

import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase     :   Dict						     =  logging.get_logger(__name__)

lowerCAmelCase     :   Optional[int]						     =  '''▁'''

lowerCAmelCase     :   str						     =  {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

lowerCAmelCase     :   Any						     =  {
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

lowerCAmelCase     :   Optional[int]						     =  {
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

lowerCAmelCase     :   Dict						     =  {
    '''ernie-m-base''': 5_14,
    '''ernie-m-large''': 5_14,
}

lowerCAmelCase     :   Optional[Any]						     =  {
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}
class  _A		(  __magic_name__):
		SCREAMING_SNAKE_CASE    :      List[Any]       =  ['''input_ids''']

		SCREAMING_SNAKE_CASE    :      Dict       =  VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE    :      Optional[Any]       =  PRETRAINED_INIT_CONFIGURATION
		SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE    :      Optional[int]       =  PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE    :      Optional[Any]       =  RESOURCE_FILES_NAMES

		def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE="utf8" ,					_SCREAMING_SNAKE_CASE="[UNK]" ,					_SCREAMING_SNAKE_CASE="[SEP]" ,					_SCREAMING_SNAKE_CASE="[PAD]" ,					_SCREAMING_SNAKE_CASE="[CLS]" ,					_SCREAMING_SNAKE_CASE="[MASK]" ,					_SCREAMING_SNAKE_CASE = None ,					**_SCREAMING_SNAKE_CASE ,					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {} if sp_model_kwargs is None else sp_model_kwargs
								super().__init__(
								    do_lower_case=UpperCamelCase_ ,					unk_token=UpperCamelCase_ ,					sep_token=UpperCamelCase_ ,					pad_token=UpperCamelCase_ ,					cls_token=UpperCamelCase_ ,					mask_token=UpperCamelCase_ ,					vocab_file=UpperCamelCase_ ,					encoding=UpperCamelCase_ ,					sp_model_kwargs=self.sp_model_kwargs ,					**UpperCamelCase_ ,					)
								SCREAMING_SNAKE_CASE_				:  Dict           =     do_lower_case
								SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sentencepiece_model_ckpt
								SCREAMING_SNAKE_CASE_				:  Tuple           =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(UpperCamelCase_					)

								# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
								if vocab_file is not None:
														SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.load_vocab(filepath=UpperCamelCase_					)
								else:
														SCREAMING_SNAKE_CASE_				:  str           =     {self.sp_model.id_to_piece(UpperCamelCase_					): id for id in range(self.sp_model.get_piece_size()					)}
								SCREAMING_SNAKE_CASE_				:  List[str]           =     {v: k for k, v in self.vocab.items()}

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								if text is None:
														return None

								SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenize(UpperCamelCase_					)
								SCREAMING_SNAKE_CASE_				:  int           =     '', []

								for i, ch in enumerate(UpperCamelCase_					):
														if ch in self.SP_CHAR_MAPPING:
																				SCREAMING_SNAKE_CASE_				:  str           =     self.SP_CHAR_MAPPING.get(UpperCamelCase_					)
														else:
																				SCREAMING_SNAKE_CASE_				:  Optional[int]           =     unicodedata.normalize('NFKC' ,					UpperCamelCase_					)
														if self.is_whitespace(UpperCamelCase_					):
																				continue
														normalized_text += ch
														char_mapping.extend([i] * len(UpperCamelCase_					)					)

								SCREAMING_SNAKE_CASE_				:  Any           =     normalized_text, [], 0

								if self.do_lower_case:
														SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     text.lower()

								for token in split_tokens:
														if token[:1] == "▁":
																				SCREAMING_SNAKE_CASE_				:  Tuple           =     token[1:]
														SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     text[offset:].index(UpperCamelCase_					) + offset
														SCREAMING_SNAKE_CASE_				:  Any           =     start + len(UpperCamelCase_					)

														token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)					)
														SCREAMING_SNAKE_CASE_				:  Tuple           =     end
								return token_mapping

		@property
		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								return len(self.vocab					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







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

		def __getstate__(				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.__dict__.copy()
								SCREAMING_SNAKE_CASE_				:  List[str]           =     None
								return state

		def __setstate__(				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Any           =     d

								# for backward compatibility
								if not hasattr(self ,					'sp_model_kwargs'					):
														SCREAMING_SNAKE_CASE_				:  Tuple           =     {}

								SCREAMING_SNAKE_CASE_				:  Tuple           =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(self.sentencepiece_model_ckpt					)

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								return "".join((self.SP_CHAR_MAPPING.get(UpperCamelCase_ ,					UpperCamelCase_					) for c in text)					)

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=False ,					_SCREAMING_SNAKE_CASE=64 ,					_SCREAMING_SNAKE_CASE=0.1					):





								"""simple docstring"""







								if self.sp_model_kwargs.get('enable_sampling'					) is True:
														SCREAMING_SNAKE_CASE_				:  List[Any]           =     True
								if self.sp_model_kwargs.get('alpha'					) is not None:
														SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.sp_model_kwargs.get('alpha'					)
								if self.sp_model_kwargs.get('nbest_size'					) is not None:
														SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.sp_model_kwargs.get('nbest_size'					)

								if not enable_sampling:
														SCREAMING_SNAKE_CASE_				:  Any           =     self.sp_model.EncodeAsPieces(UpperCamelCase_					)
								else:
														SCREAMING_SNAKE_CASE_				:  Tuple           =     self.sp_model.SampleEncodeAsPieces(UpperCamelCase_ ,					UpperCamelCase_ ,					UpperCamelCase_					)
								SCREAMING_SNAKE_CASE_				:  Any           =     []
								for pi, piece in enumerate(UpperCamelCase_					):
														if piece == SPIECE_UNDERLINE:
																				if not pieces[pi + 1].startswith(UpperCamelCase_					) and pi != 0:
																										new_pieces.append(UpperCamelCase_					)
																										continue
																				else:
																										continue
														SCREAMING_SNAKE_CASE_				:  str           =     0
														for i, chunk in enumerate(UpperCamelCase_					):
																				if chunk == SPIECE_UNDERLINE:
																										continue
																				if self.is_ch_char(UpperCamelCase_					) or self.is_punct(UpperCamelCase_					):
																										if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																																new_pieces.append(piece[lst_i:i]					)
																										new_pieces.append(UpperCamelCase_					)
																										SCREAMING_SNAKE_CASE_				:  Tuple           =     i + 1
																				elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
																										if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																																new_pieces.append(piece[lst_i:i]					)
																										SCREAMING_SNAKE_CASE_				:  Any           =     i
																				elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
																										if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																																new_pieces.append(piece[lst_i:i]					)
																										SCREAMING_SNAKE_CASE_				:  Any           =     i
														if len(UpperCamelCase_					) > lst_i:
																				new_pieces.append(piece[lst_i:]					)
								return new_pieces

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  str           =     ''.join(UpperCamelCase_					).replace(UpperCamelCase_ ,					' '					).strip()
								return out_string

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  str           =     self.convert_ids_to_tokens(UpperCamelCase_					)
								SCREAMING_SNAKE_CASE_				:  int           =     ''.join(UpperCamelCase_					).replace(UpperCamelCase_ ,					' '					).strip()
								return out_string

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								return self.vocab.get(UpperCamelCase_ ,					self.vocab.get(self.unk_token					)					)

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								return self.reverse_vocab.get(UpperCamelCase_ ,					self.unk_token					)

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None					):





								"""simple docstring"""







								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								SCREAMING_SNAKE_CASE_				:  List[Any]           =     [self.cls_token_id]
								SCREAMING_SNAKE_CASE_				:  Tuple           =     [self.sep_token_id]
								return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None					):





								"""simple docstring"""







								if offset_mapping_a is None:
														return [(0, 0)] + offset_mapping_a + [(0, 0)]

								return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=False					):





								"""simple docstring"""







								if already_has_special_tokens:
														if token_ids_a is not None:
																				raise ValueError(
																				    'You should not supply a second sequence if the provided sequence of '
																				    'ids is already formatted with special tokens for the model.'					)
														return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
														return [1] + ([0] * len(UpperCamelCase_					)) + [1, 1] + ([0] * len(UpperCamelCase_					)) + [1]
								return [1] + ([0] * len(UpperCamelCase_					)) + [1]

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





								"""simple docstring"""







								if token_ids_a is None:
														# [CLS] X [SEP]
														return (len(UpperCamelCase_					) + 2) * [0]

								# [CLS] A [SEP] [SEP] B [SEP]
								return [0] * (len(UpperCamelCase_					) + 1) + [1] * (len(UpperCamelCase_					) + 3)

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								if "\u4e00" <= char <= "\u9fff":
														return True
								return False

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								if ("a" <= char <= "z") or ("A" <= char <= "Z"):
														return True
								return False

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								if char in ",;:.?!~，；：。？！《》【】":
														return True
								return False

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								if char == " " or char == "\t" or char == "\n" or char == "\r":
														return True
								if len(UpperCamelCase_					) == 1:
														SCREAMING_SNAKE_CASE_				:  Any           =     unicodedata.category(UpperCamelCase_					)
														if cat == "Zs":
																				return True
								return False

		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  List[str]           =     {}
								with io.open(UpperCamelCase_ ,					'r' ,					encoding='utf-8'					) as f:
														for index, line in enumerate(UpperCamelCase_					):
																				SCREAMING_SNAKE_CASE_				:  List[str]           =     line.rstrip('\n'					)
																				SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     int(UpperCamelCase_					)

								return token_to_idx



		def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Optional[int]           =     0
								if os.path.isdir(UpperCamelCase_					):
														SCREAMING_SNAKE_CASE_				:  Any           =     os.path.join(
														    UpperCamelCase_ ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
								else:
														SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     (filename_prefix + '-' if filename_prefix else '') + save_directory
								with open(UpperCamelCase_ ,					'w' ,					encoding='utf-8'					) as writer:
														for token, token_index in sorted(self.vocab.items() ,					key=lambda _SCREAMING_SNAKE_CASE					: kv[1]					):
																				if index != token_index:
																										logger.warning(
																										    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																										    ' Please check that the vocabulary is not corrupted!'					)
																										SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     token_index
																				writer.write(token + '\n'					)
																				index += 1

								SCREAMING_SNAKE_CASE_				:  int           =     os.path.join(UpperCamelCase_ ,					'sentencepiece.bpe.model'					)
								with open(UpperCamelCase_ ,					'wb'					) as fi:
														SCREAMING_SNAKE_CASE_				:  int           =     self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase_					)

								return (vocab_file,)









import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel

def    A_  (							a=3_2					,   a=1_0					,   a=1_0_0					,   a=1_0_2_6					,   a=True					,   a="data/tokenized_stories_train_wikitext103.jbl"					,   a="igf_context_pairs.jbl"					,   ):

      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     generate_datasets(
          a					,   a					,   number=a					,   min_len=1_0_2_6					,   trim=a  )
      # keeps model same across runs
      set_seed(4  )
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      SCREAMING_SNAKE_CASE_				:  str           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      SCREAMING_SNAKE_CASE_				:  List[str]           =     load_gpta('gpt2'  ).to(a  )
      print('computing perplexity on objective set'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     compute_perplexity(a					,   a					,   a  ).item()
      print('perplexity on objective set:'					,   a  )

      # collect igf pairs and save to file demo.jbl
      collect_objective_set(a					,   a					,   a					,   a					,   a					,   a					,   a					,   a  )

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()

def    A_  (							a					,   a=1_5					,   a=1_2_8					,   a=1_0_0					,   a="igf_model.pt"					,   ):

      """simple docstring"""



      set_seed(4_2  )

      # Load pre-trained model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     SecondaryLearner(a  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Any           =     train_secondary_learner(
          a					,   a					,   max_epochs=a					,   batch_size=a					,   eval_freq=1_0_0					,   igf_model_path=a					,   )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner

def    A_  (							a					,   a					,   a					,   a=3_2					,   a=1_0_0_0					,   a=1_6					,   a=1.0					,   a=recopy_gpta					,   a=None					,   a=1_0					,   a="gpt2_finetuned.pt"					,   ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     RandomSampler(a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(a					,   sampler=a  )

      SCREAMING_SNAKE_CASE_				:  int           =     max_steps // (len(a  )) + 1
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     0
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.zeros((1, context_len)					,   dtype=torch.long					,   device=a  )
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     recopy_model(a					,   a					,   a  )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(a  )
            secondary_learner.eval()
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  Dict           =     []

      # Compute the performance of the transformer model at the beginning
      SCREAMING_SNAKE_CASE_				:  str           =     compute_perplexity(a					,   a					,   a  )
      test_perps.append(a  )
      print('Test perplexity, step'					,   a					,   ':'					,   a  )
      for epoch in range(int(a  )  ):
            for step, example in enumerate(a  ):
                  torch.cuda.empty_cache()
                  SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     random.randint(0					,   example.size(2  ) - context_len - 1  )
                  SCREAMING_SNAKE_CASE_				:  Any           =     example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     model(a					,   labels=a  )
                  SCREAMING_SNAKE_CASE_				:  int           =     True

                  if secondary_learner is not None:
                        SCREAMING_SNAKE_CASE_				:  str           =     secondary_learner.forward(
                            torch.tensor(a					,   dtype=torch.long					,   device=a  ).unsqueeze(0  )  )[0].item()
                        observed_qs.append(float(a  )  )

                        # Here we implement the simple non-constant threshold for the predicted IG(X) value
                        # We will decay the selectivity of our secondary learner filter from
                        # 1 standard deviation above average to 1 below average after 10 batches.

                        if global_step == 1_0:
                              SCREAMING_SNAKE_CASE_				:  Any           =     -1
                        if predicted_q < threshold:
                              SCREAMING_SNAKE_CASE_				:  Tuple           =     False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()  )  )
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     outputs[0]
                        lm_loss.backward()
                        examples += 1

                  del outputs

                  # Once the batch is filled with enough contexts, backprop on the batch.
                  if examples == batch_size:
                        torch.cuda.empty_cache()
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     0
                        # Do LM backprop
                        torch.nn.utils.clip_grad_norm_(model.parameters()					,   3.0  )
                        lm_optimizer.step()
                        lm_scheduler.step()  # Update learning rate schedule
                        global_step += 1
                        # Compute the performance of the transformer model at this batch
                        if global_step % eval_interval == 0:
                              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     compute_perplexity(a					,   a					,   a  )
                              test_perps.append(a  )

                              print('Test perplexity, step'					,   a					,   ':'					,   a  )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 6_0:
                        break
            if max_steps > 0 and global_step > 6_0:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()					,   a  )
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

      # Required parameters
      parser.add_argument(
          '--data_dir'					,   default=a					,   type=a					,   required=a					,   help='The input data dir. Should contain data files for WikiText.'					,   )
      parser.add_argument(
          '--model_name_or_path'					,   default=a					,   type=a					,   required=a					,   help='Path to pretrained model or model identifier from huggingface.co/models'					,   )
      parser.add_argument(
          '--data_file'					,   type=a					,   default=a					,   help=(
              'A jbl file containing tokenized data which can be split as objective dataset, '
              'train_dataset and test_dataset.'
          )					,   )

      parser.add_argument(
          '--igf_data_file'					,   type=a					,   default=a					,   help='A jbl file containing the context and information gain pairs to train secondary learner.'					,   )

      parser.add_argument(
          '--output_dir'					,   default=a					,   type=a					,   required=a					,   help='The output directory where the final fine-tuned model is stored.'					,   )

      parser.add_argument(
          '--tokenizer_name'					,   default=a					,   type=a					,   help='Pretrained tokenizer name or path if not the same as model_name'					,   )
      parser.add_argument('--seed'					,   type=a					,   default=a					,   help='A seed for reproducible training.'  )

      parser.add_argument(
          '--context_len'					,   default=3_2					,   type=a					,   help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )					,   )

      parser.add_argument(
          '--size_objective_set'					,   default=1_0_0					,   type=a					,   help='number of articles that are long enough to be used as our objective set'					,   )
      parser.add_argument(
          '--eval_freq'					,   default=1_0_0					,   type=a					,   help='secondary model evaluation is triggered at eval_freq'  )

      parser.add_argument('--max_steps'					,   default=1_0_0_0					,   type=a					,   help='To calculate training epochs'  )

      parser.add_argument(
          '--secondary_learner_batch_size'					,   default=1_2_8					,   type=a					,   help='batch size of training data for secondary learner'					,   )

      parser.add_argument(
          '--batch_size'					,   default=1_6					,   type=a					,   help='batch size of training data of language model(gpt2) '  )

      parser.add_argument(
          '--eval_interval'					,   default=1_0					,   type=a					,   help=(
              'decay the selectivity of our secondary learner filter from'
              '1 standard deviation above average to 1 below average after 10 batches'
          )					,   )

      parser.add_argument(
          '--number'					,   default=1_0_0					,   type=a					,   help='The number of examples split to be used as objective_set/test_data'  )

      parser.add_argument(
          '--min_len'					,   default=1_0_2_6					,   type=a					,   help='The minimum length of the article to be used as objective set'  )

      parser.add_argument(
          '--secondary_learner_max_epochs'					,   default=1_5					,   type=a					,   help='number of epochs to train secondary learner'  )

      parser.add_argument('--trim'					,   default=a					,   type=a					,   help='truncate the example if it exceeds context length'  )

      parser.add_argument(
          '--threshold'					,   default=1.0					,   type=a					,   help=(
              'The threshold value used by secondary learner to filter the train_data and allow only'
              ' informative data as input to the model'
          )					,   )

      parser.add_argument('--finetuned_model_name'					,   default='gpt2_finetuned.pt'					,   type=a					,   help='finetuned_model_name'  )

      parser.add_argument(
          '--recopy_model'					,   default=a					,   type=a					,   help='Reset the model to the original pretrained GPT-2 weights after each iteration'					,   )

      # function calls
      # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
      generate_n_pairs(
          context_len=3_2					,   max_steps=1_0					,   size_objective_set=1_0_0					,   min_len=1_0_2_6					,   trim=a					,   data_file='data/tokenized_stories_train_wikitext103.jbl'					,   igf_data_file='igf_context_pairs.jbl'					,   )

      # Load train data for secondary learner
      SCREAMING_SNAKE_CASE_				:  Tuple           =     joblib.load('data/IGF_values.jbl'  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     training_secondary_learner(
          a					,   secondary_learner_max_epochs=1_5					,   secondary_learner_batch_size=1_2_8					,   eval_freq=1_0_0					,   igf_model_path='igf_model.pt'					,   )

      # load pretrained gpt2 model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(4_2  )

      # Generate train and test data to train and evaluate gpt2 model
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     generate_datasets(
          context_len=3_2					,   file='data/tokenized_stories_train_wikitext103.jbl'					,   number=1_0_0					,   min_len=1_0_2_6					,   trim=a  )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          a					,   a					,   a					,   context_len=3_2					,   max_steps=1_0_0_0					,   batch_size=1_6					,   threshold=1.0					,   recopy_model=a					,   secondary_learner=a					,   eval_interval=1_0					,   finetuned_model_name='gpt2_finetuned.pt'					,   )


if __name__ == "__main__":
    main()






