





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def _A       (   __magic_name__  , __magic_name__=0.999  , __magic_name__="cosine"  , ):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__magic_name__	):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__magic_name__	):
         return math.exp(t * -12.0	)

   else:
      raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

   lowercase__			       =  []
   for i in range(__magic_name__	):
      lowercase__			       =  i / num_diffusion_timesteps
      lowercase__			       =  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__magic_name__	) / alpha_bar_fn(__magic_name__	)  , __magic_name__	)	)
   return torch.tensor(__magic_name__  , dtype=torch.floataa	)







class  lowerCAmelCase							(       lowercase_							,							lowercase_ ):
  __lowerCamelCase		=      [e.name for e in KarrasDiffusionSchedulers]
  __lowerCamelCase		=      2







  @register_to_config
  def __init__(      self  :Optional[int]   ,							_lowercase  :int = 10_00   ,							_lowercase  :float = 0.00085   ,							_lowercase  :float = 0.012   ,							_lowercase  :str = "linear"   ,							_lowercase  :Optional[Union[np.ndarray, List[float]]] = None   ,							_lowercase  :str = "epsilon"   ,							_lowercase  :str = "linspace"   ,							_lowercase  :int = 0   ,							):






     '''simple docstring'''





     if trained_betas is not None:
        lowercase__			       =  torch.tensor(_lowercase   ,							dtype=torch.floataa )
     elif beta_schedule == "linear":
        lowercase__			       =  torch.linspace(_lowercase   ,							_lowercase   ,							_lowercase   ,							dtype=torch.floataa )
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        lowercase__			       =  (
            torch.linspace(beta_start**0.5   ,							beta_end**0.5   ,							_lowercase   ,							dtype=torch.floataa ) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        lowercase__			       =  betas_for_alpha_bar(_lowercase )
     else:
        raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

     lowercase__			       =  1.0 - self.betas
     lowercase__			       =  torch.cumprod(self.alphas   ,							dim=0 )

     #  set all values
     self.set_timesteps(_lowercase   ,							_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Any   ,							_lowercase  :Tuple   ,							_lowercase  :List[Any]=None ):






     '''simple docstring'''





     if schedule_timesteps is None:
        lowercase__			       =  self.timesteps

     lowercase__			       =  (schedule_timesteps == timestep).nonzero()

     # The sigma index that is taken for the **very** first `step`
     # is always the second index (or the last index if there is only 1)
     # This way we can ensure we don't accidentally skip a sigma in
     # case we start in the middle of the denoising schedule (e.g. for image-to-image)
     if len(self._index_counter ) == 0:
        lowercase__			       =  1 if len(_lowercase ) > 1 else 0
     else:
        lowercase__			       =  timestep.cpu().item() if torch.is_tensor(_lowercase ) else timestep
        lowercase__			       =  self._index_counter[timestep_int]

     return indices[pos].item()







  @property
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     if self.config.timestep_spacing in ["linspace", "trailing"]:
        return self.sigmas.max()

     return (self.sigmas.max() ** 2 + 1) ** 0.5







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :Union[float, torch.FloatTensor]   ,							):






     '''simple docstring'''





     lowercase__			       =  self.index_for_timestep(_lowercase )

     if self.state_in_first_order:
        lowercase__			       =  self.sigmas[step_index]
     else:
        lowercase__			       =  self.sigmas_interpol[step_index]

     lowercase__			       =  sample / ((sigma**2 + 1) ** 0.5)
     return sample







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :int   ,							_lowercase  :Union[str, torch.device] = None   ,							_lowercase  :Optional[int] = None   ,							):






     '''simple docstring'''





     lowercase__			       =  num_inference_steps

     lowercase__			       =  num_train_timesteps or self.config.num_train_timesteps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
        lowercase__			       =  np.linspace(0   ,							num_train_timesteps - 1   ,							_lowercase   ,							dtype=_lowercase )[::-1].copy()
     elif self.config.timestep_spacing == "leading":
        lowercase__			       =  num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        lowercase__			       =  (np.arange(0   ,							_lowercase ) * step_ratio).round()[::-1].copy().astype(_lowercase )
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        lowercase__			       =  num_train_timesteps / self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        lowercase__			       =  (np.arange(_lowercase   ,							0   ,							-step_ratio )).round().copy().astype(_lowercase )
        timesteps -= 1
     else:
        raise ValueError(
            f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

     lowercase__			       =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
     lowercase__			       =  torch.from_numpy(np.log(_lowercase ) ).to(_lowercase )

     lowercase__			       =  np.interp(_lowercase   ,							np.arange(0   ,							len(_lowercase ) )   ,							_lowercase )
     lowercase__			       =  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
     lowercase__			       =  torch.from_numpy(_lowercase ).to(device=_lowercase )

     # interpolate sigmas
     lowercase__			       =  sigmas.log().lerp(sigmas.roll(1 ).log()   ,							0.5 ).exp()

     lowercase__			       =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
     lowercase__			       =  torch.cat(
         [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

     if str(_lowercase ).startswith("mps" ):
        # mps does not support float64
        lowercase__			       =  torch.from_numpy(_lowercase ).to(_lowercase   ,							dtype=torch.floataa )
     else:
        lowercase__			       =  torch.from_numpy(_lowercase ).to(_lowercase )

     # interpolate timesteps
     lowercase__			       =  self.sigma_to_t(_lowercase ).to(_lowercase   ,							dtype=timesteps.dtype )
     lowercase__			       =  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])   ,							dim=-1 ).flatten()

     lowercase__			       =  torch.cat([timesteps[:1], interleaved_timesteps] )

     lowercase__			       =  None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     lowercase__			       =  defaultdict(_lowercase )







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__			       =  sigma.log()

     # get distribution
     lowercase__			       =  log_sigma - self.log_sigmas[:, None]

     # get sigmas range
     lowercase__			       =  dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
     lowercase__			       =  low_idx + 1

     lowercase__			       =  self.log_sigmas[low_idx]
     lowercase__			       =  self.log_sigmas[high_idx]

     # interpolate sigmas
     lowercase__			       =  (low - log_sigma) / (low - high)
     lowercase__			       =  w.clamp(0   ,							1 )

     # transform interpolation to time range
     lowercase__			       =  (1 - w) * low_idx + w * high_idx
     lowercase__			       =  t.view(sigma.shape )
     return t







  @property
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     return self.sample is None







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :Union[torch.FloatTensor, np.ndarray]   ,							_lowercase  :Union[float, torch.FloatTensor]   ,							_lowercase  :Union[torch.FloatTensor, np.ndarray]   ,							_lowercase  :bool = True   ,							):






     '''simple docstring'''





     lowercase__			       =  self.index_for_timestep(_lowercase )

     # advance index counter by 1
     lowercase__			       =  timestep.cpu().item() if torch.is_tensor(_lowercase ) else timestep
     self._index_counter[timestep_int] += 1

     if self.state_in_first_order:
        lowercase__			       =  self.sigmas[step_index]
        lowercase__			       =  self.sigmas_interpol[step_index + 1]
        lowercase__			       =  self.sigmas[step_index + 1]
     else:
        # 2nd order / KDPM2's method
        lowercase__			       =  self.sigmas[step_index - 1]
        lowercase__			       =  self.sigmas_interpol[step_index]
        lowercase__			       =  self.sigmas[step_index]

     # currently only gamma=0 is supported. This usually works best anyways.
     # We can support gamma in the future but then need to scale the timestep before
     # passing it to the model which requires a change in API
     lowercase__			       =  0
     lowercase__			       =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        lowercase__			       =  sigma_hat if self.state_in_first_order else sigma_interpol
        lowercase__			       =  sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
        lowercase__			       =  sigma_hat if self.state_in_first_order else sigma_interpol
        lowercase__			       =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
            sample / (sigma_input**2 + 1)
        )
     elif self.config.prediction_type == "sample":
        raise NotImplementedError("prediction_type not implemented yet: sample" )
     else:
        raise ValueError(
            f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

     if self.state_in_first_order:
        # 2. Convert to an ODE derivative for 1st order
        lowercase__			       =  (sample - pred_original_sample) / sigma_hat
        # 3. delta timestep
        lowercase__			       =  sigma_interpol - sigma_hat

        # store for 2nd order step
        lowercase__			       =  sample
     else:
        # DPM-Solver-2
        # 2. Convert to an ODE derivative for 2nd order
        lowercase__			       =  (sample - pred_original_sample) / sigma_interpol

        # 3. delta timestep
        lowercase__			       =  sigma_next - sigma_hat

        lowercase__			       =  self.sample
        lowercase__			       =  None

     lowercase__			       =  sample + derivative * dt

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=_lowercase )







  def   UpperCAmelCase					(      self  :str   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :torch.FloatTensor   ,							_lowercase  :torch.FloatTensor   ,							):






     '''simple docstring'''





     lowercase__			       =  self.sigmas.to(device=original_samples.device   ,							dtype=original_samples.dtype )
     if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase ):
        # mps does not support float64
        lowercase__			       =  self.timesteps.to(original_samples.device   ,							dtype=torch.floataa )
        lowercase__			       =  timesteps.to(original_samples.device   ,							dtype=torch.floataa )
     else:
        lowercase__			       =  self.timesteps.to(original_samples.device )
        lowercase__			       =  timesteps.to(original_samples.device )

     lowercase__			       =  [self.index_for_timestep(_lowercase   ,							_lowercase ) for t in timesteps]

     lowercase__			       =  sigmas[step_indices].flatten()
     while len(sigma.shape ) < len(original_samples.shape ):
        lowercase__			       =  sigma.unsqueeze(-1 )

     lowercase__			       =  original_samples + noise * sigma
     return noisy_samples







  def __len__(      self  :Any ):






     '''simple docstring'''





     return self.config.num_train_timesteps













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       (   __magic_name__=32  , __magic_name__=10  , __magic_name__=100  , __magic_name__=1026  , __magic_name__=True  , __magic_name__="data/tokenized_stories_train_wikitext103.jbl"  , __magic_name__="igf_context_pairs.jbl"  , ):
   set_seed(3	)
   # generate train_data and objective_set
   lowercase__       ,							lowercase__			       =  generate_datasets(
       __magic_name__  , __magic_name__  , number=__magic_name__  , min_len=1026  , trim=__magic_name__	)
   # 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__			       =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)

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

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

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


def _A       (   __magic_name__  , __magic_name__=15  , __magic_name__=128  , __magic_name__=100  , __magic_name__="igf_model.pt"  , ):
   set_seed(42	)

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

   # Initialize secondary learner to use embedding weights of model
   lowercase__			       =  SecondaryLearner(__magic_name__	)

   # Train secondary learner
   lowercase__			       =  train_secondary_learner(
       __magic_name__  , __magic_name__  , max_epochs=__magic_name__  , batch_size=__magic_name__  , eval_freq=100  , igf_model_path=__magic_name__  , )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__=32  , __magic_name__=1000  , __magic_name__=16  , __magic_name__=1.0  , __magic_name__=recopy_gpta  , __magic_name__=None  , __magic_name__=10  , __magic_name__="gpt2_finetuned.pt"  , ):
   lowercase__			       =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
   lowercase__			       =  RandomSampler(__magic_name__	)
   lowercase__			       =  DataLoader(__magic_name__  , sampler=__magic_name__	)

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

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

   lowercase__			       =  []
   lowercase__			       =  []

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

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

            # 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__			       =  -1
            if predicted_q < threshold:
               lowercase__			       =  False

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

               print("Test perplexity, step"  , __magic_name__  , ":"  , __magic_name__	)
            # 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()  , __magic_name__	)
   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       (   ):
   lowercase__			       =  argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"	)

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

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

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

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

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

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

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

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

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

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

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

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

   parser.add_argument(
       "--recopy_model"  , default=__magic_name__  , type=__magic_name__  , 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=__magic_name__  , data_file="data/tokenized_stories_train_wikitext103.jbl"  , igf_data_file="igf_context_pairs.jbl"  , )

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

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

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

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

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


if __name__ == "__main__":
  main()






