_warmup(__snake_case     , 0     , __snake_case     )
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler





def   a_							(   __snake_case							:    Any     , __snake_case							:    Union[str, Any]     , __snake_case							:    Optional[int]     , __snake_case							:    List[str]     )							->		Dict:



       """simple docstring"""
       lowerCamelCase_							=np.array(__snake_case     ).mean()
       lowerCamelCase_							=np.array(__snake_case     ).std()
       lowerCamelCase_							=(real_perps - avg) / std
       lowerCamelCase_							=list(zip(__snake_case     , __snake_case     , __snake_case     )     )
       joblib.dump(__snake_case     , __snake_case     )





def   a_							(   __snake_case							:    List[Any]     , __snake_case							:    Any     , __snake_case							:    Tuple     , __snake_case							:    int     , __snake_case							:    str     , __snake_case							:    List[str]     , __snake_case							:    Optional[int]     , __snake_case							:    List[str]="dev.jbl"     , __snake_case							:    List[str]=recopy_gpta     , )							->		Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=[]
       lowerCamelCase_							=[]
       lowerCamelCase_							=[]

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

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

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

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

              # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=recopy_model(__snake_case     , __snake_case     , __snake_case     )

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

       intermittent_save(__snake_case     , __snake_case     , __snake_case     , __snake_case     )





def   a_							(   __snake_case							:    str     , __snake_case							:    List[str]="data/tokenized_stories_train_wikitext103.jbl"     , __snake_case							:    Optional[Any]=100     , __snake_case							:    List[Any]=1026     , __snake_case							:    Dict=True     )							->		Any:



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

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





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    Dict     , __snake_case							:    Optional[Any]     , __snake_case							:    Tuple=50     , __snake_case							:    Optional[Any]="secondary_learner.pt"     )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=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
       lowerCamelCase_							=train_dataset[:512]
       lowerCamelCase_							=train_dataset[512:]
       lowerCamelCase_							=DataLoader(__snake_case     , shuffle=__snake_case     , batch_size=__snake_case     )
       lowerCamelCase_							=DataLoader(__snake_case     , shuffle=__snake_case     , batch_size=__snake_case     )

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

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

                     secondary_learner.train()
       return secondary_learner


class 						__UpperCamelCase (				nn.Module       ):






       def __init__(							self,	lowerCAmelCase						):



              """simple docstring"""







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






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







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






       @classmethod
       def   lowercase__  (							cls,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







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










'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbartaa import MBartaaTokenizer
else:
     a_   :	Tuple							   = None


a_   :	List[Any]							   = logging.get_logger(__name__)

a_   :	Any							   = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a_   :	Any							   = {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

a_   :	Dict							   = {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
a_   :	List[str]							   = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Any   		=VOCAB_FILES_NAMES
       lowercase			:					str   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					Dict   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=MBartaaTokenizer

       lowercase			:					List[int]   		=[]
       lowercase			:					List[int]   		=[]






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="</s>",	lowerCAmelCase="</s>",	lowerCAmelCase="<s>",	lowerCAmelCase="<unk>",	lowerCAmelCase="<pad>",	lowerCAmelCase="<mask>",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=AddedToken(lowerCAmelCase,	lstrip=lowerCAmelCase,	rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase,	lowerCAmelCase						) else mask_token

              lowerCamelCase_							=kwargs.get('''additional_special_tokens''',	[]						)
              kwargs["additional_special_tokens"] += [
                  code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
              ]

              super().__init__(
                  lowerCAmelCase,	src_lang=lowerCAmelCase,	tgt_lang=lowerCAmelCase,	tokenizer_file=lowerCAmelCase,	eos_token=lowerCAmelCase,	sep_token=lowerCAmelCase,	cls_token=lowerCAmelCase,	unk_token=lowerCAmelCase,	pad_token=lowerCAmelCase,	mask_token=lowerCAmelCase,	**lowerCAmelCase,	)

              lowerCamelCase_							=vocab_file
              lowerCamelCase_							=False if not self.vocab_file else True

              lowerCamelCase_							={
                  lang_code: self.convert_tokens_to_ids(lowerCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }

              lowerCamelCase_							=src_lang if src_lang is not None else '''en_XX'''
              lowerCamelCase_							=tgt_lang
              lowerCamelCase_							=self.lang_code_to_id[self._src_lang]
              self.set_src_lang_special_tokens(self._src_lang						)






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self._src_lang






       @src_lang.setter
       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=new_src_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + self.suffix_tokens
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = "en_XX",	lowerCAmelCase = None,	lowerCAmelCase = "ro_RO",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=src_lang
              lowerCamelCase_							=tgt_lang
              return super().prepare_seqaseq_batch(lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_src_lang_special_tokens(self.src_lang						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_tgt_lang_special_tokens(self.tgt_lang						)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

              lowerCamelCase_							=self.convert_ids_to_tokens(self.prefix_tokens						)
              lowerCamelCase_							=self.convert_ids_to_tokens(self.suffix_tokens						)

              lowerCamelCase_							=processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,	self.prefix_tokens + self.suffix_tokens						)						),	)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

              lowerCamelCase_							=self.convert_ids_to_tokens(self.prefix_tokens						)
              lowerCamelCase_							=self.convert_ids_to_tokens(self.suffix_tokens						)

              lowerCamelCase_							=processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str,	self.prefix_tokens + self.suffix_tokens						)						),	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              if src_lang is None or tgt_lang is None:
                     raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''						)
              lowerCamelCase_							=src_lang
              lowerCamelCase_							=self(lowerCAmelCase,	add_special_tokens=lowerCAmelCase,	return_tensors=lowerCAmelCase,	**lowerCAmelCase						)
              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=tgt_lang_id
              return inputs






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              if not self.can_save_slow_tokenizer:
                     raise ValueError(
                         '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                         '''tokenizer.'''						)

              if not os.path.isdir(lowerCAmelCase						):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                     return
              lowerCamelCase_							=os.path.join(
                  lowerCAmelCase,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

              if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCAmelCase						):
                     copyfile(self.vocab_file,	lowerCAmelCase						)

              return (out_vocab_file,)




