def _get_logits_processor(
        self,
        generation_config: GenerationConfig,
        input_ids_seq_length: int,
        encoder_input_ids: torch.LongTensor,
        prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]],
        logits_processor: Optional[LogitsProcessorList],
        model_kwargs: Optional[Dict[str, Any]] = None,
        negative_prompt_ids: Optional[torch.Tensor] = None,
        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
    ) -> LogitsProcessorList:
        """
        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
        instances used to modify the scores of the language model head.
        """
        # instantiate processors list
        processors = LogitsProcessorList()

        if generation_config.guidance_scale is not None and generation_config.guidance_scale != 1:
            processors.append(
                UnbatchedClassifierFreeGuidanceLogitsProcessor(
                    generation_config.guidance_scale,
                    self,
                    unconditional_ids=negative_prompt_ids,
                    unconditional_attention_mask=negative_prompt_attention_mask,
                    use_cache=model_kwargs["use_cache"],
                )
            )
        if generation_config.sequence_bias is not None:
            processors.append(SequenceBiasLogitsProcessor(sequence_bias=generation_config.sequence_bias))

        if generation_config.diversity_penalty is not None and generation_config.diversity_penalty > 0.0:
            processors.append(
                HammingDiversityLogitsProcessor(
                    diversity_penalty=generation_config.diversity_penalty,
                    num_beams=generation_config.num_beams,
                    num_beam_groups=generation_config.num_beam_groups,
                )
            )
        if (
            generation_config.encoder_repetition_penalty is not None
            and generation_config.encoder_repetition_penalty != 1.0
        ):
            processors.append(
                EncoderRepetitionPenaltyLogitsProcessor(
                    penalty=generation_config.encoder_repetition_penalty, encoder_input_ids=encoder_input_ids
                )
            )
        if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
            processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
        if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
            processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
        if (
            generation_config.encoder_no_repeat_ngram_size is not None
            and generation_config.encoder_no_repeat_ngram_size > 0
        ):
            if self.config.is_encoder_decoder:
                processors.append(
                    EncoderNoRepeatNGramLogitsProcessor(
                        generation_config.encoder_no_repeat_ngram_size, encoder_input_ids
                    )
                )
            else:
                raise ValueError(
                    "It's impossible to use `encoder_no_repeat_ngram_size` with decoder-only architecture"
                )
        if generation_config.bad_words_ids is not None:
            processors.append(
                NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id)
            )
        if (
            generation_config.min_length is not None
            and generation_config.eos_token_id is not None
            and generation_config.min_length > 0
        ):
            processors.append(MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id))
        if (
            generation_config.min_new_tokens is not None
            and generation_config.eos_token_id is not None
            and generation_config.min_new_tokens > 0
        ):
            processors.append(
                MinNewTokensLengthLogitsProcessor(
                    input_ids_seq_length, generation_config.min_new_tokens, generation_config.eos_token_id
                )
            )
        if prefix_allowed_tokens_fn is not None:
            processors.append(
                PrefixConstrainedLogitsProcessor(
                    prefix_allowed_tokens_fn, generation_config.num_beams // generation_config.num_beam_groups
                )
            )
        if generation_config.forced_bos_token_id is not None:
            processors.append(ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
        if generation_config.forced_eos_token_id is not None:
            processors.append(
                ForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id)
            )
        if generation_config.remove_invalid_values is True:
            processors.append(InfNanRemoveLogitsProcessor())
        if generation_config.exponential_decay_length_penalty is not None:
            processors.append(
                ExponentialDecayLengthPenalty(
                    generation_config.exponential_decay_length_penalty,
                    generation_config.eos_token_id,
                    input_ids_seq_length,
                )
            )
        if generation_config.suppress_tokens is not None:
            processors.append(SuppressTokensLogitsProcessor(generation_config.suppress_tokens))
        if generation_config.begin_suppress_tokens is not None:
            begin_index = input_ids_seq_length
            begin_index = (
                begin_index
                if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
                else begin_index + 1
            )
            if generation_config.forced_decoder_ids is not None:
                # generation starts after the last token that is forced
                begin_index += generation_config.forced_decoder_ids[-1][0]
            processors.append(
                SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
            )
        if generation_config.forced_decoder_ids is not None:
            processors.append(ForceTokensLogitsProcessor(generation_config.forced_decoder_ids))
        processors = self._merge_criteria_processor_list(processors, logits_processor)
        # `LogitNormalization` should always be the last logit processor, when present
        if generation_config.renormalize_logits is True:
            processors.append(LogitNormalization())
        return processors

        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
        instances used to modify the scores of the language model head.
        
dpkd/transformers/src/transformers/generation/utils.py