

'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					str     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					int     	=							('BertTokenizer', 'BertTokenizerFast')


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       False
      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				str      					=       self.image_processor


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify either images or text."  )

      # Get only text
      if images is None:
         _lowercase      :				List[Any]      					=       self.tokenizer
         _lowercase      :				Optional[Any]      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         return text_encoding

      # add pixel_values
      _lowercase      :				Any      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )

      if text is not None:
         _lowercase      :				Optional[Any]      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
      else:
         _lowercase      :				int      					=       None

      if text_encoding is not None:
         encoding_image_processor.update(_UpperCamelCase  )

      return encoding_image_processor


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.batch_decode(*_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      return self.tokenizer.decode(*_UpperCamelCase      ,    **_UpperCamelCase  )






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       self.tokenizer.model_input_names
      _lowercase      :				Any      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )









'''simple docstring'''
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		(     snake_case=32		,							snake_case=10		,							snake_case=1_00		,							snake_case=10_26		,							snake_case=True		,							snake_case="data/tokenized_stories_train_wikitext103.jbl"		,							snake_case="igf_context_pairs.jbl"		,							)      ->     Optional[int]:
   set_seed(3					)
   # generate train_data and objective_set
   _lowercase					,				_lowercase      :				List[str]      					=       generate_datasets(
       snake_case		,							snake_case		,							number=snake_case		,							min_len=10_26		,							trim=snake_case					)
   # 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?
   _lowercase      :				int      					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)

   # load pretrained model
   _lowercase      :				str      					=       load_gpta("gpt2"					).to(snake_case					)
   print("computing perplexity on objective set"					)
   _lowercase      :				Dict      					=       compute_perplexity(snake_case		,							snake_case		,							snake_case					).item()
   print("perplexity on objective set:"		,							snake_case					)

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

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







def  _A		(     snake_case		,							snake_case=15		,							snake_case=1_28		,							snake_case=1_00		,							snake_case="igf_model.pt"		,							)      ->     Optional[Any]:
   set_seed(42					)

   # Load pre-trained model
   _lowercase      :				Tuple      					=       GPTaLMHeadModel.from_pretrained("gpt2"					)

   # Initialize secondary learner to use embedding weights of model
   _lowercase      :				Any      					=       SecondaryLearner(snake_case					)

   # Train secondary learner
   _lowercase      :				Any      					=       train_secondary_learner(
       snake_case		,							snake_case		,							max_epochs=snake_case		,							batch_size=snake_case		,							eval_freq=1_00		,							igf_model_path=snake_case		,							)

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case=32		,							snake_case=10_00		,							snake_case=16		,							snake_case=1.0		,							snake_case=recopy_gpta		,							snake_case=None		,							snake_case=10		,							snake_case="gpt2_finetuned.pt"		,							)      ->     Dict:
   _lowercase      :				str      					=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)
   _lowercase      :				int      					=       RandomSampler(snake_case					)
   _lowercase      :				int      					=       DataLoader(snake_case		,							sampler=snake_case					)

   _lowercase      :				Tuple      					=       max_steps // (len(snake_case					)) + 1
   _lowercase      :				Dict      					=       0
   _lowercase      :				Union[str, Any]      					=       torch.zeros((1, context_len)		,							dtype=torch.long		,							device=snake_case					)
   _lowercase					,				_lowercase					,				_lowercase      :				Union[str, Any]      					=       recopy_model(snake_case		,							snake_case		,							snake_case					)

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(snake_case					)
      secondary_learner.eval()
   _lowercase      :				Optional[Any]      					=       []
   _lowercase      :				Tuple      					=       0

   _lowercase      :				int      					=       []
   _lowercase      :				Optional[Any]      					=       []

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

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

            # 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 == 10:
               _lowercase      :				Optional[Any]      					=       -1
            if predicted_q < threshold:
               _lowercase      :				List[str]      					=       False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()					)					)
            _lowercase      :				Dict      					=       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()
            _lowercase      :				Optional[Any]      					=       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:
               _lowercase      :				Optional[Any]      					=       compute_perplexity(snake_case		,							snake_case		,							snake_case					)
               test_perps.append(snake_case					)

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

    # save finetuned transformer model
   torch.save(model.state_dict()		,							snake_case					)
   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		(     )      ->     Union[str, Any]:
   _lowercase      :				Optional[Any]      					=       argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"					)

   # Required parameters
   parser.add_argument(
       "--data_dir"		,							default=snake_case		,							type=snake_case		,							required=snake_case		,							help="The input data dir. Should contain data files for WikiText."		,							)
   parser.add_argument(
       "--model_name_or_path"		,							default=snake_case		,							type=snake_case		,							required=snake_case		,							help="Path to pretrained model or model identifier from huggingface.co/models"		,							)
   parser.add_argument(
       "--data_file"		,							type=snake_case		,							default=snake_case		,							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=snake_case		,							default=snake_case		,							help="A jbl file containing the context and information gain pairs to train secondary learner."		,							)

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

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

   parser.add_argument(
       "--context_len"		,							default=32		,							type=snake_case		,							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_00		,							type=snake_case		,							help="number of articles that are long enough to be used as our objective set"		,							)
   parser.add_argument(
       "--eval_freq"		,							default=1_00		,							type=snake_case		,							help="secondary model evaluation is triggered at eval_freq"					)

   parser.add_argument("--max_steps"		,							default=10_00		,							type=snake_case		,							help="To calculate training epochs"					)

   parser.add_argument(
       "--secondary_learner_batch_size"		,							default=1_28		,							type=snake_case		,							help="batch size of training data for secondary learner"		,							)

   parser.add_argument(
       "--batch_size"		,							default=16		,							type=snake_case		,							help="batch size of training data of language model(gpt2) "					)

   parser.add_argument(
       "--eval_interval"		,							default=10		,							type=snake_case		,							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_00		,							type=snake_case		,							help="The number of examples split to be used as objective_set/test_data"					)

   parser.add_argument(
       "--min_len"		,							default=10_26		,							type=snake_case		,							help="The minimum length of the article to be used as objective set"					)

   parser.add_argument(
       "--secondary_learner_max_epochs"		,							default=15		,							type=snake_case		,							help="number of epochs to train secondary learner"					)

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

   parser.add_argument(
       "--threshold"		,							default=1.0		,							type=snake_case		,							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=snake_case		,							help="finetuned_model_name"					)

   parser.add_argument(
       "--recopy_model"		,							default=snake_case		,							type=snake_case		,							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=32		,							max_steps=10		,							size_objective_set=1_00		,							min_len=10_26		,							trim=snake_case		,							data_file="data/tokenized_stories_train_wikitext103.jbl"		,							igf_data_file="igf_context_pairs.jbl"		,							)

   # Load train data for secondary learner
   _lowercase      :				Any      					=       joblib.load("data/IGF_values.jbl"					)

   # Train secondary learner
   _lowercase      :				Union[str, Any]      					=       training_secondary_learner(
       snake_case		,							secondary_learner_max_epochs=15		,							secondary_learner_batch_size=1_28		,							eval_freq=1_00		,							igf_model_path="igf_model.pt"		,							)

   # load pretrained gpt2 model
   _lowercase      :				Optional[Any]      					=       GPTaLMHeadModel.from_pretrained("gpt2"					)
   set_seed(42					)

   # Generate train and test data to train and evaluate gpt2 model
   _lowercase					,				_lowercase      :				Optional[Any]      					=       generate_datasets(
       context_len=32		,							file="data/tokenized_stories_train_wikitext103.jbl"		,							number=1_00		,							min_len=10_26		,							trim=snake_case					)

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       snake_case		,							snake_case		,							snake_case		,							context_len=32		,							max_steps=10_00		,							batch_size=16		,							threshold=1.0		,							recopy_model=snake_case		,							secondary_learner=snake_case		,							eval_interval=10		,							finetuned_model_name="gpt2_finetuned.pt"		,							)


if __name__ == "__main__":
    main()






