






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        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]=32     , UpperCamelCase_:							int=10     , UpperCamelCase_:							str=100     , UpperCamelCase_:							Tuple=1026     , UpperCamelCase_:							str=True     , UpperCamelCase_:							List[Any]="data/tokenized_stories_train_wikitext103.jbl"     , UpperCamelCase_:							Optional[Any]="igf_context_pairs.jbl"     , )       ->							int:







  '''simple docstring'''







  set_seed(3					)
  # generate train_data and objective_set
  _a			,	_a							=	generate_datasets(
      UpperCamelCase_     , UpperCamelCase_     , number=UpperCamelCase_     , min_len=1026     , trim=UpperCamelCase_					)
  # 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?
  _a							=	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)

  # load pretrained model
  _a							=	load_gpta("gpt2"					).to(UpperCamelCase_					)
  print("computing perplexity on objective set"					)
  _a							=	compute_perplexity(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					).item()
  print("perplexity on objective set:"     , UpperCamelCase_					)

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

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






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							Union[str, Any]=15     , UpperCamelCase_:							Dict=128     , UpperCamelCase_:							int=100     , UpperCamelCase_:							Optional[int]="igf_model.pt"     , )       ->							Tuple:







  '''simple docstring'''







  set_seed(42					)

  # Load pre-trained model
  _a							=	GPTaLMHeadModel.from_pretrained("gpt2"					)

  # Initialize secondary learner to use embedding weights of model
  _a							=	SecondaryLearner(UpperCamelCase_					)

  # Train secondary learner
  _a							=	train_secondary_learner(
      UpperCamelCase_     , UpperCamelCase_     , max_epochs=UpperCamelCase_     , batch_size=UpperCamelCase_     , eval_freq=100     , igf_model_path=UpperCamelCase_     , )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner






def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							str     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Tuple=32     , UpperCamelCase_:							Tuple=1000     , UpperCamelCase_:							Optional[Any]=16     , UpperCamelCase_:							List[str]=1.0     , UpperCamelCase_:							Optional[Any]=recopy_gpta     , UpperCamelCase_:							List[Any]=None     , UpperCamelCase_:							Optional[Any]=10     , UpperCamelCase_:							Dict="gpt2_finetuned.pt"     , )       ->							Optional[Any]:







  '''simple docstring'''







  _a							=	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)
  _a							=	RandomSampler(UpperCamelCase_					)
  _a							=	DataLoader(UpperCamelCase_     , sampler=UpperCamelCase_					)

  _a							=	max_steps // (len(UpperCamelCase_					)) + 1
  _a							=	0
  _a							=	torch.zeros((1, context_len)     , dtype=torch.long     , device=UpperCamelCase_					)
  _a			,	_a			,	_a							=	recopy_model(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(UpperCamelCase_					)
    secondary_learner.eval()
  _a							=	[]
  _a							=	0

  _a							=	[]
  _a							=	[]

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

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

        # 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:
          _a							=	-1
        if predicted_q < threshold:
          _a							=	False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()					)					)
        _a							=	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()
        _a							=	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:
          _a							=	compute_perplexity(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
          test_perps.append(UpperCamelCase_					)

          print("Test perplexity, step"     , UpperCamelCase_     , ":"     , UpperCamelCase_					)
            # 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()     , UpperCamelCase_					)
  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        lowerCAmelCase (      )       ->							Union[str, Any]:







  '''simple docstring'''







  _a							=	argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"					)

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

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

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

  parser.add_argument(
      "--context_len"     , default=32     , type=UpperCamelCase_     , 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=100     , type=UpperCamelCase_     , help="number of articles that are long enough to be used as our objective set"     , )
  parser.add_argument(
      "--eval_freq"     , default=100     , type=UpperCamelCase_     , help="secondary model evaluation is triggered at eval_freq"					)

  parser.add_argument("--max_steps"     , default=1000     , type=UpperCamelCase_     , help="To calculate training epochs"					)

  parser.add_argument(
      "--secondary_learner_batch_size"     , default=128     , type=UpperCamelCase_     , help="batch size of training data for secondary learner"     , )

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

  parser.add_argument(
      "--eval_interval"     , default=10     , type=UpperCamelCase_     , 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=100     , type=UpperCamelCase_     , help="The number of examples split to be used as objective_set/test_data"					)

  parser.add_argument(
      "--min_len"     , default=1026     , type=UpperCamelCase_     , help="The minimum length of the article to be used as objective set"					)

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

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

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

  parser.add_argument(
      "--recopy_model"     , default=UpperCamelCase_     , type=UpperCamelCase_     , 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=100     , min_len=1026     , trim=UpperCamelCase_     , data_file="data/tokenized_stories_train_wikitext103.jbl"     , igf_data_file="igf_context_pairs.jbl"     , )

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

  # Train secondary learner
  _a							=	training_secondary_learner(
      UpperCamelCase_     , secondary_learner_max_epochs=15     , secondary_learner_batch_size=128     , eval_freq=100     , igf_model_path="igf_model.pt"     , )

  # load pretrained gpt2 model
  _a							=	GPTaLMHeadModel.from_pretrained("gpt2"					)
  set_seed(42					)

  # Generate train and test data to train and evaluate gpt2 model
  _a			,	_a							=	generate_datasets(
      context_len=32     , file="data/tokenized_stories_train_wikitext103.jbl"     , number=100     , min_len=1026     , trim=UpperCamelCase_					)

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , context_len=32     , max_steps=1000     , batch_size=16     , threshold=1.0     , recopy_model=UpperCamelCase_     , secondary_learner=UpperCamelCase_     , eval_interval=10     , finetuned_model_name="gpt2_finetuned.pt"     , )


if __name__ == "__main__":
     main()










"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		str       			=				'''Salesforce/blip-image-captioning-base'''
      A__	:		Optional[int]       			=				(
          '''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
          '''image to caption, and returns a text that contains the description in English.'''
      )
      A__	:		str       			=				'''image_captioner'''
      A__	:		Tuple       			=				AutoModelForVisionaSeq

      A__	:		Tuple       			=				['''image''']
      A__	:		List[Any]       			=				['''text''']






      def __init__(    self     ,     *a_     ,     **a_	)	->Union[str, Any]:







        '''simple docstring'''


        requires_backends(self     ,     ["vision"]	)
        super().__init__(*a_     ,     **a_	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->Union[str, Any]:







        '''simple docstring'''


        return self.pre_processor(images=a_     ,     return_tensors="pt"	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->List[Any]:







        '''simple docstring'''


        return self.model.generate(**a_	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->Tuple:







        '''simple docstring'''


        return self.pre_processor.batch_decode(a_     ,     skip_special_tokens=a_	)[0].strip()


