




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


lowerCAmelCase_					    = get_logger(__name__)


lowerCAmelCase_					    = R"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""






class  _lowerCAmelCase     :

      @add_start_docstrings(__UpperCAmelCase		)
      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        raise NotImplementedError(
            f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""		)






class  _lowerCAmelCase     :

      @add_start_docstrings(__UpperCAmelCase		)
      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        raise NotImplementedError(
            f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called."""		)






class  _lowerCAmelCase     (  _lowercase	):

      @add_start_docstrings(__UpperCAmelCase		)
      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        for processor in self:
          lowerCAmelCase__				:  Tuple		       =     inspect.signature(processor.__call__		).parameters
          if len(__UpperCAmelCase		) > 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."""		)
            lowerCAmelCase__				:  int		       =     processor(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		)
          else:
            lowerCAmelCase__				:  List[str]		       =     processor(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        if not isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		) or not (temperature > 0):
          raise ValueError(f"""`temperature` has to be a strictly positive float, but is {temperature}"""		)

        lowerCAmelCase__				:  Any		       =     temperature







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     scores / self.temperature
        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = -float('''Inf'''		)      ,				__UpperCAmelCase = 1		):
        if not isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		) 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(__UpperCAmelCase      ,				__UpperCAmelCase		) 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}"""		)

        lowerCAmelCase__				:  List[str]		       =     top_p
        lowerCAmelCase__				:  Optional[Any]		       =     filter_value
        lowerCAmelCase__				:  Dict		       =     min_tokens_to_keep







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     lax.top_k(__UpperCAmelCase      ,				scores.shape[-1]		)

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

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

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

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

        return next_scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = -float('''Inf'''		)      ,				__UpperCAmelCase = 1		):
        if not isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		) or top_k <= 0:
          raise ValueError(f"""`top_k` has to be a strictly positive integer, but is {top_k}"""		)

        lowerCAmelCase__				:  Tuple		       =     max(__UpperCAmelCase      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     filter_value







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  str		       =     scores.shape
        lowerCAmelCase__				:  int		       =     jnp.full(batch_size * vocab_size      ,				self.filter_value		)

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

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






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[Any]		       =     bos_token_id







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     jnp.full(scores.shape      ,				-float('''inf'''		)		)

        lowerCAmelCase__				:  Optional[int]		       =     1 - jnp.bool_(cur_len - 1		)

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

        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     max_length
        lowerCAmelCase__				:  List[Any]		       =     eos_token_id







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     jnp.full(scores.shape      ,				-float('''inf'''		)		)

        lowerCAmelCase__				:  Dict		       =     1 - jnp.bool_(cur_len - self.max_length + 1		)

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

        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if not isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		) or min_length < 0:
          raise ValueError(f"""`min_length` has to be a positive integer, but is {min_length}"""		)

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

        lowerCAmelCase__				:  Any		       =     min_length
        lowerCAmelCase__				:  Any		       =     eos_token_id







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        # create boolean flag to decide if min length penalty should be applied
        lowerCAmelCase__				:  Dict		       =     1 - jnp.clip(cur_len - self.min_length      ,				0      ,				1		)

        lowerCAmelCase__				:  Union[str, Any]		       =     jnp.where(__UpperCAmelCase      ,				scores.at[:, self.eos_token_id].set(-float('''inf'''		)		)      ,				__UpperCAmelCase		)

        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     list(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     begin_index







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     1 - jnp.bool_(cur_len - self.begin_index		)

        lowerCAmelCase__				:  Any		       =     jnp.where(__UpperCAmelCase      ,				scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''		)		)      ,				__UpperCAmelCase		)

        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     list(__UpperCAmelCase		)







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     scores.at[..., self.suppress_tokens].set(-float('''inf'''		)		)

        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Dict		       =     dict(__UpperCAmelCase		)
        # 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.
        lowerCAmelCase__				:  List[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:
            lowerCAmelCase__				:  Optional[int]		       =     force_token_array.at[index].set(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     jnp.intaa(__UpperCAmelCase		)







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        def _force_token(__UpperCAmelCase		):
          lowerCAmelCase__				:  Dict		       =     scores.shape[0]
          lowerCAmelCase__				:  Any		       =     self.force_token_array[generation_idx]

          lowerCAmelCase__				:  Union[str, Any]		       =     jnp.ones_like(__UpperCAmelCase      ,				dtype=scores.dtype		) * -float('''inf'''		)
          lowerCAmelCase__				:  Optional[int]		       =     jnp.zeros((batch_size, 1)      ,				dtype=scores.dtype		)
          lowerCAmelCase__				:  Dict		       =     lax.dynamic_update_slice(__UpperCAmelCase      ,				__UpperCAmelCase      ,				(0, current_token)		)
          return new_scores

        lowerCAmelCase__				:  Any		       =     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(__UpperCAmelCase		)      ,				lambda: scores      ,				)      ,				)
        return scores






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     generate_config.eos_token_id
        lowerCAmelCase__				:  int		       =     generate_config.no_timestamps_token_id
        lowerCAmelCase__				:  List[str]		       =     generate_config.no_timestamps_token_id + 1

        lowerCAmelCase__				:  int		       =     decoder_input_length + 1

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







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        # suppress <|notimestamps|> which is handled by without_timestamps
        lowerCAmelCase__				:  Union[str, Any]		       =     scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''		)		)

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

          lowerCAmelCase__				:  Dict		       =     jnp.where((cur_len - self.begin_index) < 2      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__				:  int		       =     jnp.where(
              input_ids_k[cur_len - 2] >= self.timestamp_begin      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				)

          return jnp.where(
              __UpperCAmelCase      ,				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'''		)		)      ,				)      ,				__UpperCAmelCase      ,				)

        lowerCAmelCase__				:  int		       =     jax.vmap(__UpperCAmelCase		)(__UpperCAmelCase      ,				__UpperCAmelCase		)

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

        lowerCAmelCase__				:  int		       =     self.timestamp_begin + self.max_initial_timestamp_index

        lowerCAmelCase__				:  Tuple		       =     jnp.where(
            __UpperCAmelCase      ,				scores.at[:, last_allowed + 1 :].set(-float('''inf'''		)		)      ,				__UpperCAmelCase      ,				)

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

        def handle_cumulative_probs(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[Any]		       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]      ,				axis=-1		)
          lowerCAmelCase__				:  List[Any]		       =     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'''		)		)      ,				__UpperCAmelCase      ,				)

        lowerCAmelCase__				:  Optional[Any]		       =     jax.vmap(__UpperCAmelCase		)(__UpperCAmelCase      ,				__UpperCAmelCase		)

        return scores







from abc import ABC, abstractmethod
from argparse import ArgumentParser






class  _lowerCAmelCase     (  _lowercase	):

      @staticmethod
      @abstractmethod
      def 	__magic_name__(   __UpperCAmelCase		):
        raise NotImplementedError()







      @abstractmethod
      def 	__magic_name__(   self		):
        raise NotImplementedError()

