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


UpperCamelCase													=		logging.getLogger(__name__)

def     lowercase_       (    _lowerCamelCase							: Optional[Any]):
    random.seed(_lowerCamelCase)
    np.random.seed(_lowerCamelCase)
    torch.manual_seed(_lowerCamelCase)
    torch.cuda.manual_seed_all(_lowerCamelCase)

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

def     lowercase_       (    _lowerCamelCase							: str="gpt2"):
    lowercase__				:     List[str]					      =       GPTaLMHeadModel.from_pretrained(_lowerCamelCase	,			output_hidden_states=_lowerCamelCase)
    torch.save(model.state_dict()	,			model_name + "local.pt")
    return model

def     lowercase_       (    _lowerCamelCase							: Tuple	,			_lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Dict):
    lowercase__				:     List[Any]					      =       copy.deepcopy(_lowerCamelCase)
    model.to(_lowerCamelCase)

    lowercase__				:     List[Any]					      =       ["bias", "LayerNorm.weight"]
    lowercase__				:     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},
    ]
    lowercase__				:     List[Any]					      =       AdamW(_lowerCamelCase	,			lr=5E-5	,			eps=1E-8)
    lowercase__				:     str					      =       get_linear_schedule_with_warmup(_lowerCamelCase	,			0	,			_lowerCamelCase)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler

def     lowercase_       (    _lowerCamelCase							: Dict	,			_lowerCamelCase							: Any	,			_lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: List[str]):
    lowercase__				:     str					      =       np.array(_lowerCamelCase).mean()
    lowercase__				:     str					      =       np.array(_lowerCamelCase).std()
    lowercase__				:     Dict					      =       (real_perps - avg) / std
    lowercase__				:     Optional[Any]					      =       list(zip(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase))
    joblib.dump(_lowerCamelCase	,			_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: Optional[int]	,			_lowerCamelCase							: int	,			_lowerCamelCase							: int	,			_lowerCamelCase							: int	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: str	,			_lowerCamelCase							: List[str]	,			_lowerCamelCase							: List[Any]="dev.jbl"	,			_lowerCamelCase							: Tuple=recopy_gpta	,			):
    lowercase__				:     str					      =       []
    lowercase__				:     Any					      =       []
    lowercase__				:     List[str]					      =       []

    # Initialize the transformer model
    lowercase__				:     str					      =       copy.deepcopy(_lowerCamelCase)
    orig_model.to(device="cpu")
    torch.cuda.empty_cache()

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

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

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

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        lowercase__				:     Tuple					      =       recopy_model(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

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

    intermittent_save(_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase	,			_lowerCamelCase)

def     lowercase_       (    _lowerCamelCase							: Any	,			_lowerCamelCase							: Any="data/tokenized_stories_train_wikitext103.jbl"	,			_lowerCamelCase							: List[Any]=100	,			_lowerCamelCase							: List[str]=1026	,			_lowerCamelCase							: Union[str, Any]=True):
    lowercase__				:     Dict					      =       joblib.load(_lowerCamelCase)
    print("data loaded")
    lowercase__				:     Optional[int]					      =       []
    if trim:
        for i, example in enumerate(_lowerCamelCase):
            if len(example[0]) > min_len:
                lowercase__				:     List[str]					      =       random.randint(0	,			len(example[0]) - context_len - 1)
                objective_set.append(example[0, start : start + context_len])
            if len(_lowerCamelCase) >= number:
                break
        lowercase__				:     Any					      =       []
        for j in range(i + 1	,			len(_lowerCamelCase)):
            if len(data[j][0]) > min_len:
                train_data.append(data[j])
    else:
        lowercase__				:     Optional[Any]					      =       data[0:number]
        lowercase__				:     Optional[int]					      =       data[number:]

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

def     lowercase_       (    _lowerCamelCase							: Tuple	,			_lowerCamelCase							: Optional[Any]	,			_lowerCamelCase							: Optional[Any]	,			_lowerCamelCase							: str	,			_lowerCamelCase							: List[str]=50	,			_lowerCamelCase							: List[str]="secondary_learner.pt"):
    lowercase__				:     Optional[int]					      =       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
    lowercase__				:     List[Any]					      =       train_dataset[:512]
    lowercase__				:     int					      =       train_dataset[512:]
    lowercase__				:     Optional[int]					      =       DataLoader(_lowerCamelCase	,			shuffle=_lowerCamelCase	,			batch_size=_lowerCamelCase)
    lowercase__				:     Any					      =       DataLoader(_lowerCamelCase	,			shuffle=_lowerCamelCase	,			batch_size=_lowerCamelCase)

    # secondary learner model set up
    lowercase__				:     Optional[int]					      =       nn.MSELoss()
    lowercase__				:     Optional[int]					      =       nn.MSELoss(reduction="sum")
    secondary_learner.to(_lowerCamelCase)
    lowercase__				:     Optional[Any]					      =       torch.optim.Adam(secondary_learner.parameters()	,			lr=0.00001)
    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
    lowercase__				:     Optional[int]					      =       float("inf")
    # Iterate through batches until we've used max_steps batches
    for epoch in range(int(_lowerCamelCase)):
        lowercase__				:     int					      =       0.0
        secondary_learner.train()
        for step, batch in enumerate(_lowerCamelCase):
            lowercase__				:     Union[str, Any]					      =       batch[0].to(_lowerCamelCase)
            lowercase__				:     List[str]					      =       batch[1].to(_lowerCamelCase)
            lowercase__				:     Optional[Any]					      =       secondary_learner(_lowerCamelCase)
            q_optimizer.zero_grad()
            lowercase__				:     Optional[Any]					      =       loss(_lowerCamelCase	,			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(_lowerCamelCase)):
                lowercase__				:     int					      =       tr_q_loss / (step + 1)

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

            secondary_learner.train()
    return secondary_learner



class 						snake_case_ (	nn.Module ):






   def __init__(   self       :  Tuple			,		lowercase_       :  int						)      ->		Dict:
       super(lowercase_			,		self						).__init__()
       lowercase__				:     List[str]					      =       model.transformer.wte
       lowercase__				:     Tuple					      =       copy.deepcopy(model.transformer.wte.weight						)
       lowercase__				:     Dict					      =       nn.Convad(self.embeddings.weight.size(1						)			,		2_56			,		3			,		padding=1						)
       lowercase__				:     List[str]					      =       nn.Sequential(nn.Linear(2_56			,		32						)			,		nn.Dropout(p=0.1						)			,		nn.Linear(32			,		32						)			,		nn.Linear(32			,		1						)						)






   def    __UpperCamelCase  (   self       :  int			,		lowercase_       :  Any						)      ->		int:
       lowercase__				:     Any					      =       torch.max(self.conv(self.embeddings(lowercase_						).squeeze(1						).transpose(1			,		2						)						)			,		2						)[0]
       lowercase__				:     Union[str, Any]					      =       self.fc(lowercase_						)
       return qs.squeeze(1						)







   @classmethod
   def    __UpperCamelCase  (   cls       :  str			,		lowercase_       :  Tuple			,		lowercase_       :  List[Any]						)      ->		int:
       lowercase__				:     Optional[int]					      =       cls(lowercase_						)  # this calls __init__
       lowercase__				:     int					      =       torch.load(lowercase_						)
       secondary_learner.load_state_dict(lowercase_						)
       lowercase__				:     str					      =       model.transformer.wte
       lowercase__				:     List[Any]					      =       copy.deepcopy(model.transformer.wte.weight						)
       return secondary_learner




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class 						snake_case_ (	tf.keras.layers.Layer ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

       lowercase__				:     Optional[int]					      =       vocab_list
       lowercase__				:     List[Any]					      =       do_lower_case
       lowercase__				:     Dict					      =       cls_token_id or vocab_list.index("[CLS]"						)
       lowercase__				:     Any					      =       sep_token_id or vocab_list.index("[SEP]"						)
       lowercase__				:     Union[str, Any]					      =       pad_token_id or vocab_list.index("[PAD]"						)
       lowercase__				:     List[str]					      =       ShrinkLongestTrimmer(max_length - 3			,		axis=1						)  # Allow room for special tokens
       lowercase__				:     Dict					      =       max_length
       lowercase__				:     List[str]					      =       padding
       lowercase__				:     Tuple					      =       truncation
       lowercase__				:     Tuple					      =       pad_to_multiple_of
       lowercase__				:     Optional[Any]					      =       return_token_type_ids
       lowercase__				:     Dict					      =       return_attention_mask






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       lowercase__				:     Union[str, Any]					      =       tokenizer.get_vocab()
       lowercase__				:     Tuple					      =       sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,		key=lambda lowercase_						: x[1]						)
       lowercase__				:     Tuple					      =       [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowercase_			,		do_lower_case=lowercase_			,		cls_token_id=lowercase_			,		sep_token_id=lowercase_			,		pad_token_id=lowercase_			,		**lowercase_			,		)






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     str					      =       self.padding
       if padding not in ("longest", "max_length"):
           raise ValueError("Padding must be either 'longest' or 'max_length'!"						)
       if max_length is not None and text_pair is not None:
           # Because we have to instantiate a Trimmer to do it properly
           raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"						)
       if max_length is None:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None:
           if text.shape.rank > 1:
               raise ValueError("text argument should not be multidimensional when a text pair is supplied!"						)
           if text_pair.shape.rank > 1:
               raise ValueError("text_pair should not be multidimensional!"						)
       if text.shape.rank == 2:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       return {
           "vocab_list": self.vocab_list,
           "do_lower_case": self.do_lower_case,
           "cls_token_id": self.cls_token_id,
           "sep_token_id": self.sep_token_id,
           "pad_token_id": self.pad_token_id,
       }



