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compositional_test / transformers /tests /generation /test_utils.py

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	# 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

	import numpy as np

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

	from ..test_modeling_common import floats_tensor, ids_tensor
	from .test_framework_agnostic import GenerationIntegrationTestsMixin


	if is_torch_available():
	import torch

	from transformers import (
	AutoModelForCausalLM,
	AutoModelForSeq2SeqLM,
	AutoModelForSpeechSeq2Seq,
	AutoModelForVision2Seq,
	AutoTokenizer,
	BartForConditionalGeneration,
	BartTokenizer,
	GPT2LMHeadModel,
	GPT2Tokenizer,
	ImageGPTForCausalImageModeling,
	SpeechEncoderDecoderModel,
	top_k_top_p_filtering,
	)
	from transformers.generation import (
	BeamSampleDecoderOnlyOutput,
	BeamSampleEncoderDecoderOutput,
	BeamSearchDecoderOnlyOutput,
	BeamSearchEncoderDecoderOutput,
	BeamSearchScorer,
	ConstrainedBeamSearchScorer,
	DisjunctiveConstraint,
	ForcedBOSTokenLogitsProcessor,
	ForcedEOSTokenLogitsProcessor,
	GreedySearchDecoderOnlyOutput,
	GreedySearchEncoderDecoderOutput,
	HammingDiversityLogitsProcessor,
	InfNanRemoveLogitsProcessor,
	LogitsProcessorList,
	MaxLengthCriteria,
	MinLengthLogitsProcessor,
	NoBadWordsLogitsProcessor,
	NoRepeatNGramLogitsProcessor,
	PhrasalConstraint,
	RepetitionPenaltyLogitsProcessor,
	SampleDecoderOnlyOutput,
	SampleEncoderDecoderOutput,
	StoppingCriteria,
	StoppingCriteriaList,
	TemperatureLogitsWarper,
	TopKLogitsWarper,
	TopPLogitsWarper,
	)


	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]

	# 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]

	# 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()`
	if isinstance(config.eos_token_id, int):
	config.eos_token_id = [config.eos_token_id]
	config.pad_token_id = config.eos_token_id[0]
	# TransfoXL has no attention mask
	if "transfoxl" in config.__class__.__name__.lower():
	attention_mask = None
	else:
	attention_mask = torch.ones_like(input_ids, dtype=torch.long)[:max_batch_size, :sequence_length]

	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 = {}
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	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,
	**model_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():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_greedy = model.greedy_search(
	input_ids,
	max_length=max_length,
	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,
	**model_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)
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	do_sample=True,
	num_beams=1,
	max_length=max_length,
	num_return_sequences=num_return_sequences,
	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,
	**model_kwargs,
	)

	torch.manual_seed(0)
	kwargs = {}
	if model.config.is_encoder_decoder:
	encoder_outputs, input_ids, attention_mask = 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
	elif attention_mask is not None:
	attention_mask = attention_mask.repeat_interleave(num_return_sequences, dim=0)

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

	with torch.no_grad():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_sample = model.sample(
	input_ids.repeat_interleave(num_return_sequences, dim=0),
	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,
	**model_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,
	):
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	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,
	**model_kwargs,
	)

	# beam_search does not automatically interleave `batch_size` dim for `num_beams`
	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,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	)
	kwargs["encoder_outputs"] = encoder_outputs
	elif attention_mask is not None:
	attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)

	with torch.no_grad():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_beam_search = model.beam_search(
	input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
	beam_scorer,
	max_length=max_length,
	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,
	**model_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)
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	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,
	**model_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
	elif attention_mask is not None:
	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():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	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,
	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,
	**model_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,
	):
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	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,
	**model_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, attention_mask = 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
	elif attention_mask is not None:
	attention_mask = attention_mask.repeat_interleave(beam_scorer.num_beams, dim=0)

	with torch.no_grad():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_group_beam_search = model.group_beam_search(
	input_ids.repeat_interleave(beam_scorer.num_beams, dim=0),
	beam_scorer,
	max_length=max_length,
	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,
	**model_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,
	):
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	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,
	**model_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, attention_mask = 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
	elif attention_mask is not None:
	attention_mask = attention_mask.repeat_interleave(constrained_beam_scorer.num_beams, dim=0)

	with torch.no_grad():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_group_beam_search = model.constrained_beam_search(
	input_ids.repeat_interleave(constrained_beam_scorer.num_beams, dim=0),
	constrained_beam_scorer,
	max_length=max_length,
	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,
	**model_kwargs,
	)
	return output_generate, output_group_beam_search

	def _contrastive_generate(
	self,
	model,
	input_ids,
	attention_mask,
	max_length,
	output_scores=False,
	output_attentions=False,
	output_hidden_states=False,
	return_dict_in_generate=False,
	):
	contrastive_search_kwargs = {
	"penalty_alpha": 0.6,
	"top_k": 5,
	}

	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 = {}
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	output_generate = model.generate(
	input_ids,
	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,
	**model_kwargs,
	**contrastive_search_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():
	model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
	stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])
	output_contrastive = model.contrastive_search(
	input_ids,
	stopping_criteria=stopping_criteria,
	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,
	**model_kwargs,
	**contrastive_search_kwargs,
	)
	return output_contrastive, output_generate

	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=2)

	# 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
	min_id = 3
	max_id = model.config.vocab_size
	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_contrastive_generate(self):
	# check `generate()` and `contrastive_search()` are equal
	for model_class in self.all_generative_model_classes:
	# won't fix: FSMT and Reformer have a different cache variable type (and format).
	if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
	return

	config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

	# NOTE: contrastive search only works with cache on at the moment.
	if not hasattr(config, "use_cache"):
	return
	config.use_cache = True
	config.is_decoder = True

	# test old generation output for backwards compatibility
	model = model_class(config).to(torch_device).eval()
	output_contrastive, output_generate = self._contrastive_generate(
	model=model, input_ids=input_ids, attention_mask=attention_mask, max_length=max_length
	)
	self.assertListEqual(output_contrastive.tolist(), output_generate.tolist())

	def test_contrastive_generate_dict_outputs_use_cache(self):
	for model_class in self.all_generative_model_classes:
	# won't fix: FSMT and Reformer have a different cache variable type (and format).
	if any(model_name in model_class.__name__.lower() for model_name in ["fsmt", "reformer"]):
	return

	# enable cache
	config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

	# NOTE: contrastive search only works with cache on at the moment.
	if not hasattr(config, "use_cache"):
	return
	config.use_cache = True
	config.is_decoder = True

	model = model_class(config).to(torch_device).eval()
	output_contrastive, output_generate = self._contrastive_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_contrastive.sequences.tolist())

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

	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()
	# We want to test only encoder-decoder models
	if not config.is_encoder_decoder:
	continue
	model = model_class(config).to(torch_device)

	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()) < {*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)

	# TODO (joao): this test is actually not slow :) However, it is not passing in some models (e.g. GPTNeoX) and the
	# fix for some models is quite lengthy. Being slow means it doesn't block our push CI while we fix it.
	@slow
	def test_left_padding_compatibility(self):
	# The check done in this test is fairly difficult -- depending on the model architecture, passing the right
	# position index for the position embeddings can still result in a different output, due to numerical masking.
	# On the other hand, for some types of position embeddings, an incorrect position index can have a minimal
	# impact on the output.
	# There are two tricks employed to check whether left-padding compatibility is in place:
	# 1 - To reduce the negative impact of the numerical attention mask on a correct position index, we set the
	# padding size to 1.
	# 2 - To reduce the chance of false positives (i.e. passing when it should be failing), we run the check
	# multiple times with random inputs, and it has to pass with all of them.
	# NOTE: because of 2), there is some chance of false positives in this test.

	for model_class in self.all_generative_model_classes:
	config, _, _, _ = self._get_input_ids_and_config()
	if config.is_encoder_decoder:
	continue # skip for encoder-decoder models -- they don't need left-padding compatibility
	model = model_class(config).to(torch_device).eval()
	signature = inspect.signature(model.forward).parameters.keys()

	no_failures = True
	for _ in range(10): # there may be false positives with 10 runs, we rely on the CI to catch the flakiness
	_, input_ids, attention_mask, _ = self._get_input_ids_and_config()
	model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask}
	if "position_ids" in signature:
	position_ids = torch.cumsum(attention_mask, dim=-1) - 1
	position_ids.masked_fill_(attention_mask == 0, 1)
	model_kwargs["position_ids"] = position_ids
	next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :]

	pad_size = (input_ids.shape[0], 1)
	padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.pad_token_id
	padded_input_ids = torch.cat((padding, input_ids), dim=1)
	padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1)
	model_kwargs = {"input_ids": padded_input_ids, "attention_mask": padded_attention_mask}
	if "position_ids" in signature:
	position_ids = torch.cumsum(padded_attention_mask, dim=-1) - 1
	position_ids.masked_fill_(padded_attention_mask == 0, 1)
	model_kwargs["position_ids"] = position_ids
	next_logits_with_padding = model(**model_kwargs).logits[:, -1, :]
	if not torch.allclose(next_logits_wo_padding, next_logits_with_padding):
	no_failures = False
	break

	self.assertTrue(no_failures)

	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, GenerationIntegrationTestsMixin):
	# setting framework_dependent_parameters needs to be gated, just like its contents' imports
	if is_torch_available():
	framework_dependent_parameters = {
	"AutoModelForCausalLM": AutoModelForCausalLM,
	"AutoModelForSpeechSeq2Seq": AutoModelForSpeechSeq2Seq,
	"AutoModelForSeq2SeqLM": AutoModelForSeq2SeqLM,
	"AutoModelForVision2Seq": AutoModelForVision2Seq,
	"LogitsProcessorList": LogitsProcessorList,
	"MinLengthLogitsProcessor": MinLengthLogitsProcessor,
	"create_tensor_fn": torch.tensor,
	"floats_tensor": floats_tensor,
	"return_tensors": "pt",
	}

	@slow
	def test_diverse_beam_search(self):
	# PT-only test: TF doesn't have a diverse beam search implementation
	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):
	# PT-only test: TF doesn't have StoppingCriteria
	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):
	# PT-only test: TF doesn't have StoppingCriteria
	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):
	# PT-only test: TF doesn't have StoppingCriteria
	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):
	# PT-only test: TF doesn't have StoppingCriteria & group beam search
	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):
	# PT-only test: TF doesn't have StoppingCriteria
	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_custom_stopping_criteria_overload_error(self):
	# PT-only test: TF doesn't have StoppingCriteria
	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):
	# PT-only test: TF doesn't have StoppingCriteria
	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_stop_sequence_stopping_criteria(self):
	# PT-only test: TF doesn't have StoppingCriteria
	prompt = """Hello I believe in"""
	generator = pipeline("text-generation", model="hf-internal-testing/tiny-random-bart")
	output = generator(prompt)
	self.assertEqual(
	output,
	[
	{
	"generated_text": (
	"Hello I believe in in in number number number number number number number number number"
	)
	}
	],
	)

	output = generator(prompt, stop_sequence=" number")
	self.assertEqual(output, [{"generated_text": "Hello I believe in in in number"}])

	def test_generate_non_nlp_input_ids_as_kwarg(self):
	# PT-only test: AFAIK there's no non-NLP model architecture in TF that supports `input_ids` as its only input
	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_values_as_encoder_kwarg(self):
	# PT-only test: AFAIK there's no generate-capable architecture in TF that supports `input_values` as its input
	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_transition_scores_group_beam_search_encoder_decoder(self):
	# PT-only test: TF doesn't have group beam search
	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_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_beam_search_example_integration(self):
	# PT-only test: TF doesn't have a BeamSearchScorer
	# 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):
	# PT-only test: TF doesn't have constrained beam search
	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 what to do. They had no idea how they would react if"
	" the enemy attacked them, big weapons scared"
	],
	)

	@slow
	def test_constrained_beam_search_mixed(self):
	# PT-only test: TF doesn't have constrained beam search
	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 had been stationed at the base for more than a year before being evacuated"
	" screaming scared",
	"The child was taken to a local hospital where he died.\n 'I don't think screaming scared",
	],
	)

	@slow
	def test_constrained_beam_search_mixed_mixin(self):
	# PT-only test: TF doesn't have constrained beam search
	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 had been stationed at the base for more than a year before being evacuated"
	" screaming scared",
	"The child was taken to a local hospital where he died.\n 'I don't think screaming scared",
	],
	)

	@slow
	def test_constrained_beam_search_example_translation_mixin(self):
	# PT-only test: TF doesn't have constrained beam search
	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 alt sind Sie?"])

	@slow
	def test_constrained_beam_search_example_integration(self):
	# PT-only test: TF doesn't have constrained beam search
	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 alt sind Sie?"])

	def test_constrained_beam_search_mixin_type_checks(self):
	# PT-only test: TF doesn't have constrained beam search
	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]]])

	def test_contrastive_search_batched(self):
	# PT-only test: TF doesn't have constrained beam search
	# Tests that contrastive search works with batched inputs (i.e. has the same output as for non-batched inputs)
	articles = ["Foo", "Bar Baz"]
	tokenizer = BartTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
	model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)

	model.config.eos_token_id = None
	input_ids_batched = tokenizer(articles, padding=True, return_tensors="pt").input_ids.to(torch_device)
	input_ids = tokenizer(articles[1], return_tensors="pt").input_ids.to(torch_device)

	output_sequences_batched = model.generate(
	input_ids=input_ids_batched, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True
	)
	output_sequences = model.generate(
	input_ids=input_ids, penalty_alpha=0.6, top_k=4, return_dict_in_generate=True, output_scores=True
	)

	batched_out = tokenizer.decode(output_sequences_batched.sequences[1], skip_special_tokens=True)
	out = tokenizer.decode(output_sequences.sequences[0], skip_special_tokens=True)
	self.assertEqual(batched_out, out)

	# output_sequences_batched.scores[0][1] -> 1st set of logits, 2nd sequence
	max_score_diff = (output_sequences_batched.scores[0][1] - output_sequences.scores[0][0]).abs().max()
	self.assertTrue(max_score_diff < 1e-5)

	def test_eos_token_id_int_and_list_top_k_top_sampling(self):
	# Has TF equivalent: this test relies on random sampling
	generation_kwargs = {
	"do_sample": True,
	"num_beams": 1,
	"top_p": 0.7,
	"top_k": 10,
	"temperature": 0.7,
	}
	expectation = 20

	tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
	text = """Hello, my dog is cute and"""
	tokens = tokenizer(text, return_tensors="pt").to(torch_device)
	model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)

	torch.manual_seed(0)
	eos_token_id = 846
	generated_tokens = model.generate(tokens, eos_token_id=eos_token_id, generation_kwargs)
	self.assertTrue(expectation == len(generated_tokens[0]))

	torch.manual_seed(0)
	eos_token_id = [846, 198]
	generated_tokens = model.generate(tokens, eos_token_id=eos_token_id, generation_kwargs)
	self.assertTrue(expectation == len(generated_tokens[0]))

	def test_generate_from_inputs_embeds_decoder_only(self):
	# PT-only test: TF doesn't have a model with support to generate from input embeds (yet ;))
	# Note: the model must support generation from input embeddings
	model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device)
	tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
	model.config.pad_token_id = tokenizer.eos_token_id

	text = "Hello world"
	tokenized_inputs = tokenizer([text, text], return_tensors="pt")
	input_ids = tokenized_inputs.input_ids.to(torch_device)

	# Traditional way of generating text
	outputs_from_ids = model.generate(input_ids)
	self.assertEqual(outputs_from_ids.shape, (2, 20))

	# Same thing, but from input embeddings
	inputs_embeds = model.transformer.wte(input_ids)
	outputs_from_embeds = model.generate(input_ids, inputs_embeds=inputs_embeds)
	self.assertListEqual(outputs_from_ids.tolist(), outputs_from_embeds.tolist())

	# But if we pass different inputs_embeds, we should get different outputs
	torch.manual_seed(0)
	random_embeds = torch.rand_like(inputs_embeds)
	outputs_from_rand_embeds = model.generate(input_ids, inputs_embeds=random_embeds)
	with self.assertRaises(AssertionError):
	self.assertListEqual(outputs_from_rand_embeds.tolist(), outputs_from_embeds.tolist())

	# input_ids is not a required input -- if we don't pass it, the newly generated tokens will be the same
	outputs_from_embeds_wo_ids = model.generate(
	inputs_embeds=inputs_embeds, max_new_tokens=20 - inputs_embeds.shape[1]
	)
	self.assertListEqual(
	outputs_from_embeds[:, inputs_embeds.shape[1] :].tolist(),
	outputs_from_embeds_wo_ids[:, 1:].tolist(),
	)

	def test_model_kwarg_encoder_signature_filtering(self):
	# Has TF equivalent: ample use of framework-specific code
	bart_tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
	article = """Hugging Face is a technology company based in New York and Paris."""
	input_ids = bart_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
	bart_model = BartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart").to(
	torch_device
	)
	output = bart_model.generate(input_ids).cpu().numpy()

	# Let's create a fake model that has a different signature. In particular, this fake model accepts "foo" as an
	# argument. Because "foo" is not in the encoder signature and doesn't start with "decoder_", it will be part of
	# the encoder kwargs prior to signature filtering, which would lead to an exception. But filtering kicks in and
	# saves the day.
	class FakeBart(BartForConditionalGeneration):
	def forward(self, input_ids, foo=None, **kwargs):
	return super().forward(input_ids, **kwargs)

	bart_model = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart").to(torch_device)
	fake_output = bart_model.generate(input_ids, foo="bar").cpu().numpy()
	self.assertTrue(np.array_equal(output, fake_output))

	# Encoder signature filtering only kicks in if it doesn't accept wildcard kwargs. The following test will fail
	# because it doesn't do signature filtering.
	class FakeEncoder(bart_model.model.encoder.__class__):
	def forward(self, input_ids, **kwargs):
	return super().forward(input_ids, **kwargs)

	fake_encoder = FakeEncoder(bart_model.config, bart_model.model.shared).to(torch_device)
	bart_model.model.encoder = fake_encoder

	# Normal generation still works (the output will be different because the encoder weights are different)
	fake_output = bart_model.generate(input_ids).cpu().numpy()
	with self.assertRaises(TypeError):
	# FakeEncoder.forward() accepts **kwargs -> no filtering -> type error due to unexpected input "foo"
	bart_model.generate(input_ids, foo="bar")