test_generation_utils.py

# coding=utf-8
# Copyright 2020 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import inspect
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ..test_modeling_common import floats_tensor, ids_tensor


if is_torch_available():
    import torch

    from transformers import (
        AutoModelForSeq2SeqLM,
        AutoTokenizer,
        BartForConditionalGeneration,
        BartTokenizer,
        GPT2LMHeadModel,
        GPT2Tokenizer,
        ImageGPTForCausalImageModeling,
        Speech2TextForConditionalGeneration,
        SpeechEncoderDecoderModel,
        VisionEncoderDecoderModel,
        top_k_top_p_filtering,
    )
    from transformers.generation_beam_constraints import DisjunctiveConstraint, PhrasalConstraint
    from transformers.generation_beam_search import BeamSearchScorer, ConstrainedBeamSearchScorer
    from transformers.generation_logits_process import (
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitsProcessorList,
        MinLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
    )
    from transformers.generation_stopping_criteria import MaxLengthCriteria, StoppingCriteria, StoppingCriteriaList
    from transformers.generation_utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
    )


class GenerationTesterMixin:
    model_tester = None
    all_generative_model_classes = ()
    input_name = "input_ids"

    def _get_input_ids_and_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        input_ids = inputs_dict[self.input_name]
        attention_mask = torch.ones_like(input_ids, dtype=torch.long)

        # cut to half length & take max batch_size 3
        max_batch_size = 2
        sequence_length = input_ids.shape[-1] // 2
        input_ids = input_ids[:max_batch_size, :sequence_length]
        attention_mask = attention_mask[:max_batch_size, :sequence_length]

        # generate max 3 tokens
        max_length = input_ids.shape[-1] + 3
        if config.eos_token_id is not None and config.pad_token_id is None:
            # hack to allow generate for models such as GPT2 as is done in `generate()`
            config.pad_token_id = config.eos_token_id
        return config, input_ids, attention_mask, max_length

    @staticmethod
    def _get_logits_processor_and_kwargs(
        input_length,
        eos_token_id,
        forced_bos_token_id=None,
        forced_eos_token_id=None,
        max_length=None,
        diversity_penalty=None,
    ):
        process_kwargs = {
            "min_length": input_length + 1 if max_length is None else max_length - 1,
            "bad_words_ids": [[1, 0]],
            "no_repeat_ngram_size": 2,
            "repetition_penalty": 1.2,
        }
        logits_processor = LogitsProcessorList(
            (
                [
                    HammingDiversityLogitsProcessor(diversity_penalty, num_beams=2, num_beam_groups=2),
                ]
                if diversity_penalty is not None
                else []
            )
            + (
                [
                    MinLengthLogitsProcessor(process_kwargs["min_length"], eos_token_id),
                ]
                if eos_token_id is not None
                else []
            )
            + (
                [
                    ForcedBOSTokenLogitsProcessor(forced_bos_token_id),
                ]
                if forced_bos_token_id is not None
                else []
            )
            + (
                [ForcedEOSTokenLogitsProcessor(max_length, forced_eos_token_id)]
                if forced_eos_token_id is not None
                else []
            )
            + [
                NoBadWordsLogitsProcessor(process_kwargs["bad_words_ids"], eos_token_id),
                NoRepeatNGramLogitsProcessor(process_kwargs["no_repeat_ngram_size"]),
                RepetitionPenaltyLogitsProcessor(process_kwargs["repetition_penalty"]),
            ]
        )
        return process_kwargs, logits_processor

    @staticmethod
    def _get_warper_and_kwargs(num_beams):
        warp_kwargs = {"top_k": 10, "top_p": 0.7, "temperature": 0.7}
        logits_warper = LogitsProcessorList(
            [
                TemperatureLogitsWarper(warp_kwargs["temperature"]),
                TopKLogitsWarper(top_k=warp_kwargs["top_k"], min_tokens_to_keep=(2 if num_beams > 1 else 1)),
                TopPLogitsWarper(top_p=warp_kwargs["top_p"], min_tokens_to_keep=(2 if num_beams > 1 else 1)),
            ]
        )
        return warp_kwargs, logits_warper

    @staticmethod
    def _get_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1):
        beam_kwargs = {
            "early_stopping": False,
            "length_penalty": 2.0,
            "num_beams": 2,
            "num_return_sequences": num_return_sequences,
        }
        beam_scorer = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=beam_kwargs["num_beams"],
            device=torch_device,
            length_penalty=beam_kwargs["length_penalty"],
            do_early_stopping=beam_kwargs["early_stopping"],
            num_beam_hyps_to_keep=num_return_sequences,
        )
        return beam_kwargs, beam_scorer

    @staticmethod
    def _get_diverse_beam_scorer_and_kwargs(batch_size, max_length, num_return_sequences=1):
        beam_kwargs = {
            "early_stopping": False,
            "length_penalty": 2.0,
            "num_beams": 2,
            "num_return_sequences": num_return_sequences,
            "num_beam_groups": 2,  # one beam per group
            "diversity_penalty": 2.0,
        }
        beam_scorer = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=beam_kwargs["num_beams"],
            device=torch_device,
            length_penalty=beam_kwargs["length_penalty"],
            do_early_stopping=beam_kwargs["early_stopping"],
            num_beam_hyps_to_keep=num_return_sequences,
            num_beam_groups=beam_kwargs["num_beam_groups"],
        )
        return beam_kwargs, beam_scorer

    @staticmethod
    def _get_constrained_beam_scorer_and_kwargs(batch_size, max_length, constraints, num_return_sequences=1):
        beam_kwargs = {
            "early_stopping": False,
            "length_penalty": 2.0,
            "num_beams": num_return_sequences * 4,
            "num_return_sequences": num_return_sequences,
        }
        beam_scorer = ConstrainedBeamSearchScorer(
            batch_size=batch_size,
            constraints=constraints,
            num_beams=beam_kwargs["num_beams"],
            device=torch_device,
            length_penalty=beam_kwargs["length_penalty"],
            do_early_stopping=beam_kwargs["early_stopping"],
            num_beam_hyps_to_keep=num_return_sequences,
        )
        return beam_kwargs, beam_scorer

    @staticmethod
    def _get_encoder_outputs(
        model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
    ):
        encoder = model.get_encoder()
        encoder_outputs = encoder(
            input_ids,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )
        encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
            num_interleave, dim=0
        )
        input_ids = torch.zeros_like(input_ids[:, :1]) + model._get_decoder_start_token_id()
        attention_mask = None
        return encoder_outputs, input_ids, attention_mask

    def _greedy_generate(
        self,
        model,
        input_ids,
        attention_mask,
        max_length,
        output_scores=False,
        output_attentions=False,
        output_hidden_states=False,
        return_dict_in_generate=False,
    ):
        if model.config.is_encoder_decoder:
            max_length = 4
        logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
            input_ids.shape[-1],
            eos_token_id=model.config.eos_token_id,
            forced_bos_token_id=model.config.forced_bos_token_id,
            forced_eos_token_id=model.config.forced_eos_token_id,
            max_length=max_length,
        )

        kwargs = {}

        output_generate = model.generate(
            input_ids,
            attention_mask=attention_mask,
            do_sample=False,
            num_beams=1,
            max_length=max_length,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_scores=output_scores,
            return_dict_in_generate=return_dict_in_generate,
            remove_invalid_values=True,
            **logits_process_kwargs,
        )

        if model.config.is_encoder_decoder:
            encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
                model,
                input_ids,
                attention_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            kwargs["encoder_outputs"] = encoder_outputs

        with torch.no_grad():
            output_greedy = model.greedy_search(
                input_ids,
                max_length=max_length,
                attention_mask=attention_mask,
                logits_processor=logits_processor,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                output_scores=output_scores,
                return_dict_in_generate=return_dict_in_generate,
                **kwargs,
            )
        return output_greedy, output_generate

    def _sample_generate(
        self,
        model,
        input_ids,
        attention_mask,
        max_length,
        num_return_sequences,
        logits_processor,
        logits_warper,
        logits_warper_kwargs,
        process_kwargs,
        output_scores=False,
        output_attentions=False,
        output_hidden_states=False,
        return_dict_in_generate=False,
    ):
        torch.manual_seed(0)
        output_generate = model.generate(
            input_ids,
            do_sample=True,
            num_beams=1,
            max_length=max_length,
            num_return_sequences=num_return_sequences,
            attention_mask=attention_mask,
            output_scores=output_scores,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict_in_generate=return_dict_in_generate,
            remove_invalid_values=True,
            **logits_warper_kwargs,
            **process_kwargs,
        )

        torch.manual_seed(0)
        kwargs = {}
        if model.config.is_encoder_decoder:
            encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
                model,
                input_ids,
                attention_mask,
                num_interleave=num_return_sequences,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            kwargs["encoder_outputs"] = encoder_outputs
            input_ids_clone = input_ids_clone.repeat_interleave(num_return_sequences, dim=0)
        else:
            attention_mask_clone = attention_mask.repeat_interleave(num_return_sequences, dim=0)
            input_ids_clone = input_ids.repeat_interleave(num_return_sequences, dim=0)

        # prevent flaky generation test failures
        logits_processor.append(InfNanRemoveLogitsProcessor())

        with torch.no_grad():
            output_sample = model.sample(
                input_ids_clone,
                attention_mask=attention_mask_clone,
                max_length=max_length,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                output_scores=output_scores,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict_in_generate=return_dict_in_generate,
                **kwargs,
            )
        return output_sample, output_generate

    def _beam_search_generate(
        self,
        model,
        input_ids,
        attention_mask,
        max_length,
        beam_scorer,
        beam_kwargs,
        logits_processor,
        logits_process_kwargs,
        output_scores=False,
        output_attentions=False,
        output_hidden_states=False,
        return_dict_in_generate=False,
    ):
        output_generate = model.generate(
            input_ids,
            attention_mask=attention_mask,
            do_sample=False,
            max_length=max_length,
            output_scores=output_scores,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict_in_generate=return_dict_in_generate,
            remove_invalid_values=True,
            **beam_kwargs,
            **logits_process_kwargs,
        )

        # beam_search does not automatically interleave `batch_size` dim for `num_beams`
        kwargs = {}
        if model.config.is_encoder_decoder:
            encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
                model,
                input_ids,
                attention_mask,
                num_interleave=beam_scorer.num_beams,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            kwargs["encoder_outputs"] = encoder_outputs
            input_ids_clone = input_ids_clone.repeat_interleave(beam_scorer.num_beams, dim=0)
        else:
            attention_mask_clone = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
            input_ids_clone = input_ids.repeat_interleave(beam_scorer.num_beams, dim=0)

        with torch.no_grad():
            output_beam_search = model.beam_search(
                input_ids_clone,
                beam_scorer,
                max_length=max_length,
                attention_mask=attention_mask_clone,
                logits_processor=logits_processor,
                output_scores=output_scores,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict_in_generate=return_dict_in_generate,
                **kwargs,
            )
        return output_generate, output_beam_search

    def _beam_sample_generate(
        self,
        model,
        input_ids,
        attention_mask,
        max_length,
        num_return_sequences,
        beam_scorer,
        beam_kwargs,
        logits_warper,
        logits_warper_kwargs,
        output_scores=False,
        output_attentions=False,
        output_hidden_states=False,
        return_dict_in_generate=False,
    ):
        torch.manual_seed(0)
        output_generate = model.generate(
            input_ids,
            attention_mask=attention_mask,
            do_sample=True,
            max_length=max_length,
            output_scores=output_scores,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict_in_generate=return_dict_in_generate,
            remove_invalid_values=True,
            **beam_kwargs,
            **logits_warper_kwargs,
        )
        # beam_search does not automatically interleave `batch_size` dim for `num_beams * num_return_sequences`
        kwargs = {}
        if model.config.is_encoder_decoder:
            encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
                model,
                input_ids,
                attention_mask,
                num_interleave=beam_scorer.num_beams * num_return_sequences,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            kwargs["encoder_outputs"] = encoder_outputs
        else:
            attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0)

        # prevent flaky generation test failures
        logits_processor = LogitsProcessorList()
        logits_processor.append(InfNanRemoveLogitsProcessor())

        torch.manual_seed(0)
        with torch.no_grad():
            output_beam_sample = model.beam_sample(
                input_ids.repeat_interleave(beam_scorer.num_beams * num_return_sequences, dim=0),
                beam_scorer,
                max_length=max_length,
                attention_mask=attention_mask,
                logits_warper=logits_warper,
                logits_processor=logits_processor,
                output_scores=output_scores,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict_in_generate=return_dict_in_generate,
                **kwargs,
            )

        return output_generate, output_beam_sample

    def _group_beam_search_generate(
        self,
        model,
        input_ids,
        attention_mask,
        max_length,
        beam_scorer,
        beam_kwargs,
        logits_processor,
        logits_process_kwargs,
        output_scores=False,
        output_attentions=False,
        output_hidden_states=False,
        return_dict_in_generate=False,
    ):
        output_generate = model.generate(
            input_ids,
            attention_mask=attention_mask,
            do_sample=False,
            max_length=max_length,
            output_scores=output_scores,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict_in_generate=return_dict_in_generate,
            remove_invalid_values=True,
            **beam_kwargs,
            **logits_process_kwargs,
        )

        # group_beam_search does not automatically interleave `batch_size` dim for `num_beams`
        kwargs = {}
        if model.config.is_encoder_decoder:
            encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
                model,
                input_ids,
                attention_mask,
                num_interleave=beam_scorer.num_beams,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            kwargs["encoder_outputs"] = encoder_outputs
            input_ids_clone = input_ids_clone.repeat_interleave(beam_scorer.num_beams, dim=0)
        else:
            attention_mask_clone = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)
            input_ids_clone = input_ids.repeat_interleave(beam_scorer.num_beams, dim=0)

        with torch.no_grad():
            output_group_beam_search = model.group_beam_search(
                input_ids_clone,
                beam_scorer,
                max_length=max_length,
                attention_mask=attention_mask_clone,
                logits_processor=logits_processor,
                output_scores=output_scores,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict_in_generate=return_dict_in_generate,
                **kwargs,
            )
        return output_generate, output_group_beam_search

    def _constrained_beam_search_generate(
        self,
        model,
        input_ids,
        attention_mask,
        max_length,
        constrained_beam_scorer,
        constraints,
        beam_kwargs,
        logits_processor,
        logits_process_kwargs,
        output_scores=False,
        output_attentions=False,
        output_hidden_states=False,
        return_dict_in_generate=False,
    ):
        output_generate = model.generate(
            input_ids,
            attention_mask=attention_mask,
            do_sample=False,
            max_length=max_length,
            output_scores=output_scores,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict_in_generate=return_dict_in_generate,
            remove_invalid_values=True,
            constraints=constraints,
            **beam_kwargs,
            **logits_process_kwargs,
        )

        # group_beam_search does not automatically interleave `batch_size` dim for `num_beams`
        kwargs = {}
        if model.config.is_encoder_decoder:
            encoder_outputs, input_ids_clone, attention_mask_clone = self._get_encoder_outputs(
                model,
                input_ids,
                attention_mask,
                num_interleave=constrained_beam_scorer.num_beams,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
            )
            kwargs["encoder_outputs"] = encoder_outputs
            input_ids_clone = input_ids_clone.repeat_interleave(constrained_beam_scorer.num_beams, dim=0)
        else:
            attention_mask_clone = attention_mask.repeat_interleave(constrained_beam_scorer.num_beams, dim=0)
            input_ids_clone = input_ids.repeat_interleave(constrained_beam_scorer.num_beams, dim=0)

        with torch.no_grad():
            output_group_beam_search = model.constrained_beam_search(
                input_ids_clone,
                constrained_beam_scorer,
                max_length=max_length,
                attention_mask=attention_mask_clone,
                logits_processor=logits_processor,
                output_scores=output_scores,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict_in_generate=return_dict_in_generate,
                **kwargs,
            )
        return output_generate, output_group_beam_search

    def test_greedy_generate(self):
        # check `generate()` and `greedy_search()` are equal
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
            # test old generation output for backwards compatibility
            model = model_class(config).to(torch_device).eval()
            output_greedy, output_generate = self._greedy_generate(
                model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length
            )
            self.assertListEqual(output_greedy.tolist(), output_generate.tolist())

    def test_greedy_generate_dict_outputs(self):
        for model_class in self.all_generative_model_classes:
            # disable cache
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
            config.use_cache = False
            model = model_class(config).to(torch_device).eval()
            output_greedy, output_generate = self._greedy_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput)
                self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput)
                self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput)

            self.assertListEqual(output_generate.sequences.tolist(), output_greedy.sequences.tolist())

            for output in (output_greedy, output_generate):
                self._check_outputs(output, input_ids, model.config)

    def test_greedy_generate_dict_outputs_use_cache(self):
        for model_class in self.all_generative_model_classes:
            # enable cache
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            if not hasattr(config, "use_cache"):
                # only relevant if model has "use_cache"
                return

            config.use_cache = True
            config.is_decoder = True
            model = model_class(config).to(torch_device).eval()
            output_greedy, output_generate = self._greedy_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            self.assertListEqual(output_generate.sequences.tolist(), output_greedy.sequences.tolist())

            for output in (output_greedy, output_generate):
                self._check_outputs(output, input_ids, model.config, use_cache=True)

    def test_sample_generate(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
            model = model_class(config).to(torch_device).eval()

            if model.config.is_encoder_decoder:
                max_length = 4

            process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                model.config.eos_token_id,
                forced_bos_token_id=model.config.forced_bos_token_id,
                forced_eos_token_id=model.config.forced_eos_token_id,
                max_length=max_length,
            )
            logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)

            # check `generate()` and `sample()` are equal
            output_sample, output_generate = self._sample_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                num_return_sequences=1,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                logits_warper_kwargs=logits_warper_kwargs,
                process_kwargs=process_kwargs,
            )
            self.assertListEqual(output_sample.tolist(), output_generate.tolist())

            # check `generate()` and `sample()` yield equal results for `num_return_sequences`
            output_sample, output_generate = self._sample_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                num_return_sequences=3,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                logits_warper_kwargs=logits_warper_kwargs,
                process_kwargs=process_kwargs,
            )
            self.assertListEqual(output_sample.tolist(), output_generate.tolist())

    def test_sample_generate_dict_output(self):
        for model_class in self.all_generative_model_classes:
            # disable cache
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
            config.use_cache = False
            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 4

            process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                model.config.eos_token_id,
                forced_bos_token_id=model.config.forced_bos_token_id,
                forced_eos_token_id=model.config.forced_eos_token_id,
                max_length=max_length,
            )
            logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)

            output_sample, output_generate = self._sample_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                num_return_sequences=2,
                logits_processor=logits_processor,
                logits_warper=logits_warper,
                logits_warper_kwargs=logits_warper_kwargs,
                process_kwargs=process_kwargs,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_sample, SampleEncoderDecoderOutput)
                self.assertIsInstance(output_generate, SampleEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_sample, SampleDecoderOnlyOutput)
                self.assertIsInstance(output_generate, SampleDecoderOnlyOutput)

            self.assertListEqual(output_generate.sequences.tolist(), output_sample.sequences.tolist())

            for output in (output_sample, output_generate):
                self._check_outputs(output, input_ids, model.config, num_return_sequences=2)

    def test_beam_search_generate(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 4

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
            )
            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)

            # check `generate()` and `beam_search()` are equal
            output_generate, output_beam_search = self._beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_process_kwargs=logits_process_kwargs,
                logits_processor=logits_processor,
            )

            self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())

            # check `generate()` and `beam_search()` are equal for `num_return_sequences`
            num_return_sequences = 2
            if model.config.is_encoder_decoder:
                max_length = 4
            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
                input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
            )

            output_generate, output_beam_search = self._beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_process_kwargs=logits_process_kwargs,
                logits_processor=logits_processor,
            )
            self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())

    def test_beam_search_generate_dict_output(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # disable cache
            config.use_cache = False

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 4

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
            )
            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
            output_generate, output_beam_search = self._beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_process_kwargs=logits_process_kwargs,
                logits_processor=logits_processor,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )
            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_beam_search, BeamSearchEncoderDecoderOutput)
                self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_beam_search, BeamSearchDecoderOnlyOutput)
                self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)

            self.assertListEqual(output_generate.sequences.tolist(), output_beam_search.sequences.tolist())
            self.assertTrue(
                torch.allclose(output_generate["sequences_scores"], output_beam_search["sequences_scores"], atol=1e-3)
            )
            self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
            self.assertTrue((output_generate["sequences_scores"] < 0).all().item())

            for output in (output_beam_search, output_generate):
                self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams)

    def test_beam_search_generate_dict_outputs_use_cache(self):
        for model_class in self.all_generative_model_classes:
            # enable cache
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            if not hasattr(config, "use_cache"):
                # only relevant if model has "use_cache"
                return

            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 4

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
            )

            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(input_ids.shape[0], max_length)

            config.use_cache = True
            config.is_decoder = True
            model = model_class(config).to(torch_device).eval()
            output_beam, output_generate = self._beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_process_kwargs=logits_process_kwargs,
                logits_processor=logits_processor,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            self.assertListEqual(output_generate.sequences.tolist(), output_beam.sequences.tolist())

            for output in (output_beam, output_generate):
                self._check_outputs(
                    output, input_ids, model.config, use_cache=True, num_return_sequences=beam_scorer.num_beams
                )

    def test_beam_sample_generate(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)

            model = model_class(config).to(torch_device).eval()

            # check `generate()` and `beam_search()` are equal
            # change `num_return_sequences = 2` but not for `beam_scorer`
            num_return_sequences = 2
            if model.config.is_encoder_decoder:
                max_length = 4
            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
                input_ids.shape[0] * num_return_sequences, max_length
            )
            beam_kwargs["num_return_sequences"] = num_return_sequences

            output_generate, output_beam_sample = self._beam_sample_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                num_return_sequences=num_return_sequences,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_warper=logits_warper,
                logits_warper_kwargs=logits_warper_kwargs,
            )
            self.assertListEqual(output_generate.tolist(), output_beam_sample.tolist())

    def test_beam_sample_generate_dict_output(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # disable cache
            config.use_cache = False

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)

            num_return_sequences = 2
            if model.config.is_encoder_decoder:
                max_length = 4
            beam_kwargs, beam_scorer = self._get_beam_scorer_and_kwargs(
                input_ids.shape[0] * num_return_sequences, max_length
            )
            beam_kwargs["num_return_sequences"] = num_return_sequences

            output_beam_sample, output_generate = self._beam_sample_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                num_return_sequences=num_return_sequences,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_warper=logits_warper,
                logits_warper_kwargs=logits_warper_kwargs,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_beam_sample, BeamSampleEncoderDecoderOutput)
                self.assertIsInstance(output_generate, BeamSampleEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_beam_sample, BeamSampleDecoderOnlyOutput)
                self.assertIsInstance(output_generate, BeamSampleDecoderOnlyOutput)

            self.assertListEqual(output_generate.sequences.tolist(), output_beam_sample.sequences.tolist())
            self.assertTrue(
                torch.allclose(output_generate["sequences_scores"], output_beam_sample["sequences_scores"], atol=1e-3)
            )
            self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
            self.assertTrue((output_generate["sequences_scores"] < 0).all().item())

            for output in (output_beam_sample, output_generate):
                self._check_outputs(
                    output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams
                )

    def test_generate_without_input_ids(self):
        config, _, _, max_length = self._get_input_ids_and_config()

        # if no bos token id => cannot generate from None
        if config.bos_token_id is None:
            return

        for model_class in self.all_generative_model_classes:
            model = model_class(config).to(torch_device)
            model.eval()

            output_ids_generate = model.generate(
                do_sample=False,
                max_length=max_length,
                remove_invalid_values=True,
            )

            self.assertIsNotNone(output_ids_generate)

    def test_group_beam_search_generate(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 4

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
                diversity_penalty=2.0,
            )

            # check `generate()` and `group_beam_search()` are equal
            beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(input_ids.shape[0], max_length)
            output_generate, output_group_beam_search = self._group_beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_processor=logits_processor,
                logits_process_kwargs=logits_process_kwargs,
            )
            self.assertListEqual(output_generate.tolist(), output_group_beam_search.tolist())

            # check `generate()` and `group_beam_search()` are equal for `num_return_sequences`
            num_return_sequences = 2
            if model.config.is_encoder_decoder:
                max_length = 4
            beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(
                input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
            )
            output_generate, output_group_beam_search = self._group_beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_processor=logits_processor,
                logits_process_kwargs=logits_process_kwargs,
            )
            self.assertListEqual(output_generate.tolist(), output_group_beam_search.tolist())

    def test_group_beam_search_generate_dict_output(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
            config.use_cache = False

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 4

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
                diversity_penalty=2.0,
            )

            num_return_sequences = 1
            beam_kwargs, beam_scorer = self._get_diverse_beam_scorer_and_kwargs(
                input_ids.shape[0], max_length, num_return_sequences=num_return_sequences
            )
            output_generate, output_group_beam_search = self._group_beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                beam_scorer=beam_scorer,
                beam_kwargs=beam_kwargs,
                logits_processor=logits_processor,
                logits_process_kwargs=logits_process_kwargs,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )
            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_group_beam_search, BeamSearchEncoderDecoderOutput)
                self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_group_beam_search, BeamSearchDecoderOnlyOutput)
                self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)

            self.assertListEqual(output_generate.sequences.tolist(), output_group_beam_search.sequences.tolist())
            self.assertTrue(
                torch.allclose(
                    output_generate["sequences_scores"], output_group_beam_search["sequences_scores"], atol=1e-3
                )
            )
            self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
            self.assertTrue((output_generate["sequences_scores"] < 0).all().item())

            for output in (output_group_beam_search, output_generate):
                self._check_outputs(
                    output, input_ids, model.config, num_return_sequences=num_return_sequences * beam_scorer.num_beams
                )

    def test_constrained_beam_search_generate(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            max_length = 20

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
            )

            # check `generate()` and `constrained_beam_search()` are equal
            # Sample constraints
            if not input_ids.dtype == torch.float32:
                min_id = torch.min(input_ids) + 3
                max_id = torch.max(input_ids)
            else:
                # otherwise this throws an error for Speech2TextModel since its inputs are floating points
                min_id = 3
                max_id = 100

            force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
            constraints = [
                PhrasalConstraint(force_tokens),
            ]

            beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs(
                input_ids.shape[0], max_length, constraints, num_return_sequences=1
            )
            output_generate, output_beam_search = self._constrained_beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                constrained_beam_scorer=beam_scorer,
                constraints=constraints,
                beam_kwargs=beam_kwargs,
                logits_processor=logits_processor,
                logits_process_kwargs=logits_process_kwargs,
            )
            self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())
            for generation_output in output_generate:
                self._check_sequence_inside_sequence(force_tokens, generation_output)

            # check `generate()` and `constrained_beam_search()` are equal for `num_return_sequences`
            # Sample constraints
            force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
            constraints = [
                PhrasalConstraint(force_tokens),
            ]

            num_return_sequences = 2
            max_length = 20

            beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs(
                input_ids.shape[0], max_length, constraints, num_return_sequences=num_return_sequences
            )

            output_generate, output_beam_search = self._constrained_beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                constrained_beam_scorer=beam_scorer,
                constraints=constraints,
                beam_kwargs=beam_kwargs,
                logits_processor=logits_processor,
                logits_process_kwargs=logits_process_kwargs,
            )
            self.assertListEqual(output_generate.tolist(), output_beam_search.tolist())

            for generation_output in output_generate:
                self._check_sequence_inside_sequence(force_tokens, generation_output)

    def test_constrained_beam_search_generate_dict_output(self):
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

            # disable cache
            config.use_cache = False

            # It is important set set the eos_token_id to None to ensure that no sequences
            # shorter than `max_length` can be generated which could lead to flaky circle ci
            # failures if the top `num_return_sequences` beams are all shorter than the longest beam
            config.eos_token_id = None
            config.forced_eos_token_id = None

            model = model_class(config).to(torch_device).eval()
            if model.config.is_encoder_decoder:
                max_length = 20

            logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
                input_ids.shape[-1],
                config.eos_token_id,
                config.forced_bos_token_id,
                config.forced_eos_token_id,
                max_length,
            )

            # Sample constraints
            if not input_ids.dtype == torch.float32:
                min_id = torch.min(input_ids) + 3
                max_id = torch.max(input_ids)
            else:
                # otherwise this throws an error for Speech2TextModel since its inputs are floating points
                min_id = 3
                max_id = 100
            force_tokens = torch.randint(min_id, max_id, (1, 2)).tolist()[0]
            constraints = [
                PhrasalConstraint(force_tokens),
            ]

            beam_kwargs, beam_scorer = self._get_constrained_beam_scorer_and_kwargs(
                input_ids.shape[0], max_length, constraints, num_return_sequences=1
            )
            output_generate, output_beam_search = self._constrained_beam_search_generate(
                model=model,
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_length=max_length,
                constrained_beam_scorer=beam_scorer,
                constraints=constraints,
                beam_kwargs=beam_kwargs,
                logits_processor=logits_processor,
                logits_process_kwargs=logits_process_kwargs,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_beam_search, BeamSearchEncoderDecoderOutput)
                self.assertIsInstance(output_generate, BeamSearchEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_beam_search, BeamSearchDecoderOnlyOutput)
                self.assertIsInstance(output_generate, BeamSearchDecoderOnlyOutput)

            self.assertListEqual(output_generate.sequences.tolist(), output_beam_search.sequences.tolist())
            self.assertTrue(
                torch.allclose(output_generate["sequences_scores"], output_beam_search["sequences_scores"], atol=1e-3)
            )
            self.assertTrue(output_generate["sequences_scores"].shape == (output_generate["sequences"].shape[0],))
            self.assertTrue((output_generate["sequences_scores"] < 0).all().item())

            for output in (output_beam_search, output_generate):
                self._check_outputs(output, input_ids, model.config, num_return_sequences=beam_scorer.num_beams)

    def test_generate_with_head_masking(self):
        """Test designed for encoder-decoder models to ensure the attention head masking is used."""
        attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
        for model_class in self.all_generative_model_classes:
            config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
            model = model_class(config).to(torch_device)
            # We want to test only encoder-decoder models
            if not config.is_encoder_decoder:
                continue

            head_masking = {
                "head_mask": torch.zeros(config.encoder_layers, config.encoder_attention_heads, device=torch_device),
                "decoder_head_mask": torch.zeros(
                    config.decoder_layers, config.decoder_attention_heads, device=torch_device
                ),
                "cross_attn_head_mask": torch.zeros(
                    config.decoder_layers, config.decoder_attention_heads, device=torch_device
                ),
            }

            signature = inspect.signature(model.forward)
            # We want to test only models where encoder/decoder head masking is implemented
            if not set(head_masking.keys()) < set([*signature.parameters.keys()]):
                continue

            for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
                out = model.generate(
                    input_ids,
                    attention_mask=attention_mask,
                    num_beams=1,
                    output_attentions=True,
                    return_dict_in_generate=True,
                    remove_invalid_values=True,
                    **{name: mask},
                )
                # We check the state of decoder_attentions and cross_attentions just from the last step
                attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0)

    def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
        batch_size, seq_length = input_ids.shape
        num_sequences_in_output = batch_size * num_return_sequences
        gen_len = (
            output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
        )

        # scores
        self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)

        # Attentions
        if config.is_encoder_decoder:
            # encoder
            self._check_encoder_attention_for_generate(output.encoder_attentions, batch_size, config, seq_length)
            # decoder
            self._check_attentions_for_generate(
                num_sequences_in_output,
                output.decoder_attentions,
                min_length=1,
                max_length=output.sequences.shape[-1],
                config=config,
                use_cache=use_cache,
            )
        else:
            # if use_cache first input is equal to no use_cache, so skip here
            attentions = output.attentions if not use_cache else output.attentions[1:]
            min_length = seq_length if not use_cache else seq_length + 1
            self._check_attentions_for_generate(
                num_sequences_in_output,
                attentions=attentions,
                min_length=min_length,
                max_length=output.sequences.shape[-1],
                config=config,
                use_cache=use_cache,
            )

        # Hidden States
        if config.is_encoder_decoder:
            # encoder
            self._check_encoder_hidden_states_for_generate(
                output.encoder_hidden_states, batch_size, config, seq_length
            )

            # decoder
            self._check_hidden_states_for_generate(
                num_sequences_in_output,
                output.decoder_hidden_states,
                min_length=1,
                max_length=output.sequences.shape[-1],
                config=config,
                use_cache=use_cache,
            )
        else:
            # if use_cache first input is equal to no use_cache, so skip here
            hidden_states = output.hidden_states if not use_cache else output.hidden_states[1:]
            min_length = seq_length if not use_cache else seq_length + 1
            self._check_hidden_states_for_generate(
                num_sequences_in_output,
                hidden_states,
                min_length=min_length,
                max_length=output.sequences.shape[-1],
                config=config,
                use_cache=use_cache,
            )

    def _check_scores(self, batch_size, scores, length, config):
        expected_shape = (batch_size, config.vocab_size)
        self.assertIsInstance(scores, tuple)
        self.assertEqual(len(scores), length)
        self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))

    def _check_attentions_for_generate(
        self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(attentions, tuple)
        self.assertListEqual(
            [isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
        )
        self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

        for idx, iter_attentions in enumerate(attentions):
            tgt_len = min_length + idx if not use_cache else 1
            src_len = min_length + idx

            expected_shape = (
                batch_size * num_beam_groups,
                config.num_attention_heads,
                tgt_len,
                src_len,
            )
            # check attn size
            self.assertListEqual(
                [layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
            )

    def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length):
        encoder_expected_shape = (batch_size, config.num_attention_heads, seq_length, seq_length)
        self.assertIsInstance(attentions, tuple)
        self.assertListEqual(
            [layer_attentions.shape for layer_attentions in attentions],
            [encoder_expected_shape] * len(attentions),
        )

    def _check_hidden_states_for_generate(
        self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
    ):
        self.assertIsInstance(hidden_states, tuple)
        self.assertListEqual(
            [isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
            [True] * len(hidden_states),
        )
        self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

        for idx, iter_hidden_states in enumerate(hidden_states):
            seq_len = min_length + idx if not use_cache else 1
            expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
            # check hidden size
            self.assertListEqual(
                [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
                [expected_shape] * len(iter_hidden_states),
            )

    def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, seq_length):
        encoder_expected_shape = (batch_size, seq_length, config.hidden_size)
        self.assertIsInstance(hidden_states, tuple)
        self.assertListEqual(
            [layer_hidden_states.shape for layer_hidden_states in hidden_states],
            [encoder_expected_shape] * len(hidden_states),
        )

    def _check_sequence_inside_sequence(self, tensor_1, tensor_2):
        # check if tensor_1 inside tensor_2 or tensor_2 inside tensor_1.
        # set to same device. we don't care what device.

        if not isinstance(tensor_1, list):
            tensor_1 = tensor_1.cpu().tolist()
        if not isinstance(tensor_2, list):
            tensor_2 = tensor_2.cpu().tolist()

        in_order = len(tensor_1) <= len(tensor_2)
        longer = tensor_2 if in_order else tensor_1
        shorter = tensor_1 if in_order else tensor_2

        flag = False
        chunk_size = len(shorter)
        for chunk_idx in range(len(longer) - chunk_size + 1):
            subseq = longer[chunk_idx : chunk_idx + chunk_size]
            if subseq == shorter:
                flag = True
                break

        self.assertTrue(flag)


@require_torch
class UtilsFunctionsTest(unittest.TestCase):

    # tests whether the top_k_top_p function behaves as expected
    def test_top_k_top_p_filtering(self):
        logits = torch.tensor(
            [
                [
                    8.2220991,  # 3rd highest value; idx. 0
                    -0.5620044,
                    5.23229752,
                    4.0386393,
                    -6.8798378,
                    -0.54785802,
                    -3.2012153,
                    2.92777176,
                    1.88171953,
                    7.35341276,
                    8.43207833,  # 2nd highest value; idx. 10
                    -9.85711836,
                    -5.96209236,
                    -1.13039161,
                    -7.1115294,
                    -0.8369633,
                    -5.3186408,
                    7.06427407,
                    0.81369344,
                    -0.82023817,
                    -5.9179796,
                    0.58813443,
                    -6.99778438,
                    4.71551189,
                    -0.18771637,
                    7.44020759,  # 4th highest value; idx. 25
                    9.38450987,  # 1st highest value; idx. 26
                    2.12662941,
                    -9.32562038,
                    2.35652522,
                ],  # cummulative prob of 4 highest values <= 0.6
                [
                    0.58425518,
                    4.53139238,
                    -5.57510464,
                    -6.28030699,
                    -7.19529503,
                    -4.02122551,
                    1.39337037,
                    -6.06707057,
                    1.59480517,
                    -9.643119,
                    0.03907799,
                    0.67231762,
                    -8.88206726,
                    6.27115922,  # 4th highest value; idx. 13
                    2.28520723,
                    4.82767506,
                    4.30421368,
                    8.8275313,  # 2nd highest value; idx. 17
                    5.44029958,
                    -4.4735794,
                    7.38579536,  # 3rd highest value; idx. 20
                    -2.91051663,
                    2.61946077,
                    -2.5674762,
                    -9.48959302,
                    -4.02922645,
                    -1.35416918,
                    9.67702323,  # 1st highest value; idx. 27
                    -5.89478553,
                    1.85370467,
                ],  # cummulative prob of 4 highest values <= 0.6
            ],
            dtype=torch.float,
            device=torch_device,
        )

        non_inf_expected_idx = torch.tensor(
            [[0, 0], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 20], [1, 27]],
            dtype=torch.long,
            device=torch_device,
        )  # expected non filtered idx as noted above

        non_inf_expected_output = torch.tensor(
            [
                8.2221,
                8.4321,
                7.4402,
                9.3845,
                6.2712,
                8.8275,
                7.3858,
                9.6770,
            ],  # expected non filtered values as noted above
            dtype=torch.float,
            device=torch_device,
        )

        output = top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)
        non_inf_output = output[output != -float("inf")].to(device=torch_device)
        non_inf_idx = (output != -float("inf")).nonzero().to(device=torch_device)

        self.assertTrue(torch.allclose(non_inf_expected_output, non_inf_output, atol=1e-12))
        self.assertTrue(torch.all(torch.eq(non_inf_expected_idx, non_inf_idx)))

    # tests whether the function uses filter_value instead of default -inf
    def test_top_k_top_p_filtering_with_filter_value(self):
        logits = torch.tensor(
            [
                [
                    1,
                    1,
                    1,
                    0.99,  # get filtered by top-p filtering
                    0.98,  # get filtered by top-k filtering
                ]
            ],
            dtype=torch.float,
            device=torch_device,
        )

        expected_output = torch.tensor(
            [[1, 1, 1, 0, 0]],
            dtype=torch.float,
            device=torch_device,
        )

        output = top_k_top_p_filtering(logits, top_k=4, top_p=0.5, filter_value=0.0)

        self.assertTrue(torch.allclose(expected_output, output, atol=1e-12))


@require_torch
class GenerationIntegrationTests(unittest.TestCase):
    @slow
    def test_diverse_beam_search(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood.
        The celebrity couple announced the arrival of their son, Silas Randall Timberlake, in statements to People.
        "Silas was the middle name of Timberlake's maternal grandfather Bill Bomar, who died in 2012, while Randall is the musician's own middle name, as well as his father's first," People reports.
        The couple announced the pregnancy in January, with an Instagram post. It is the first baby for both."""

        bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        bart_model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn").to(torch_device)
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        outputs = bart_model.generate(
            input_ids,
            num_beams=4,
            num_return_sequences=2,
            num_beam_groups=4,
            diversity_penalty=2.0,
            remove_invalid_values=True,
        )

        generated_text = bart_tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(
            generated_text,
            [
                "The couple announced the birth of their son, Silas Randall Timberlake, in a statement. Silas was the"
                " middle name of Timberlake's maternal grandfather Bill Bomar. Randall is the musician's own middle"
                " name, as well as his father's first. It is the first baby for both of them.",
                "Justin Timberlake and Jessica Biel have a son. The baby is named Silas Randall Timberlake. It is the"
                " first child for both. The couple announced the pregnancy in January. The name Silas is the middle"
                " name of Timberlake's maternal grandfather. It's also his own middle name.",
            ],
        )

    def test_max_length_backward_compat_greedy(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        max_length = 20
        input_ids = input_ids.expand(2, -1)
        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
            input_ids.shape[0],
            decoder_start_token_id=bart_model.config.decoder_start_token_id,
            bos_token_id=bart_model.config.bos_token_id,
        )

        with self.assertWarns(UserWarning):
            bart_model.greedy_search(
                input_ids,
                max_length=max_length,
                pad_token_id=bart_model.config.pad_token_id,
                eos_token_id=bart_model.config.eos_token_id,
                **model_kwargs,
            )

    def test_max_length_backward_compat_sample(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        max_length = 20
        input_ids = input_ids.expand(2, -1)
        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
            input_ids.shape[0],
            decoder_start_token_id=bart_model.config.decoder_start_token_id,
            bos_token_id=bart_model.config.bos_token_id,
        )
        with torch.no_grad():
            with self.assertWarns(UserWarning):
                bart_model.sample(
                    input_ids,
                    max_length=max_length,
                    pad_token_id=bart_model.config.pad_token_id,
                    eos_token_id=bart_model.config.eos_token_id,
                    **model_kwargs,
                )

    def test_max_length_backward_compat_beam_search(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        batch_size = 1
        max_length = 20
        num_beams = 2

        input_ids = input_ids.expand(2, -1)
        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
            input_ids.shape[0],
            decoder_start_token_id=bart_model.config.decoder_start_token_id,
            bos_token_id=bart_model.config.bos_token_id,
        )

        beam_scorer = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=num_beams,
            device=torch_device,
        )
        with self.assertWarns(UserWarning):
            _ = bart_model.beam_search(
                input_ids, num_beams=num_beams, max_length=max_length, beam_scorer=beam_scorer, **model_kwargs
            )

    def test_max_length_backward_compat_group_beam_search(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        batch_size = 1
        max_length = 20
        num_beams = 6
        num_beam_groups = 3
        num_return_sequences = num_beams * batch_size

        input_ids = input_ids.expand(6, -1)
        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
            input_ids.shape[0],
            decoder_start_token_id=bart_model.config.decoder_start_token_id,
            bos_token_id=bart_model.config.bos_token_id,
        )

        diverse_beam_scorer = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=num_beams,
            device=torch_device,
            num_beam_hyps_to_keep=num_return_sequences,
            num_beam_groups=num_beam_groups,
        )
        with self.assertWarns(UserWarning):
            bart_model.group_beam_search(
                input_ids, diverse_beam_scorer, num_beams=num_beams, max_length=max_length, **model_kwargs
            )

    def test_max_length_warning_if_different(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        batch_size = 1

        max_length = 20
        num_beams = 6
        num_beam_groups = 3
        num_return_sequences = num_beams * batch_size
        stopping_criteria_max_length = 18
        stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)])

        # Greedy
        input_ids = input_ids.expand(6, -1)
        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})
        input_ids = bart_model._prepare_decoder_input_ids_for_generation(
            input_ids.shape[0],
            decoder_start_token_id=bart_model.config.decoder_start_token_id,
            bos_token_id=bart_model.config.bos_token_id,
        )

        with self.assertWarns(UserWarning):
            bart_model.greedy_search(
                input_ids,
                max_length=max_length,
                pad_token_id=bart_model.config.pad_token_id,
                stopping_criteria=stopping_criteria,
                eos_token_id=bart_model.config.eos_token_id,
                **model_kwargs,
            )

        # Sample
        with self.assertWarns(UserWarning):
            with torch.no_grad():
                bart_model.sample(
                    input_ids,
                    max_length=max_length,
                    stopping_criteria=stopping_criteria,
                    pad_token_id=bart_model.config.pad_token_id,
                    eos_token_id=bart_model.config.eos_token_id,
                    **model_kwargs,
                )

        # Beam
        beam_scorer = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=num_beams,
            device=torch_device,
        )
        with self.assertWarns(UserWarning):
            with torch.no_grad():
                bart_model.beam_search(
                    input_ids,
                    num_beams=num_beams,
                    stopping_criteria=stopping_criteria,
                    max_length=max_length,
                    beam_scorer=beam_scorer,
                    **model_kwargs,
                )

        # Grouped beam search
        diverse_beam_scorer = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=num_beams,
            device=torch_device,
            num_beam_hyps_to_keep=num_return_sequences,
            num_beam_groups=num_beam_groups,
        )
        with self.assertWarns(UserWarning):
            bart_model.group_beam_search(
                input_ids,
                diverse_beam_scorer,
                stopping_criteria=stopping_criteria,
                num_beams=num_beams,
                max_length=max_length,
                **model_kwargs,
            )

    def test_beam_search_warning_if_max_length_is_passed(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )

        batch_size = 1
        num_beams = 3

        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        input_ids = input_ids.expand(num_beams, -1)
        model_kwargs = bart_model._prepare_encoder_decoder_kwargs_for_generation(input_ids, {})

        # pretend decoder_input_ids correspond to first encoder input id
        decoder_input_ids = input_ids[:, :1]

        stopping_criteria_max_length = 18
        stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=stopping_criteria_max_length)])

        with self.assertWarns(UserWarning):
            beam_scorer = BeamSearchScorer(
                batch_size=batch_size,
                num_beams=num_beams,
                device=torch_device,
                max_length=10,
            )

        generated_ids = bart_model.beam_search(
            decoder_input_ids,
            num_beams=num_beams,
            stopping_criteria=stopping_criteria,
            beam_scorer=beam_scorer,
            **model_kwargs,
        )

        beam_scorer_no_max_len = BeamSearchScorer(
            batch_size=batch_size,
            num_beams=num_beams,
            device=torch_device,
        )

        generated_ids_no_max_len = bart_model.beam_search(
            decoder_input_ids,
            num_beams=num_beams,
            stopping_criteria=stopping_criteria,
            beam_scorer=beam_scorer_no_max_len,
            **model_kwargs,
        )

        # BeamSearchScorer max_length should not influence "real" max_length
        self.assertEqual(generated_ids.tolist(), generated_ids_no_max_len.tolist())

    def test_custom_stopping_criteria_overload_error(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
        bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)

        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        stopping_criteria = StoppingCriteriaList()
        stopping_criteria.append(MaxLengthCriteria(max_length=42))
        with self.assertRaises(ValueError):
            bart_model.generate(input_ids, stopping_criteria=stopping_criteria)
        with self.assertRaises(ValueError):
            bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=32)

    def test_custom_stopping_criteria(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
        bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random").to(torch_device)
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        class DummyCriteria(StoppingCriteria):
            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
                return input_ids.shape[-1] >= 20

        stopping_criteria = StoppingCriteriaList()
        stopping_criteria.append(DummyCriteria())

        self.assertEqual(
            list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=22).shape),
            [1, 20],
        )
        self.assertEqual(
            list(bart_model.generate(input_ids, stopping_criteria=stopping_criteria, max_length=18).shape),
            [1, 18],
        )

    def test_custom_logits_processor(self):
        bart_tokenizer = BartTokenizer.from_pretrained("sshleifer/bart-tiny-random")
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_model = BartForConditionalGeneration.from_pretrained("sshleifer/bart-tiny-random", min_length=1).to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        logits_processor = LogitsProcessorList()
        logits_processor.append(MinLengthLogitsProcessor(min_length=10, eos_token_id=0))
        # it should not be allowed to both define `min_length` via config and `logits_processor` list
        with self.assertRaises(ValueError):
            bart_model.generate(input_ids, logits_processor=logits_processor)

        bart_model.config.min_length = None
        bart_model.generate(input_ids, logits_processor=logits_processor)

    def test_max_new_tokens_encoder_decoder(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        bart_tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
            torch_device
        )
        input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        self.assertEqual(list(input_ids.shape), [1, 29])

        max_new_tokens = 3
        bart_model.config.max_length = 20
        bart_model.config.eos_token_id = None

        # Encoder decoder call
        outputs = bart_model.generate(input_ids, max_new_tokens=max_new_tokens)
        # 1 BOS + 3 new tokens
        self.assertEqual(list(outputs.shape), [1, 4])

        # Decoder only call
        outputs = bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=max_new_tokens)
        # 29 + 3 new tokens
        self.assertEqual(list(outputs.shape), [1, 32])

        # Encoder decoder call > 20
        outputs = bart_model.generate(max_new_tokens=max_new_tokens + 20)

        # 1 BOS + 20 + 3 new tokens
        self.assertEqual(list(outputs.shape), [1, 24])

        # max_new_tokens and max_length serve the same purpose and should not be used together.
        with self.assertWarns(UserWarning):
            bart_model.generate(decoder_input_ids=input_ids, max_new_tokens=10, max_length=20)

    def test_max_new_tokens_decoder_only(self):
        article = """Justin Timberlake."""
        gpt2_tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        gpt2_model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
        input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)

        self.assertEqual(list(input_ids.shape), [1, 9])

        max_new_tokens = 3
        gpt2_model.config.max_length = 20

        # call < 20
        outputs = gpt2_model.generate(input_ids, max_new_tokens=max_new_tokens)

        # 9 input_ids + 3 new tokens
        self.assertEqual(list(outputs.shape), [1, 12])

        # call > 20
        outputs = gpt2_model.generate(max_new_tokens=max_new_tokens + 20)

        # 1 BOS token + 23 new tokens
        self.assertEqual(list(outputs.shape), [1, 24])

        # max_new_tokens and max_length serve the same purpose and should not be used together.
        with self.assertWarns(UserWarning):
            gpt2_model.generate(decoder_input_ids=input_ids, max_new_tokens=10, max_length=20)

    def test_encoder_decoder_generate_with_inputs_embeds(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=5).to(
            torch_device
        )
        model.config.eos_token_id = None
        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        inputs_embeds = model.get_input_embeddings()(input_ids)

        output_sequences = model.generate(inputs_embeds=inputs_embeds)

        # make sure model generated correctly until `max_length`
        self.assertEqual(output_sequences.shape, (1, 5))

    def test_encoder_decoder_generate_attention_mask(self):
        articles = ["Timberlake", "Jessica Biel, welcome to parenthood among other things"]
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        # need extrem generation values here to force this test
        # to fail when `attention_mask` is not correctly treated in generate
        model = BartForConditionalGeneration.from_pretrained(
            "hf-internal-testing/tiny-random-bart", max_length=50, num_beams=5, num_return_sequences=5
        ).to(torch_device)

        model.config.eos_token_id = None
        input_ids = tokenizer(articles[0], return_tensors="pt").input_ids.to(torch_device)
        input_ids_batched = tokenizer(articles, padding=True, return_tensors="pt").input_ids.to(torch_device)

        output_sequences_batched = model.generate(
            input_ids=input_ids_batched, return_dict_in_generate=True, output_scores=True
        )
        output_sequences = model.generate(input_ids=input_ids, return_dict_in_generate=True, output_scores=True)

        batched_out = output_sequences_batched.sequences_scores
        out = output_sequences.sequences_scores

        diff = (batched_out[:5].sum() - out.sum()).abs()

        self.assertTrue(diff < 1e-4)

    def test_decoder_generate_with_inputs_embeds(self):
        article = """I need input_ids to generate"""
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=5).to(torch_device)
        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        inputs_embeds = model.get_input_embeddings()(input_ids)

        # cannot generate from `inputs_embeds` for decoder only
        with self.assertRaises(ValueError):
            model.generate(inputs_embeds=inputs_embeds)

    def test_generate_input_ids_as_kwarg(self):
        article = """I need input_ids to generate"""
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=15).to(torch_device)
        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        output_sequences_kwargs = model.generate(input_ids=input_ids).cpu()
        output_sequences = model.generate(input_ids).cpu()

        self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
        self.assertEqual(output_sequences.shape, (1, 15))

    def test_generate_non_nlp_input_ids_as_kwarg(self):
        model = ImageGPTForCausalImageModeling.from_pretrained(
            "hf-internal-testing/tiny-random-imagegpt", max_length=10
        ).to(torch_device)
        input_ids = ids_tensor((3, 5), vocab_size=10)

        output_sequences_kwargs = model.generate(input_ids=input_ids).cpu()
        output_sequences = model.generate(input_ids).cpu()

        self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
        self.assertEqual(output_sequences.shape, (3, 10))

    def test_generate_input_ids_as_encoder_kwarg(self):
        article = """Justin Timberlake and Jessica Biel, welcome to parenthood."""
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart", max_length=5).to(
            torch_device
        )
        model.config.eos_token_id = None
        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        output_sequences_kwargs = model.generate(input_ids=input_ids).cpu()
        output_sequences = model.generate(input_ids).cpu()

        self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
        self.assertEqual(output_sequences.shape, (1, 5))

    def test_generate_inputs_and_encoder_kwargs(self):
        article = """I need input_ids to generate"""
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=10).to(torch_device)
        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        with self.assertRaises(ValueError):
            model.generate(input_ids, input_ids=input_ids)

    def test_generate_too_many_encoder_kwargs(self):
        article = """I need input_ids to generate"""
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2", max_length=10).to(torch_device)
        input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
        with self.assertRaises(ValueError):
            model.generate(input_ids=input_ids, inputs_embeds=input_ids)

    def test_generate_input_values_as_encoder_kwarg(self):
        input_values = floats_tensor((2, 250))
        model = SpeechEncoderDecoderModel.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder")
        model = model.to(torch_device)
        output_sequences_kwargs = model.generate(input_values=input_values, max_length=5).cpu()
        output_sequences = model.generate(input_values, max_length=5).cpu()

        self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
        self.assertEqual(output_sequences.shape, (2, 5))

    def test_generate_input_features_as_encoder_kwarg(self):
        input_features = floats_tensor((3, 20, 24))
        model = Speech2TextForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-speech_to_text")
        model = model.to(torch_device)
        output_sequences_kwargs = model.generate(input_features=input_features, max_length=5).cpu()
        output_sequences = model.generate(input_features, max_length=5).cpu()

        self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
        self.assertEqual(output_sequences.shape, (3, 5))

    def test_generate_pixel_values_as_encoder_kwarg(self):
        pixel_values = floats_tensor((2, 3, 30, 30))
        model = VisionEncoderDecoderModel.from_pretrained("hf-internal-testing/tiny-random-vision-encoder-decoder")
        model = model.to(torch_device)
        output_sequences_kwargs = model.generate(pixel_values=pixel_values, max_length=5).cpu()
        output_sequences = model.generate(pixel_values, max_length=5).cpu()

        self.assertListEqual(output_sequences.tolist(), output_sequences_kwargs.tolist())
        self.assertEqual(output_sequences.shape, (2, 5))

    def test_generate_encoder_outputs_attention_mask(self):
        input_values = floats_tensor((2, 250)).to(torch_device)
        attention_mask = torch.ones_like(input_values)
        model = SpeechEncoderDecoderModel.from_pretrained("hf-internal-testing/tiny-random-speech-encoder-decoder")
        model = model.to(torch_device)

        encoder = model.get_encoder()

        encoder_outputs = encoder(input_values)

        output_sequences_no_mask = model.generate(encoder_outputs=encoder_outputs).cpu()
        output_sequences_with_mask = model.generate(encoder_outputs=encoder_outputs, attention_mask=attention_mask)
        output_sequences_with_mask = output_sequences_with_mask.cpu()

        self.assertListEqual(output_sequences_no_mask.tolist(), output_sequences_with_mask.tolist())

    def test_transition_scores_beam_search_encoder_decoder(self):
        articles = [
            "Justin Timberlake and Jessica Biel, welcome to parenthood.",
            "Michael Phelps is arguably the most decorated Olympian of all time.",
        ]
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained(
            "hf-internal-testing/tiny-random-bart",
            max_length=10,
            num_beams=4,
            num_return_sequences=2,
            eos_token_id=None,
            return_dict_in_generate=True,
            output_scores=True,
            length_penalty=0.0,
        )
        model = model.to(torch_device)

        input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
        outputs = model.generate(input_ids=input_ids)

        transition_scores = model.compute_transition_beam_scores(
            outputs.sequences, outputs.scores, outputs.beam_indices
        )
        transition_scores_sum = transition_scores.sum(-1)

        self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))

    def test_transition_scores_beam_search_encoder_decoder_with_eos(self):
        articles = [
            "Justin Timberlake and Jessica Biel, welcome to parenthood.",
            "Michael Phelps is arguably the most decorated Olympian of all time.",
        ]
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained(
            "hf-internal-testing/tiny-random-bart",
            max_length=10,
            num_beams=4,
            num_return_sequences=2,
            return_dict_in_generate=True,
            output_scores=True,
            length_penalty=0.0,
        )
        model = model.to(torch_device)

        input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
        outputs = model.generate(input_ids=input_ids)

        transition_scores = model.compute_transition_beam_scores(
            outputs.sequences, outputs.scores, outputs.beam_indices
        )
        transition_scores_sum = transition_scores.sum(-1)

        self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))

    def test_transition_scores_beam_search_decoder_only(self):
        articles = [
            "Justin Timberlake",
            "Michael Phelps",
        ]
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        tokenizer.pad_token = tokenizer.eos_token

        model = GPT2LMHeadModel.from_pretrained(
            "hf-internal-testing/tiny-random-gpt2",
            max_length=10,
            num_beams=4,
            num_return_sequences=2,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=None,
            return_dict_in_generate=True,
            output_scores=True,
            length_penalty=0.0,
        )
        model = model.to(torch_device)

        input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
        outputs = model.generate(input_ids=input_ids)

        transition_scores = model.compute_transition_beam_scores(
            outputs.sequences, outputs.scores, outputs.beam_indices
        )
        transition_scores_sum = transition_scores.sum(-1)

        self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))

    def test_transition_scores_beam_sample_encoder_decoder(self):
        articles = [
            "Justin Timberlake and Jessica Biel, welcome to parenthood.",
            "Michael Phelps is arguably the most decorated Olympian of all time.",
        ]
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained(
            "hf-internal-testing/tiny-random-bart",
            do_sample=True,
            max_length=10,
            num_beams=4,
            num_return_sequences=2,
            eos_token_id=None,
            return_dict_in_generate=True,
            output_scores=True,
            length_penalty=0.0,
        )
        model = model.to(torch_device)

        input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
        outputs = model.generate(input_ids=input_ids)

        transition_scores = model.compute_transition_beam_scores(
            outputs.sequences, outputs.scores, outputs.beam_indices
        )
        transition_scores_sum = transition_scores.sum(-1)

        self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))

    def test_transition_scores_group_beam_search_encoder_decoder(self):
        articles = [
            "Justin Timberlake and Jessica Biel, welcome to parenthood.",
            "Michael Phelps is arguably the most decorated Olympian of all time.",
        ]
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained(
            "hf-internal-testing/tiny-random-bart",
            max_length=10,
            num_beams=2,
            num_beam_groups=2,
            num_return_sequences=2,
            eos_token_id=None,
            return_dict_in_generate=True,
            output_scores=True,
            length_penalty=0.0,
        )
        model = model.to(torch_device)

        input_ids = tokenizer(articles, return_tensors="pt", padding=True).input_ids.to(torch_device)
        outputs = model.generate(input_ids=input_ids)

        transition_scores = model.compute_transition_beam_scores(
            outputs.sequences, outputs.scores, outputs.beam_indices
        )
        transition_scores_sum = transition_scores.sum(-1)

        self.assertTrue(torch.allclose(transition_scores_sum, outputs.sequences_scores, atol=1e-3))

    @slow
    def test_transition_scores_early_stopping(self):
        # This is an aggressive test that makes sure that `beam_search's`
        # transition scores are computed correctly for varying `num_return_sequences`,
        # `num_beams` and `batch_size > 1`
        # 2 x input_ids for "question: How are you? \n context: I had a long day, "
        input_ids = torch.tensor(2 * [[822, 10, 571, 33, 25, 58, 2625, 10, 27, 141, 3, 9, 307, 239, 6, 1]]).to(
            torch_device
        )

        model = AutoModelForSeq2SeqLM.from_pretrained("t5-small").to(torch_device)

        result = model.generate(
            input_ids,
            max_length=10,
            return_dict_in_generate=True,
            output_scores=True,
            forced_eos_token_id=model.config.eos_token_id,
            num_beams=4,
            do_sample=False,
            num_return_sequences=3,
            length_penalty=0.0,
        )

        transition_scores = model.compute_transition_beam_scores(
            sequences=result.sequences, scores=result.scores, beam_indices=result.beam_indices
        )

        sum_transition_scores = torch.sum(transition_scores, dim=1)

        self.assertListEqual(sum_transition_scores.cpu().tolist(), result.sequences_scores.cpu().tolist())

    def test_log_scores_sample_decoder_only(self):
        articles = ["I need input_ids to generate", "Short and"]
        tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
        tokenizer.padding_side = "left"
        tokenizer.pad_token = tokenizer.eos_token

        model = GPT2LMHeadModel.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)

        inputs = tokenizer(articles, return_tensors="pt", padding=True).to(torch_device)

        result = model.generate(
            **inputs,
            max_length=15,
            return_dict_in_generate=True,
            do_sample=False,
            output_scores=True,
        )

        # decoder-only starts generating from `input_ids`
        begin_generation = inputs.input_ids.shape[-1]

        gen_sequences = result.sequences[:, begin_generation:]
        probs = torch.stack(result.scores, dim=1).softmax(-1)

        gen_probs = torch.gather(probs, 2, gen_sequences[:, :, None]).squeeze(-1)
        expected_probs = torch.tensor([[0.0014, 0.0015], [0.0014, 0.0014]])

        self.assertTrue(torch.allclose(gen_probs.cpu(), expected_probs, atol=1e-3))

    def test_log_scores_sample_encoder_decoder(self):
        articles = ["I need input_ids to generate", "Short and"]
        tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
        model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)

        inputs = tokenizer(articles, return_tensors="pt", padding=True).to(torch_device)

        result = model.generate(
            **inputs,
            max_length=3,
            return_dict_in_generate=True,
            do_sample=False,
            num_beams=1,
            output_scores=True,
        )

        # encoder-decoder has one decoder_start_token_id by default
        begin_generation = 1

        gen_sequences = result.sequences[:, begin_generation:]
        probs = torch.stack(result.scores, dim=1).softmax(-1)

        gen_probs = torch.gather(probs, 2, gen_sequences[:, :, None]).squeeze(-1)
        expected_probs = torch.tensor([[0.0013, 1.0000], [0.0013, 1.0000]])

        self.assertTrue(torch.allclose(gen_probs.cpu(), expected_probs, atol=1e-3))

    @slow
    def test_beam_search_example_integration(self):
        # exactly the example provided in the docstrings of beam search, which previously
        # failed after directly copying from it. Refer to PR #15555
        tokenizer = AutoTokenizer.from_pretrained("t5-base")
        model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

        encoder_input_str = "translate English to German: How old are you?"
        encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids

        # lets run beam search using 3 beams
        num_beams = 3
        # define decoder start token ids
        input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
        input_ids = input_ids * model.config.decoder_start_token_id

        # add encoder_outputs to model keyword arguments
        model_kwargs = {
            "encoder_outputs": model.get_encoder()(
                encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
            )
        }

        # instantiate beam scorer
        beam_scorer = BeamSearchScorer(
            batch_size=1,
            num_beams=num_beams,
            device=model.device,
        )

        # instantiate logits processors
        logits_processor = LogitsProcessorList(
            [
                MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
            ]
        )

        outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(outputs, ["Wie alt bist du?"])

    @slow
    def test_constrained_beam_search(self):
        model = GPT2LMHeadModel.from_pretrained("gpt2").to(torch_device)
        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

        force_tokens = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
        force_tokens_2 = tokenizer("big weapons", add_prefix_space=True, add_special_tokens=False).input_ids

        constraints = [
            PhrasalConstraint(force_tokens),
            PhrasalConstraint(force_tokens_2),
        ]

        starting_text = ["The soldiers were not prepared and"]

        input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)

        outputs = model.generate(
            input_ids,
            constraints=constraints,
            num_beams=10,
            num_return_sequences=1,
            no_repeat_ngram_size=1,
            max_length=30,
            remove_invalid_values=True,
        )

        generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(
            generated_text,
            [
                "The soldiers were not prepared and didn't know how big the big weapons would be, so they scared them"
                " off. They had no idea what to do",
            ],
        )

    @slow
    def test_constrained_beam_search_mixed(self):
        model = GPT2LMHeadModel.from_pretrained("gpt2").to(torch_device)
        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

        force_phrase = tokenizer("scared", add_prefix_space=True, add_special_tokens=False).input_ids
        flexible_phrases = tokenizer(
            ["scream", "screams", "screaming", "screamed"], add_prefix_space=True, add_special_tokens=False
        ).input_ids

        constraints = [
            PhrasalConstraint(force_phrase),
            DisjunctiveConstraint(flexible_phrases),
        ]

        starting_text = ["The soldiers", "The child"]

        input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)

        outputs = model.generate(
            input_ids,
            constraints=constraints,
            num_beams=10,
            num_return_sequences=1,
            no_repeat_ngram_size=1,
            # max_length=20,
            remove_invalid_values=True,
        )

        generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(
            generated_text,
            [
                "The soldiers, who were all scared and screaming at each other as they tried to get out of the",
                "The child was taken to a local hospital where she screamed and scared for her life, police said.",
            ],
        )

    @slow
    def test_constrained_beam_search_mixed_mixin(self):
        model = GPT2LMHeadModel.from_pretrained("gpt2").to(torch_device)
        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

        force_word = "scared"
        force_flexible = ["scream", "screams", "screaming", "screamed"]

        force_words_ids = [
            tokenizer([force_word], add_prefix_space=True, add_special_tokens=False).input_ids,
            tokenizer(force_flexible, add_prefix_space=True, add_special_tokens=False).input_ids,
        ]

        starting_text = ["The soldiers", "The child"]

        input_ids = tokenizer(starting_text, return_tensors="pt").input_ids.to(torch_device)

        outputs = model.generate(
            input_ids,
            force_words_ids=force_words_ids,
            num_beams=10,
            num_return_sequences=1,
            no_repeat_ngram_size=1,
            remove_invalid_values=True,
        )

        generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(
            generated_text,
            [
                "The soldiers, who were all scared and screaming at each other as they tried to get out of the",
                "The child was taken to a local hospital where she screamed and scared for her life, police said.",
            ],
        )

    @slow
    def test_constrained_beam_search_example_translation_mixin(self):
        tokenizer = AutoTokenizer.from_pretrained("t5-base")
        model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

        encoder_input_str = "translate English to German: How old are you?"
        force_words = ["sind"]

        input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
        force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids

        outputs = model.generate(
            input_ids,
            force_words_ids=force_words_ids,
            num_beams=10,
            num_return_sequences=1,
            no_repeat_ngram_size=1,
            remove_invalid_values=True,
        )

        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(outputs, ["Wie alter sind Sie?"])

    @slow
    def test_constrained_beam_search_example_integration(self):
        tokenizer = AutoTokenizer.from_pretrained("t5-base")
        model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

        encoder_input_str = "translate English to German: How old are you?"
        encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids

        # lets run beam search using 5 beams
        num_beams = 5
        # define decoder start token ids
        input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
        input_ids = input_ids * model.config.decoder_start_token_id

        # add encoder_outputs to model keyword arguments
        model_kwargs = {
            "encoder_outputs": model.get_encoder()(
                encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
            )
        }

        constraint_str = "sind"
        constraint_token_ids = tokenizer.encode(constraint_str)[:-1]  # remove eos token
        constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]

        # instantiate beam scorer
        beam_scorer = ConstrainedBeamSearchScorer(
            batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints
        )

        # instantiate logits processors
        logits_processor = LogitsProcessorList(
            [
                MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
            ]
        )

        outputs = model.constrained_beam_search(
            input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
        )
        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)

        self.assertListEqual(outputs, ["Wie alter sind Sie?"])

    def test_constrained_beam_search_mixin_type_checks(self):
        tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/t5-tiny-random")
        model = AutoModelForSeq2SeqLM.from_pretrained("patrickvonplaten/t5-tiny-random")

        encoder_input_str = "translate English to German: How old are you?"
        input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids

        with self.assertRaises(ValueError):
            force_words = ["sind"]
            force_words_ids = tokenizer(force_words, return_tensors="pt").input_ids
            model.generate(
                input_ids,
                force_words_ids=force_words_ids,
                num_beams=10,
                num_return_sequences=1,
                no_repeat_ngram_size=1,
                remove_invalid_values=True,
            )

        with self.assertRaises(ValueError):
            force_words = ["sind"]
            force_words_ids = [tokenizer(force_words, return_tensors="pt").input_ids]
            model.generate(
                input_ids,
                force_words_ids=force_words_ids,
                num_beams=10,
                num_return_sequences=1,
                no_repeat_ngram_size=1,
                remove_invalid_values=True,
            )

        with self.assertRaises(ValueError):
            model.generate(input_ids, force_words_ids=[])

        with self.assertRaises(ValueError):
            model.generate(input_ids, force_words_ids=[[-1]])

        with self.assertRaises(ValueError):
            model.generate(input_ids, force_words_ids=[[[-1]]])