





"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
   if is_torch_available():
      import torch
   if is_tf_available():
      import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


lowerCamelCase_	:		List[str]         =   logging.get_logger(__name__)

lowerCamelCase_	:		Optional[int]         =   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase_	:		Optional[Any]         =   {
    'vocab_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json',
    },
    'merges_file': {
        'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt',
    },
}

lowerCamelCase_	:		str         =   {
    'Salesforce/codegen-350M-mono': 20_48,
}





@lru_cache()
def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			str									=    (
      list(range(ord('!'   )					,   ord('~'   ) + 1   )   ) + list(range(ord('¡'   )					,   ord('¬'   ) + 1   )   ) + list(range(ord('®'   )					,   ord('ÿ'   ) + 1   )   )
  )
  A_    :			Any									=    bs[:]
  A_    :			Any									=    0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(_UpperCAmelCase   )
      cs.append(2**8 + n   )
      n += 1
  A_    :			Dict									=    [chr(_UpperCAmelCase   ) for n in cs]
  return dict(zip(_UpperCAmelCase					,   _UpperCAmelCase   )   )





def 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Tuple									=    set()
  A_    :			Any									=    word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    A_    :			List[Any]									=    char
  return pairs





class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Any             =  VOCAB_FILES_NAMES
      lowercase_ :					List[str]             =  PRETRAINED_VOCAB_FILES_MAP
      lowercase_ :					str             =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase_ :					int             =  ["""input_ids""", """attention_mask"""]

      def __init__(	self   , snake_case_   , snake_case_   , snake_case_="replace"   , snake_case_="<|endoftext|>"   , snake_case_="<|endoftext|>"   , snake_case_="<|endoftext|>"   , snake_case_=None   , snake_case_=False   , snake_case_=False   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Optional[int]									=    AddedToken(snake_case_   , lstrip=snake_case_   , rstrip=snake_case_    ) if isinstance(snake_case_   , snake_case_    ) else bos_token
        A_    :			List[Any]									=    AddedToken(snake_case_   , lstrip=snake_case_   , rstrip=snake_case_    ) if isinstance(snake_case_   , snake_case_    ) else eos_token
        A_    :			str									=    AddedToken(snake_case_   , lstrip=snake_case_   , rstrip=snake_case_    ) if isinstance(snake_case_   , snake_case_    ) else unk_token
        A_    :			Tuple									=    AddedToken(snake_case_   , lstrip=snake_case_   , rstrip=snake_case_    ) if isinstance(snake_case_   , snake_case_    ) else pad_token
        super().__init__(
            errors=snake_case_   , unk_token=snake_case_   , bos_token=snake_case_   , eos_token=snake_case_   , pad_token=snake_case_   , add_prefix_space=snake_case_   , add_bos_token=snake_case_   , **snake_case_   , )
        A_    :			str									=    add_bos_token

        with open(snake_case_   , encoding='utf-8'    ) as vocab_handle:
          A_    :			Union[str, Any]									=    json.load(snake_case_    )
        A_    :			Union[str, Any]									=    {v: k for k, v in self.encoder.items()}
        A_    :			Any									=    errors  # how to handle errors in decoding
        A_    :			List[Any]									=    bytes_to_unicode()
        A_    :			Union[str, Any]									=    {v: k for k, v in self.byte_encoder.items()}
        with open(snake_case_   , encoding='utf-8'    ) as merges_handle:
          A_    :			Any									=    merges_handle.read().split('\n'    )[1:-1]
        A_    :			Dict									=    [tuple(merge.split()    ) for merge in bpe_merges]
        A_    :			List[Any]									=    dict(zip(snake_case_   , range(len(snake_case_    )    )    )    )
        A_    :			Dict									=    {}
        A_    :			int									=    add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        A_    :			List[Any]									=    re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'    )

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return len(self.encoder    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return dict(self.encoder   , **self.added_tokens_encoder    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if token in self.cache:
          return self.cache[token]
        A_    :			Any									=    tuple(snake_case_    )
        A_    :			Dict									=    get_pairs(snake_case_    )

        if not pairs:
          return token

        while True:
          A_    :			str									=    min(snake_case_   , key=lambda snake_case_    : self.bpe_ranks.get(snake_case_   , float('inf'    )    )    )
          if bigram not in self.bpe_ranks:
            break
          A_		,			A_    :			List[str]									=    bigram
          A_    :			int									=    []
          A_    :			Union[str, Any]									=    0
          while i < len(snake_case_    ):
            try:
              A_    :			Optional[int]									=    word.index(snake_case_   , snake_case_    )
            except ValueError:
              new_word.extend(word[i:]    )
              break
            else:
              new_word.extend(word[i:j]    )
              A_    :			Tuple									=    j

            if word[i] == first and i < len(snake_case_    ) - 1 and word[i + 1] == second:
              new_word.append(first + second    )
              i += 2
            else:
              new_word.append(word[i]    )
              i += 1
          A_    :			str									=    tuple(snake_case_    )
          A_    :			Dict									=    new_word
          if len(snake_case_    ) == 1:
            break
          else:
            A_    :			Tuple									=    get_pairs(snake_case_    )
        A_    :			Any									=    ' '.join(snake_case_    )
        A_    :			Any									=    word
        return word

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        if self.add_bos_token:
          A_    :			Optional[Any]									=    [self.bos_token_id]
        else:
          A_    :			List[str]									=    []

        A_    :			Optional[int]									=    bos_token_ids + token_ids_a

        if token_ids_a is None:
          return output

        return output + bos_token_ids + token_ids_a

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			List[str]									=    []
        for token in re.findall(self.pat   , snake_case_    ):
          A_    :			str									=    ''.join(
              self.byte_encoder[b] for b in token.encode('utf-8'    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_    ).split(' '    )    )
        return bpe_tokens

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return self.encoder.get(snake_case_   , self.encoder.get(self.unk_token    )    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return self.decoder.get(snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    ''.join(snake_case_    )
        A_    :			Tuple									=    bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'   , errors=self.errors    )
        return text

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        if not os.path.isdir(snake_case_    ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
          return
        A_    :			int									=    os.path.join(
            snake_case_   , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
        A_    :			Tuple									=    os.path.join(
            snake_case_   , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

        with open(snake_case_   , 'w'   , encoding='utf-8'    ) as f:
          f.write(json.dumps(self.encoder   , indent=2   , sort_keys=snake_case_   , ensure_ascii=snake_case_    ) + '\n'    )

        A_    :			Optional[int]									=    0
        with open(snake_case_   , 'w'   , encoding='utf-8'    ) as writer:
          writer.write('#version: 0.2\n'    )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   , key=lambda snake_case_    : kv[1]    ):
            if index != token_index:
              logger.warning(
                  F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  ' Please check that the tokenizer is not corrupted!'    )
              A_    :			Dict									=    token_index
            writer.write(' '.join(snake_case_    ) + '\n'    )
            index += 1

        return vocab_file, merge_file

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=False   , **snake_case_    ):

        """simple docstring"""

        A_    :			Optional[int]									=    kwargs.pop('add_prefix_space'   , self.add_prefix_space    )
        if is_split_into_words or add_prefix_space:
          A_    :			Any									=    ' ' + text
        return (text, kwargs)

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = False   , snake_case_ = None   , snake_case_ = None   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Dict									=    super()._decode(
            token_ids=snake_case_   , skip_special_tokens=snake_case_   , clean_up_tokenization_spaces=snake_case_   , **snake_case_   , )

        if truncate_before_pattern is not None and len(snake_case_    ) > 0:
          A_    :			List[str]									=    self.truncate(snake_case_   , snake_case_    )

        return decoded_text

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        def find_re(snake_case_   , snake_case_   , snake_case_    ):
          A_    :			List[str]									=    pattern.search(snake_case_   , snake_case_    )
          return m.start() if m else -1

        A_    :			int									=    [re.compile(snake_case_   , re.MULTILINE    ) for pattern in truncate_before_pattern]

        A_    :			Optional[Any]									=    list(re.finditer('^print'   , snake_case_   , re.MULTILINE    )    )

        if len(snake_case_    ) > 1:
          A_    :			Tuple									=    completion[: prints[1].start()]

        A_    :			Any									=    list(re.finditer('^def'   , snake_case_   , re.MULTILINE    )    )

        if len(snake_case_    ) > 1:
          A_    :			int									=    completion[: defs[1].start()]

        A_    :			List[str]									=    0

        A_    :			Tuple									=    [
            pos for pos in [find_re(snake_case_   , snake_case_   , snake_case_    ) for terminal in terminals] if pos != -1
        ]

        if len(snake_case_    ) > 0:
          return completion[: min(snake_case_    )]
        else:
          return completion


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
