'''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  UpperCamelCase__					(					_lowercase    :				Optional[Any]=3_2   ,    _lowercase    :				Tuple=1_0   ,    _lowercase    :				List[Any]=1_0_0   ,    _lowercase    :				Optional[Any]=1_0_2_6   ,    _lowercase    :				Any=True   ,    _lowercase    :				Optional[int]="data/tokenized_stories_train_wikitext103.jbl"   ,    _lowercase    :				Dict="igf_context_pairs.jbl"   ,    )							->	Any:
  set_seed(3     )
  # generate train_data and objective_set
  __UpperCAmelCase:					Optional[Any]		= generate_datasets(
      _lowercase   ,    _lowercase   ,    number=_lowercase   ,    min_len=1_0_2_6   ,    trim=_lowercase     )
  # 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?
  __UpperCAmelCase:					Optional[int]		= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )

  # load pretrained model
  __UpperCAmelCase:					Optional[Any]		= load_gpta("""gpt2"""     ).to(_lowercase     )
  print("""computing perplexity on objective set"""     )
  __UpperCAmelCase:					Union[str, Any]		= compute_perplexity(_lowercase   ,    _lowercase   ,    _lowercase     ).item()
  print("""perplexity on objective set:"""   ,    _lowercase     )

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

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






def  UpperCamelCase__					(					_lowercase    :				Any   ,    _lowercase    :				Dict=1_5   ,    _lowercase    :				Dict=1_2_8   ,    _lowercase    :				Optional[int]=1_0_0   ,    _lowercase    :				Optional[int]="igf_model.pt"   ,    )							->	Optional[Any]:
  set_seed(4_2     )

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

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

  # Train secondary learner
  __UpperCAmelCase:					Tuple		= train_secondary_learner(
      _lowercase   ,    _lowercase   ,    max_epochs=_lowercase   ,    batch_size=_lowercase   ,    eval_freq=1_0_0   ,    igf_model_path=_lowercase   ,    )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]   ,    _lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]   ,    _lowercase    :				Union[str, Any]=3_2   ,    _lowercase    :				Optional[Any]=1_0_0_0   ,    _lowercase    :				Dict=1_6   ,    _lowercase    :				Union[str, Any]=1.0   ,    _lowercase    :				Dict=recopy_gpta   ,    _lowercase    :				Optional[int]=None   ,    _lowercase    :				List[Any]=1_0   ,    _lowercase    :				List[Any]="gpt2_finetuned.pt"   ,    )							->	List[Any]:
  __UpperCAmelCase:					Optional[int]		= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )
  __UpperCAmelCase:					Any		= RandomSampler(_lowercase     )
  __UpperCAmelCase:					Any		= DataLoader(_lowercase   ,    sampler=_lowercase     )

  __UpperCAmelCase:					Optional[int]		= max_steps // (len(_lowercase     )) + 1
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Dict		= torch.zeros((1, context_len)   ,    dtype=torch.long   ,    device=_lowercase     )
  __UpperCAmelCase:					List[Any]		= recopy_model(_lowercase   ,    _lowercase   ,    _lowercase     )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(_lowercase     )
    secondary_learner.eval()
  __UpperCAmelCase:					str		= []
  __UpperCAmelCase:					Any		= 0

  __UpperCAmelCase:					Union[str, Any]		= []
  __UpperCAmelCase:					str		= []

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

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

        # 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:
          __UpperCAmelCase:					List[str]		= -1
        if predicted_q < threshold:
          __UpperCAmelCase:					List[str]		= False

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

          print("""Test perplexity, step"""   ,    _lowercase   ,    """:"""   ,    _lowercase     )
            # 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()   ,    _lowercase     )
  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  UpperCamelCase__					(					)							->	Optional[Any]:
  __UpperCAmelCase:					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=_lowercase   ,    type=_lowercase   ,    required=_lowercase   ,    help="""The input data dir. Should contain data files for WikiText."""   ,    )
  parser.add_argument(
      """--model_name_or_path"""   ,    default=_lowercase   ,    type=_lowercase   ,    required=_lowercase   ,    help="""Path to pretrained model or model identifier from huggingface.co/models"""   ,    )
  parser.add_argument(
      """--data_file"""   ,    type=_lowercase   ,    default=_lowercase   ,    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=_lowercase   ,    default=_lowercase   ,    help="""A jbl file containing the context and information gain pairs to train secondary learner."""   ,    )

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

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

  parser.add_argument(
      """--context_len"""   ,    default=3_2   ,    type=_lowercase   ,    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=_lowercase   ,    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=_lowercase   ,    help="""secondary model evaluation is triggered at eval_freq"""     )

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

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

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

  parser.add_argument(
      """--eval_interval"""   ,    default=1_0   ,    type=_lowercase   ,    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=_lowercase   ,    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=_lowercase   ,    help="""The minimum length of the article to be used as objective set"""     )

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

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

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

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

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

  # Train secondary learner
  __UpperCAmelCase:					Optional[int]		= training_secondary_learner(
      _lowercase   ,    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
  __UpperCAmelCase:					List[str]		= GPTaLMHeadModel.from_pretrained("""gpt2"""     )
  set_seed(4_2     )

  # Generate train and test data to train and evaluate gpt2 model
  __UpperCAmelCase:					Optional[Any]		= generate_datasets(
      context_len=3_2   ,    file="""data/tokenized_stories_train_wikitext103.jbl"""   ,    number=1_0_0   ,    min_len=1_0_2_6   ,    trim=_lowercase     )

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


if __name__ == "__main__":
    main()





'''simple docstring'''






import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


SCREAMING_SNAKE_CASE_	   =			get_logger(__name__)


SCREAMING_SNAKE_CASE_	   =			R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'




class    a :



  """simple docstring"""







  @add_start_docstrings(snake_case_					)
  def __call__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    raise NotImplementedError(
        F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)




class    a :



  """simple docstring"""







  @add_start_docstrings(snake_case_					)
  def __call__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    raise NotImplementedError(
        F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  @add_start_docstrings(snake_case_					)
  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    for processor in self:
      __UpperCAmelCase:					str		= inspect.signature(processor.__call__					).parameters
      if len(snake_case_					) > 3:
        if not all(arg in kwargs for arg in list(function_args.keys()					)[2:]					):
          raise ValueError(
              F'''Make sure that all the required parameters: {list(function_args.keys()					)} for '''
              F'''{processor.__class__} are passed to the logits processor.'''					)
        __UpperCAmelCase:					List[Any]		= processor(snake_case_   ,    snake_case_   ,    snake_case_   ,    **snake_case_					)
      else:
        __UpperCAmelCase:					Optional[Any]		= processor(snake_case_   ,    snake_case_   ,    snake_case_					)
    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    if not isinstance(snake_case_   ,    snake_case_					) or not (temperature > 0):
      raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''					)

    __UpperCAmelCase:					Any		= temperature







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= scores / self.temperature
    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_ = -float("""Inf"""					)   ,    snake_case_ = 1					):

    '''simple docstring'''







    if not isinstance(snake_case_   ,    snake_case_					) or (top_p < 0 or top_p > 1.0):
      raise ValueError(F'''`top_p` has to be a float > 0 and < 1, but is {top_p}'''					)
    if not isinstance(snake_case_   ,    snake_case_					) or (min_tokens_to_keep < 1):
      raise ValueError(F'''`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'''					)

    __UpperCAmelCase:					int		= top_p
    __UpperCAmelCase:					str		= filter_value
    __UpperCAmelCase:					List[Any]		= min_tokens_to_keep







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= lax.top_k(snake_case_   ,    scores.shape[-1]					)

    __UpperCAmelCase:					Tuple		= jnp.full_like(snake_case_   ,    self.filter_value					)
    __UpperCAmelCase:					Optional[int]		= jax.nn.softmax(snake_case_   ,    axis=-1					).cumsum(axis=-1					)
    __UpperCAmelCase:					List[str]		= cumulative_probs < self.top_p

    # include the token that is higher than top_p as well
    __UpperCAmelCase:					str		= jnp.roll(snake_case_   ,    1					)
    score_mask |= score_mask.at[:, 0].set(snake_case_					)

    # min tokens to keep
    __UpperCAmelCase:					str		= score_mask.at[:, : self.min_tokens_to_keep].set(snake_case_					)

    __UpperCAmelCase:					List[Any]		= jnp.where(snake_case_   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= jax.lax.sort_key_val(snake_case_   ,    snake_case_					)[-1]

    return next_scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_ = -float("""Inf"""					)   ,    snake_case_ = 1					):

    '''simple docstring'''







    if not isinstance(snake_case_   ,    snake_case_					) or top_k <= 0:
      raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''					)

    __UpperCAmelCase:					Optional[Any]		= max(snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					List[str]		= filter_value







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= scores.shape
    __UpperCAmelCase:					List[Any]		= jnp.full(batch_size * vocab_size   ,    self.filter_value					)

    __UpperCAmelCase:					Any		= min(self.top_k   ,    scores.shape[-1]					)  # Safety check
    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= lax.top_k(snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= jnp.broadcast_to((jnp.arange(snake_case_					) * vocab_size)[:, None]   ,    (batch_size, topk)					).flatten()
    __UpperCAmelCase:					List[Any]		= topk_scores.flatten()
    __UpperCAmelCase:					str		= topk_indices.flatten() + shift

    __UpperCAmelCase:					List[Any]		= next_scores_flat.at[topk_indices_flat].set(snake_case_					)
    __UpperCAmelCase:					Any		= next_scores_flat.reshape(snake_case_   ,    snake_case_					)
    return next_scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= bos_token_id







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= jnp.full(scores.shape   ,    -float("""inf"""					)					)

    __UpperCAmelCase:					Optional[Any]		= 1 - jnp.bool_(cur_len - 1					)

    __UpperCAmelCase:					Any		= jnp.where(snake_case_   ,    new_scores.at[:, self.bos_token_id].set(0					)   ,    snake_case_					)

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= max_length
    __UpperCAmelCase:					List[str]		= eos_token_id







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= jnp.full(scores.shape   ,    -float("""inf"""					)					)

    __UpperCAmelCase:					List[str]		= 1 - jnp.bool_(cur_len - self.max_length + 1					)

    __UpperCAmelCase:					Any		= jnp.where(snake_case_   ,    new_scores.at[:, self.eos_token_id].set(0					)   ,    snake_case_					)

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    if not isinstance(snake_case_   ,    snake_case_					) or min_length < 0:
      raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''					)

    if not isinstance(snake_case_   ,    snake_case_					) or eos_token_id < 0:
      raise ValueError(F'''`eos_token_id` has to be a positive integer, but is {eos_token_id}'''					)

    __UpperCAmelCase:					List[Any]		= min_length
    __UpperCAmelCase:					Optional[int]		= eos_token_id







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= 1 - jnp.clip(cur_len - self.min_length   ,    0   ,    1					)

    __UpperCAmelCase:					int		= jnp.where(snake_case_   ,    scores.at[:, self.eos_token_id].set(-float("""inf"""					)					)   ,    snake_case_					)

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= list(snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= begin_index







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= 1 - jnp.bool_(cur_len - self.begin_index					)

    __UpperCAmelCase:					str		= jnp.where(snake_case_   ,    scores.at[:, self.begin_suppress_tokens].set(-float("""inf"""					)					)   ,    snake_case_					)

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= list(snake_case_					)







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= scores.at[..., self.suppress_tokens].set(-float("""inf"""					)					)

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= dict(snake_case_					)
    # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
    # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
    # Indexes without forced tokens will have a negative value.
    __UpperCAmelCase:					Optional[Any]		= jnp.ones((max(force_token_map.keys()					) + 1)   ,    dtype=jnp.intaa					) * -1
    for index, token in force_token_map.items():
      if token is not None:
        __UpperCAmelCase:					Union[str, Any]		= force_token_array.at[index].set(snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= jnp.intaa(snake_case_					)







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    def _force_token(snake_case_					):
      __UpperCAmelCase:					List[Any]		= scores.shape[0]
      __UpperCAmelCase:					int		= self.force_token_array[generation_idx]

      __UpperCAmelCase:					Union[str, Any]		= jnp.ones_like(snake_case_   ,    dtype=scores.dtype					) * -float("""inf"""					)
      __UpperCAmelCase:					List[Any]		= jnp.zeros((batch_size, 1)   ,    dtype=scores.dtype					)
      __UpperCAmelCase:					List[Any]		= lax.dynamic_update_slice(snake_case_   ,    snake_case_   ,    (0, current_token)					)
      return new_scores

    __UpperCAmelCase:					Optional[int]		= lax.cond(
        cur_len >= self.force_token_array.shape[0]   ,    lambda: scores   ,    lambda: lax.cond(
            self.force_token_array[cur_len] >= 0   ,    lambda: _force_token(snake_case_					)   ,    lambda: scores   ,    )   ,    )
    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= generate_config.eos_token_id
    __UpperCAmelCase:					Union[str, Any]		= generate_config.no_timestamps_token_id
    __UpperCAmelCase:					Tuple		= generate_config.no_timestamps_token_id + 1

    __UpperCAmelCase:					Any		= decoder_input_length + 1

    if generate_config.is_multilingual:
      # room for language token and task token
      self.begin_index += 2
    if hasattr(snake_case_   ,    """max_initial_timestamp_index"""					):
      __UpperCAmelCase:					Optional[int]		= generate_config.max_initial_timestamp_index
    else:
      __UpperCAmelCase:					List[str]		= model_config.vocab_size
    if self.max_initial_timestamp_index is None:
      __UpperCAmelCase:					Any		= model_config.vocab_size







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= scores.at[:, self.no_timestamps_token_id].set(-float("""inf"""					)					)

    def handle_pairs(snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					Optional[int]		= jnp.where((cur_len - self.begin_index) >= 1   ,    snake_case_   ,    snake_case_					)
      __UpperCAmelCase:					Union[str, Any]		= jnp.where(
          input_ids_k[cur_len - 1] >= self.timestamp_begin   ,    True and last_was_timestamp   ,    snake_case_   ,    )

      __UpperCAmelCase:					Union[str, Any]		= jnp.where((cur_len - self.begin_index) < 2   ,    snake_case_   ,    snake_case_					)
      __UpperCAmelCase:					List[str]		= jnp.where(
          input_ids_k[cur_len - 2] >= self.timestamp_begin   ,    snake_case_   ,    snake_case_   ,    )

      return jnp.where(
          snake_case_   ,    jnp.where(
              penultimate_was_timestamp > 0   ,    scores_k.at[self.timestamp_begin :].set(-float("""inf"""					)					)   ,    scores_k.at[: self.eos_token_id].set(-float("""inf"""					)					)   ,    )   ,    snake_case_   ,    )

    __UpperCAmelCase:					Dict		= jax.vmap(snake_case_					)(snake_case_   ,    snake_case_					)

    __UpperCAmelCase:					Dict		= jnp.where(cur_len == self.begin_index   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					List[Any]		= jnp.where(
        self.max_initial_timestamp_index is not None   ,    True and apply_max_initial_timestamp   ,    snake_case_   ,    )

    __UpperCAmelCase:					List[str]		= self.timestamp_begin + self.max_initial_timestamp_index

    __UpperCAmelCase:					List[str]		= jnp.where(
        snake_case_   ,    scores.at[:, last_allowed + 1 :].set(-float("""inf"""					)					)   ,    snake_case_   ,    )

    # if sum of probability over timestamps is above any other token, sample timestamp
    __UpperCAmelCase:					Union[str, Any]		= jax.nn.log_softmax(snake_case_   ,    axis=-1					)

    def handle_cumulative_probs(snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					int		= jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,    axis=-1					)
      __UpperCAmelCase:					Tuple		= jnp.max(logprobs_k[: self.timestamp_begin]					)
      return jnp.where(
          timestamp_logprob > max_text_token_logprob   ,    scores_k.at[: self.timestamp_begin].set(-float("""inf"""					)					)   ,    snake_case_   ,    )

    __UpperCAmelCase:					Tuple		= jax.vmap(snake_case_					)(snake_case_   ,    snake_case_					)

    return scores