

'''simple docstring'''
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_snake_case         =  'src/diffusers'

# Matches is_xxx_available()
_snake_case         =  re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
_snake_case         =  re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


_snake_case         =  '\n{0} = None\n'

_snake_case         =  '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


_snake_case         =  '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'







def  _A		(     snake_case					)      ->     int:
   _lowercase      :				Dict      					=       _re_backend.findall(snake_case					)
   if len(snake_case					) == 0:
      return None

   return "_and_".join(snake_case					)







def  _A		(     )      ->     int:
   with open(os.path.join(snake_case		,							"__init__.py"					)		,							"r"		,							encoding="utf-8"		,							newline="\n"					) as f:
      _lowercase      :				List[str]      					=       f.readlines()

   # Get to the point we do the actual imports for type checking
   _lowercase      :				Any      					=       0
   _lowercase      :				List[str]      					=       {}
   # Go through the end of the file
   while line_index < len(snake_case					):
      # If the line contains is_backend_available, we grab all objects associated with the `else` block
      _lowercase      :				List[Any]      					=       find_backend(lines[line_index]					)
      if backend is not None:
         while not lines[line_index].startswith("else:"					):
            line_index += 1
         line_index += 1
         _lowercase      :				Tuple      					=       []
         # Until we unindent, add backend objects to the list
         while line_index < len(snake_case					) and len(lines[line_index]					) > 1:
            _lowercase      :				Dict      					=       lines[line_index]
            _lowercase      :				Union[str, Any]      					=       _re_single_line_import.search(snake_case					)
            if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(", "					)					)
            elif line.startswith(" " * 8					):
               objects.append(line[8:-2]					)
            line_index += 1

         if len(snake_case					) > 0:
            _lowercase      :				Dict      					=       objects
      else:
         line_index += 1

   return backend_specific_objects







def  _A		(     snake_case		,							snake_case					)      ->     Optional[int]:
   if name.isupper():
      return DUMMY_CONSTANT.format(snake_case					)
   elif name.islower():
      return DUMMY_FUNCTION.format(snake_case		,							snake_case					)
   else:
      return DUMMY_CLASS.format(snake_case		,							snake_case					)







def  _A		(     snake_case=None					)      ->     Any:
   if backend_specific_objects is None:
      _lowercase      :				Optional[int]      					=       read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   _lowercase      :				List[str]      					=       {}

   for backend, objects in backend_specific_objects.items():
      _lowercase      :				int      					=       "[" + ", ".join(F'''"{b}"''' for b in backend.split("_and_"					)					) + "]"
      _lowercase      :				Optional[int]      					=       "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(snake_case		,							snake_case					) for o in objects]					)
      _lowercase      :				Optional[int]      					=       dummy_file

   return dummy_files







def  _A		(     snake_case=False					)      ->     Optional[int]:
   _lowercase      :				List[Any]      					=       create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   _lowercase      :				Optional[Any]      					=       {"torch": "pt"}

   # Locate actual dummy modules and read their content.
   _lowercase      :				int      					=       os.path.join(snake_case		,							"utils"					)
   _lowercase      :				Optional[Any]      					=       {
       backend: os.path.join(snake_case		,							F'''dummy_{short_names.get(snake_case		,							snake_case					)}_objects.py'''					)
       for backend in dummy_files.keys()
   }

   _lowercase      :				Optional[int]      					=       {}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(snake_case					):
         with open(snake_case		,							"r"		,							encoding="utf-8"		,							newline="\n"					) as f:
            _lowercase      :				Optional[Any]      					=       f.read()
      else:
         _lowercase      :				Any      					=       ""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                F'''Updating diffusers.utils.dummy_{short_names.get(snake_case		,							snake_case					)}_objects.py as the main '''
                "__init__ has new objects."					)
            with open(dummy_file_paths[backend]		,							"w"		,							encoding="utf-8"		,							newline="\n"					) as f:
               f.write(dummy_files[backend]					)
         else:
            raise ValueError(
                "The main __init__ has objects that are not present in "
                F'''diffusers.utils.dummy_{short_names.get(snake_case		,							snake_case					)}_objects.py. Run `make fix-copies` '''
                "to fix this."					)


if __name__ == "__main__":
    _snake_case         =  argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    _snake_case         =  parser.parse_args()

    check_dummies(args.fix_and_overwrite)









'''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()






