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	# coding=utf-8
	# Copyright 2018 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
	# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
	#
	# 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 copy 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 logging
	from typing import Iterable, Optional, Tuple

	import torch
	from torch import Tensor
	from torch.nn import functional as F


	logger = logging.getLogger(__name__)


	class GenerationMixin:
	"""
	A class contraining all of the functions supporting generation, to be used as a mixin in PreTrainedModel.
	"""

	def prepare_inputs_for_generation(self, input_ids, **kwargs):
	return {"input_ids": input_ids}

	def adjust_logits_during_generation(self, logits, **kwargs):
	return logits

	def _use_cache(self, outputs, use_cache):
	"""During generation, decide whether to pass the `past` variable to the next forward pass."""
	if len(outputs) <= 1 or use_cache is False:
	return False
	if hasattr(self.config, "mem_len") and self.config.mem_len == 0:
	return False
	return True

	def enforce_repetition_penalty_(self, lprobs, batch_size, num_beams, prev_output_tokens, repetition_penalty):
	"""repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858). """
	for i in range(batch_size * num_beams):
	for previous_token in set(prev_output_tokens[i].tolist()):
	# if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
	if lprobs[i, previous_token] < 0:
	lprobs[i, previous_token] *= repetition_penalty
	else:
	lprobs[i, previous_token] /= repetition_penalty

	def postprocess_next_token_scores(
	self,
	scores,
	input_ids,
	no_repeat_ngram_size,
	bad_words_ids,
	cur_len,
	min_length,
	max_length,
	eos_token_id,
	repetition_penalty,
	batch_size,
	num_beams,
	):
	# repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
	if repetition_penalty != 1.0:
	self.enforce_repetition_penalty_(
	scores, batch_size, num_beams, input_ids, repetition_penalty,
	)

	# set eos token prob to zero if min_length is not reached
	if eos_token_id is not None and cur_len < min_length:
	scores[:, eos_token_id] = -float("inf")

	if no_repeat_ngram_size > 0:
	# calculate a list of banned tokens to prevent repetitively generating the same ngrams
	num_batch_hypotheses = batch_size * num_beams
	# from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
	banned_batch_tokens = calc_banned_ngram_tokens(
	input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
	)
	for i, banned_tokens in enumerate(banned_batch_tokens):
	scores[i, banned_tokens] = -float("inf")

	if bad_words_ids is not None:
	# calculate a list of banned tokens according to bad words
	banned_tokens = calc_banned_bad_words_ids(input_ids, bad_words_ids)

	for i, banned_tokens in enumerate(banned_tokens):
	scores[i, banned_tokens] = -float("inf")

	return scores

	@torch.no_grad()
	def generate(
	self,
	input_ids: Optional[torch.LongTensor] = None,
	max_length: Optional[int] = None,
	min_length: Optional[int] = None,
	do_sample: Optional[bool] = None,
	early_stopping: Optional[bool] = None,
	num_beams: Optional[int] = None,
	temperature: Optional[float] = None,
	top_k: Optional[int] = None,
	top_p: Optional[float] = None,
	repetition_penalty: Optional[float] = None,
	bad_words_ids: Optional[Iterable[int]] = None,
	bos_token_id: Optional[int] = None,
	pad_token_id: Optional[int] = None,
	eos_token_id: Optional[int] = None,
	length_penalty: Optional[float] = None,
	no_repeat_ngram_size: Optional[int] = None,
	num_return_sequences: Optional[int] = None,
	attention_mask: Optional[torch.LongTensor] = None,
	decoder_start_token_id: Optional[int] = None,
	use_cache: Optional[bool] = None,
	**model_specific_kwargs
	) -> torch.LongTensor:
	r""" Generates sequences for models with a LM head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.

	Adapted in part from `Facebook's XLM beam search code`_.

	.. _`Facebook's XLM beam search code`:
	https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529


	Parameters:

	input_ids: (`optional`) `torch.LongTensor` of shape `(batch_size, sequence_length)`
	The sequence used as a prompt for the generation. If `None` the method initializes
	it as an empty `torch.LongTensor` of shape `(1,)`.

	max_length: (`optional`) int
	The max length of the sequence to be generated. Between `min_length` and infinity. Default to 20.

	min_length: (`optional`) int
	The min length of the sequence to be generated. Between 0 and infinity. Default to 0.

	do_sample: (`optional`) bool
	If set to `False` greedy decoding is used. Otherwise sampling is used. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.

	early_stopping: (`optional`) bool
	if set to `True` beam search is stopped when at least `num_beams` sentences finished per batch. Defaults to `False` as defined in `configuration_utils.PretrainedConfig`.

	num_beams: (`optional`) int
	Number of beams for beam search. Must be between 1 and infinity. 1 means no beam search. Default to 1.

	temperature: (`optional`) float
	The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.

	top_k: (`optional`) int
	The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.

	top_p: (`optional`) float
	The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.

	repetition_penalty: (`optional`) float
	The parameter for repetition penalty. Between 1.0 and infinity. 1.0 means no penalty. Default to 1.0.

	pad_token_id: (`optional`) int
	Padding token. Default to specicic model pad_token_id or None if it does not exist.

	bos_token_id: (`optional`) int
	BOS token. Defaults to `bos_token_id` as defined in the models config.

	eos_token_id: (`optional`) int
	EOS token. Defaults to `eos_token_id` as defined in the models config.

	length_penalty: (`optional`) float
	Exponential penalty to the length. Default to 1.

	no_repeat_ngram_size: (`optional`) int
	If set to int > 0, all ngrams of size `no_repeat_ngram_size` can only occur once.
	bad_words_ids: (`optional`) list of lists of int
	`bad_words_ids` contains tokens that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use `tokenizer.encode(bad_word, add_prefix_space=True)`.

	num_return_sequences: (`optional`) int
	The number of independently computed returned sequences for each element in the batch. Default to 1.

	attention_mask (`optional`) obj: `torch.LongTensor` of same shape as `input_ids`
	Mask to avoid performing attention on padding token indices.
	Mask values selected in ``[0, 1]``:
	``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
	Defaults to `None`.

	`What are attention masks? <../glossary.html#attention-mask>`__

	decoder_start_token_id=None: (`optional`) int
	If an encoder-decoder model starts decoding with a different token than BOS.
	Defaults to `None` and is changed to `BOS` later.

	use_cache: (`optional`) bool
	If `use_cache` is True, past key values are used to speed up decoding if applicable to model. Defaults to `True`.

	model_specific_kwargs: (`optional`) dict
	Additional model specific kwargs will be forwarded to the `forward` function of the model.

	Return:

	output: `torch.LongTensor` of shape `(batch_size * num_return_sequences, sequence_length)`
	sequence_length is either equal to max_length or shorter if all batches finished early due to the `eos_token_id`

	Examples::

	tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer
	model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache.
	outputs = model.generate(max_length=40) # do greedy decoding
	print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

	tokenizer = AutoTokenizer.from_pretrained('openai-gpt') # Initialize tokenizer
	model = AutoModelWithLMHead.from_pretrained('openai-gpt') # Download model and configuration from S3 and cache.
	input_context = 'The dog'
	input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context
	outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5) # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
	for i in range(3): # 3 output sequences were generated
	print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

	tokenizer = AutoTokenizer.from_pretrained('distilgpt2') # Initialize tokenizer
	model = AutoModelWithLMHead.from_pretrained('distilgpt2') # Download model and configuration from S3 and cache.
	input_context = 'The dog'
	input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context
	outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3) # 3 generate sequences using by sampling
	for i in range(3): # 3 output sequences were generated
	print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

	tokenizer = AutoTokenizer.from_pretrained('ctrl') # Initialize tokenizer
	model = AutoModelWithLMHead.from_pretrained('ctrl') # Download model and configuration from S3 and cache.
	input_context = 'Legal My neighbor is' # "Legal" is one of the control codes for ctrl
	input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context
	outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2) # generate sequences
	print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

	tokenizer = AutoTokenizer.from_pretrained('gpt2') # Initialize tokenizer
	model = AutoModelWithLMHead.from_pretrained('gpt2') # Download model and configuration from S3 and cache.
	input_context = 'My cute dog' # "Legal" is one of the control codes for ctrl
	bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
	input_ids = tokenizer.encode(input_context, return_tensors='pt') # encode input context
	outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids) # generate sequences without allowing bad_words to be generated
	"""

	# We cannot generate if the model does not have a LM head
	if self.get_output_embeddings() is None:
	raise AttributeError(
	"You tried to generate sequences with a model that does not have a LM Head."
	"Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )"
	)

	max_length = max_length if max_length is not None else self.config.max_length
	min_length = min_length if min_length is not None else self.config.min_length
	do_sample = do_sample if do_sample is not None else self.config.do_sample
	early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
	use_cache = use_cache if use_cache is not None else self.config.use_cache
	num_beams = num_beams if num_beams is not None else self.config.num_beams
	temperature = temperature if temperature is not None else self.config.temperature
	top_k = top_k if top_k is not None else self.config.top_k
	top_p = top_p if top_p is not None else self.config.top_p
	repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty
	bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
	pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
	eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
	length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
	no_repeat_ngram_size = (
	no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
	)
	bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids
	num_return_sequences = (
	num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
	)
	decoder_start_token_id = (
	decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id
	)

	if input_ids is not None:
	batch_size = input_ids.shape[0] # overriden by the input batch_size
	else:
	batch_size = 1

	assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
	assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
	assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
	assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
	assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
	assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
	assert temperature > 0, "`temperature` should be strictly positive."
	assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
	assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1."
	assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1."
	assert input_ids is not None or (
	isinstance(bos_token_id, int) and bos_token_id >= 0
	), "If input_ids is not defined, `bos_token_id` should be a positive integer."
	assert pad_token_id is None or (
	isinstance(pad_token_id, int) and (pad_token_id >= 0)
	), "`pad_token_id` should be a positive integer."
	assert (eos_token_id is None) or (
	isinstance(eos_token_id, int) and (eos_token_id >= 0)
	), "`eos_token_id` should be a positive integer."
	assert length_penalty > 0, "`length_penalty` should be strictly positive."
	assert (
	isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
	), "`no_repeat_ngram_size` should be a positive integer."
	assert (
	isinstance(num_return_sequences, int) and num_return_sequences > 0
	), "`num_return_sequences` should be a strictly positive integer."
	assert (
	bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list)
	), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated"

	if input_ids is None:
	assert isinstance(bos_token_id, int) and bos_token_id >= 0, (
	"you should either supply a context to complete as `input_ids` input "
	"or a `bos_token_id` (integer >= 0) as a first token to start the generation."
	)
	input_ids = torch.full(
	(batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device,
	)
	else:
	assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."

	# not allow to duplicate outputs when greedy decoding
	if do_sample is False:
	if num_beams == 1:
	# no_beam_search greedy generation conditions
	assert (
	num_return_sequences == 1
	), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1"

	else:
	# beam_search greedy generation conditions
	assert (
	num_beams >= num_return_sequences
	), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"

	# create attention mask if necessary
	# TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
	if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):
	attention_mask = input_ids.ne(pad_token_id).long()
	elif attention_mask is None:
	attention_mask = input_ids.new_ones(input_ids.shape)

	# set pad_token_id to eos_token_id if not set. Important that this is done after
	# attention_mask is created
	if pad_token_id is None and eos_token_id is not None:
	logger.warning(
	"Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id)
	)
	pad_token_id = eos_token_id

	# current position and vocab size
	if hasattr(self.config, "vocab_size"):
	vocab_size = self.config.vocab_size
	elif (
	self.config.is_encoder_decoder
	and hasattr(self.config, "decoder")
	and hasattr(self.config.decoder, "vocab_size")
	):
	vocab_size = self.config.decoder.vocab_size

	# set effective batch size and effective batch multiplier according to do_sample
	if do_sample:
	effective_batch_size = batch_size * num_return_sequences
	effective_batch_mult = num_return_sequences
	else:
	effective_batch_size = batch_size
	effective_batch_mult = 1

	if self.config.is_encoder_decoder:
	if decoder_start_token_id is None:
	decoder_start_token_id = bos_token_id

	assert (
	decoder_start_token_id is not None
	), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation"
	assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self)
	assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder)

	# get encoder and store encoder outputs
	encoder = self.get_encoder()

	encoder_outputs: tuple = encoder(input_ids, attention_mask=attention_mask)

	# Expand input ids if num_beams > 1 or num_return_sequences > 1
	if num_return_sequences > 1 or num_beams > 1:
	input_ids_len = input_ids.shape[-1]
	input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len)
	attention_mask = attention_mask.unsqueeze(1).expand(
	batch_size, effective_batch_mult * num_beams, input_ids_len
	)

	input_ids = input_ids.contiguous().view(
	effective_batch_size * num_beams, input_ids_len
	) # shape: (batch_size * num_return_sequences * num_beams, cur_len)
	attention_mask = attention_mask.contiguous().view(
	effective_batch_size * num_beams, input_ids_len
	) # shape: (batch_size * num_return_sequences * num_beams, cur_len)

	if self.config.is_encoder_decoder:
	# create empty decoder_input_ids
	input_ids = torch.full(
	(effective_batch_size * num_beams, 1),
	decoder_start_token_id,
	dtype=torch.long,
	device=next(self.parameters()).device,
	)
	cur_len = 1

	assert (
	batch_size == encoder_outputs[0].shape[0]
	), f"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} "

	# expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1)
	expanded_batch_idxs = (
	torch.arange(batch_size)
	.view(-1, 1)
	.repeat(1, num_beams * effective_batch_mult)
	.view(-1)
	.to(input_ids.device)
	)
	# expand encoder_outputs
	encoder_outputs = (encoder_outputs[0].index_select(0, expanded_batch_idxs), *encoder_outputs[1:])

	else:
	encoder_outputs = None
	cur_len = input_ids.shape[-1]

	assert (
	cur_len < max_length
	), f"The context has {cur_len} number of tokens, but `max_length` is only {max_length}. Please make sure that `max_length` is bigger than the number of tokens, by setting either `generate(max_length=...,...)` or `config.max_length = ...`"

	if num_beams > 1:
	output = self._generate_beam_search(
	input_ids,
	cur_len=cur_len,
	max_length=max_length,
	min_length=min_length,
	do_sample=do_sample,
	early_stopping=early_stopping,
	temperature=temperature,
	top_k=top_k,
	top_p=top_p,
	repetition_penalty=repetition_penalty,
	no_repeat_ngram_size=no_repeat_ngram_size,
	bad_words_ids=bad_words_ids,
	pad_token_id=pad_token_id,
	eos_token_id=eos_token_id,
	batch_size=effective_batch_size,
	num_return_sequences=num_return_sequences,
	length_penalty=length_penalty,
	num_beams=num_beams,
	vocab_size=vocab_size,
	encoder_outputs=encoder_outputs,
	attention_mask=attention_mask,
	use_cache=use_cache,
	model_specific_kwargs=model_specific_kwargs,
	)
	else:
	output = self._generate_no_beam_search(
	input_ids,
	cur_len=cur_len,
	max_length=max_length,
	min_length=min_length,
	do_sample=do_sample,
	temperature=temperature,
	top_k=top_k,
	top_p=top_p,
	repetition_penalty=repetition_penalty,
	no_repeat_ngram_size=no_repeat_ngram_size,
	bad_words_ids=bad_words_ids,
	pad_token_id=pad_token_id,
	eos_token_id=eos_token_id,
	batch_size=effective_batch_size,
	encoder_outputs=encoder_outputs,
	attention_mask=attention_mask,
	use_cache=use_cache,
	model_specific_kwargs=model_specific_kwargs,
	)

	return output

	def _generate_no_beam_search(
	self,
	input_ids,
	cur_len,
	max_length,
	min_length,
	do_sample,
	temperature,
	top_k,
	top_p,
	repetition_penalty,
	no_repeat_ngram_size,
	bad_words_ids,
	pad_token_id,
	eos_token_id,
	batch_size,
	encoder_outputs,
	attention_mask,
	use_cache,
	model_specific_kwargs,
	):
	""" Generate sequences for each example without beam search (num_beams == 1).
	All returned sequence are generated independantly.
	"""
	# length of generated sentences / unfinished sentences
	unfinished_sents = input_ids.new(batch_size).fill_(1)
	sent_lengths = input_ids.new(batch_size).fill_(max_length)

	past = (encoder_outputs, None) if encoder_outputs is not None else None

	while cur_len < max_length:
	model_inputs = self.prepare_inputs_for_generation(
	input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_specific_kwargs
	)

	outputs = self(**model_inputs)
	next_token_logits = outputs[0][:, -1, :]

	scores = self.postprocess_next_token_scores(
	scores=next_token_logits,
	input_ids=input_ids,
	no_repeat_ngram_size=no_repeat_ngram_size,
	bad_words_ids=bad_words_ids,
	cur_len=cur_len,
	min_length=min_length,
	max_length=max_length,
	eos_token_id=eos_token_id,
	repetition_penalty=repetition_penalty,
	batch_size=batch_size,
	num_beams=1,
	)

	# if model has past, then set the past variable to speed up decoding
	if self._use_cache(outputs, use_cache):
	past = outputs[1]

	if do_sample:
	# Temperature (higher temperature => more likely to sample low probability tokens)
	if temperature != 1.0:
	scores = scores / temperature
	# Top-p/top-k filtering
	next_token_logscores = top_k_top_p_filtering(scores, top_k=top_k, top_p=top_p)
	# Sample
	probs = F.softmax(next_token_logscores, dim=-1)
	next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
	else:
	# Greedy decoding
	next_token = torch.argmax(next_token_logits, dim=-1)

	# update generations and finished sentences
	if eos_token_id is not None:
	# pad finished sentences if eos_token_id exist
	tokens_to_add = next_token * unfinished_sents + (pad_token_id) * (1 - unfinished_sents)
	else:
	tokens_to_add = next_token

	# add token and increase length by one
	input_ids = torch.cat([input_ids, tokens_to_add.unsqueeze(-1)], dim=-1)
	cur_len = cur_len + 1

	if eos_token_id is not None:
	eos_in_sents = tokens_to_add == eos_token_id
	# if sentence is unfinished and the token to add is eos, sent_lengths is filled with current length
	is_sents_unfinished_and_token_to_add_is_eos = unfinished_sents.mul(eos_in_sents.long()).bool()
	sent_lengths.masked_fill_(is_sents_unfinished_and_token_to_add_is_eos, cur_len)
	# unfinished_sents is set to zero if eos in sentence
	unfinished_sents.mul_((~eos_in_sents).long())

	# stop when there is a </s> in each sentence, or if we exceed the maximul length
	if unfinished_sents.max() == 0:
	break

	# extend attention_mask for new generated input if only decoder
	if self.config.is_encoder_decoder is False:
	attention_mask = torch.cat(
	[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
	)

	return input_ids

	def _generate_beam_search(
	self,
	input_ids,
	cur_len,
	max_length,
	min_length,
	do_sample,
	early_stopping,
	temperature,
	top_k,
	top_p,
	repetition_penalty,
	no_repeat_ngram_size,
	bad_words_ids,
	pad_token_id,
	eos_token_id,
	batch_size,
	num_return_sequences,
	length_penalty,
	num_beams,
	vocab_size,
	encoder_outputs,
	attention_mask,
	use_cache,
	model_specific_kwargs,
	):
	""" Generate sequences for each example with beam search.
	"""

	# generated hypotheses
	generated_hyps = [
	BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
	for _ in range(batch_size)
	]

	# scores for each sentence in the beam
	beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)

	# for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
	if do_sample is False:
	beam_scores[:, 1:] = -1e9
	beam_scores = beam_scores.view(-1) # shape (batch_size * num_beams,)

	# cache compute states
	past = (encoder_outputs, None) if encoder_outputs is not None else None

	# done sentences
	done = [False for _ in range(batch_size)]

	while cur_len < max_length:
	model_inputs = self.prepare_inputs_for_generation(
	input_ids, past=past, attention_mask=attention_mask, use_cache=use_cache, **model_specific_kwargs
	)
	outputs = self(*model_inputs) # (batch_size num_beams, cur_len, vocab_size)
	next_token_logits = outputs[0][:, -1, :] # (batch_size * num_beams, vocab_size)

	# if model has past, then set the past variable to speed up decoding
	if self._use_cache(outputs, use_cache):
	past = outputs[1]
	if self.config.is_encoder_decoder and do_sample is False:
	# TODO (PVP) still a bit hacky here - there might be a better solution
	next_token_logits = self.adjust_logits_during_generation(
	next_token_logits, cur_len=cur_len, max_length=max_length
	)

	scores = F.log_softmax(next_token_logits, dim=-1) # (batch_size * num_beams, vocab_size)

	scores = self.postprocess_next_token_scores(
	scores=scores,
	input_ids=input_ids,
	no_repeat_ngram_size=no_repeat_ngram_size,
	bad_words_ids=bad_words_ids,
	cur_len=cur_len,
	min_length=min_length,
	max_length=max_length,
	eos_token_id=eos_token_id,
	repetition_penalty=repetition_penalty,
	batch_size=batch_size,
	num_beams=num_beams,
	)

	assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format(
	scores.shape, (batch_size * num_beams, vocab_size)
	)

	if do_sample:
	_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size)
	# Temperature
	if temperature != 1.0:
	_scores = _scores / temperature
	# Top-p/top-k filtering
	_scores = top_k_top_p_filtering(
	_scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
	) # (batch_size * num_beams, vocab_size)
	# re-organize to group the beam together to sample from all beam_idxs
	_scores = _scores.contiguous().view(
	batch_size, num_beams * vocab_size
	) # (batch_size, num_beams * vocab_size)

	# Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
	probs = F.softmax(_scores, dim=-1)
	next_tokens = torch.multinomial(probs, num_samples=2 * num_beams) # (batch_size, num_beams * 2)
	# Compute next scores
	next_scores = torch.gather(_scores, -1, next_tokens) # (batch_size, num_beams * 2)
	# sort the sampled vector to make sure that the first num_beams samples are the best
	next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)
	next_tokens = torch.gather(next_tokens, -1, next_scores_indices) # (batch_size, num_beams * 2)

	else:
	next_scores = scores + beam_scores[:, None].expand_as(scores) # (batch_size * num_beams, vocab_size)

	# re-organize to group the beam together (we are keeping top hypothesis accross beams)
	next_scores = next_scores.view(
	batch_size, num_beams * vocab_size
	) # (batch_size, num_beams * vocab_size)

	next_scores, next_tokens = torch.topk(next_scores, 2 * num_beams, dim=1, largest=True, sorted=True)

	assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)

	# next batch beam content
	next_batch_beam = []

	# for each sentence
	for batch_idx in range(batch_size):

	# if we are done with this sentence, add a pad token
	if done[batch_idx]:
	assert (
	len(generated_hyps[batch_idx]) >= num_beams
	), "Batch can only be done if at least {} beams have been generated".format(num_beams)
	assert (
	eos_token_id is not None and pad_token_id is not None
	), "generated beams >= num_beams -> eos_token_id and pad_token have to be defined"
	next_batch_beam.extend([(0, pad_token_id, 0)] * num_beams) # pad the batch
	continue

	# next sentence beam content, this will get added to next_batch_beam
	next_sent_beam = []

	# next tokens for this sentence
	for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
	zip(next_tokens[batch_idx], next_scores[batch_idx])
	):
	# get beam and token IDs
	beam_id = beam_token_id // vocab_size
	token_id = beam_token_id % vocab_size

	effective_beam_id = batch_idx * num_beams + beam_id
	# add to generated hypotheses if end of sentence
	if (eos_token_id is not None) and (token_id.item() == eos_token_id):
	# if beam_token does not belong to top num_beams tokens, it should not be added
	is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
	if is_beam_token_worse_than_top_num_beams:
	continue
	generated_hyps[batch_idx].add(
	input_ids[effective_beam_id].clone(), beam_token_score.item(),
	)
	else:
	# add next predicted token since it is not eos_token
	next_sent_beam.append((beam_token_score, token_id, effective_beam_id))

	# once the beam for next step is full, don't add more tokens to it.
	if len(next_sent_beam) == num_beams:
	break

	# Check if we are done so that we can save a pad step if all(done)
	done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
	next_scores[batch_idx].max().item(), cur_len
	)

	# update next beam content
	assert len(next_sent_beam) == num_beams, "Beam should always be full"
	next_batch_beam.extend(next_sent_beam)
	assert len(next_batch_beam) == num_beams * (batch_idx + 1), "We should have added num_beams each step"

	# stop when we are done with each sentence
	if all(done):
	break

	# sanity check / prepare next batch
	assert len(next_batch_beam) == batch_size * num_beams
	beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
	beam_tokens = input_ids.new([x[1] for x in next_batch_beam])
	beam_idx = input_ids.new([x[2] for x in next_batch_beam])

	# re-order batch and update current length
	input_ids = input_ids[beam_idx, :]
	input_ids = torch.cat([input_ids, beam_tokens.unsqueeze(1)], dim=-1)
	cur_len = cur_len + 1

	# re-order internal states
	if past is not None:
	past = self._reorder_cache(past, beam_idx)

	# extend attention_mask for new generated input if only decoder
	if self.config.is_encoder_decoder is False:
	attention_mask = torch.cat(
	[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
	)

	# finalize all open beam hypotheses and add to generated hypotheses
	for batch_idx in range(batch_size):
	if done[batch_idx]:
	continue

	# test that beam scores match previously calculated scores if not eos and batch_idx not done
	if eos_token_id is not None and all(
	(token_id % vocab_size).item() != eos_token_id for token_id in next_tokens[batch_idx]
	):
	assert torch.all(
	next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
	), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
	next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx],
	)

	# need to add best num_beams hypotheses to generated hyps
	for beam_id in range(num_beams):
	effective_beam_id = batch_idx * num_beams + beam_id
	final_score = beam_scores[effective_beam_id].item()
	final_tokens = input_ids[effective_beam_id]
	generated_hyps[batch_idx].add(final_tokens, final_score)

	# depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
	output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
	output_num_return_sequences_per_batch = 1 if do_sample else num_return_sequences

	# select the best hypotheses
	sent_lengths = input_ids.new(output_batch_size)
	best = []

	# retrieve best hypotheses
	for i, hypotheses in enumerate(generated_hyps):
	sorted_hyps = sorted(hypotheses.beams, key=lambda x: x[0])
	for j in range(output_num_return_sequences_per_batch):
	effective_batch_idx = output_num_return_sequences_per_batch * i + j
	best_hyp = sorted_hyps.pop()[1]
	sent_lengths[effective_batch_idx] = len(best_hyp)
	best.append(best_hyp)

	# shorter batches are padded
	if sent_lengths.min().item() != sent_lengths.max().item():
	assert pad_token_id is not None, "`Pad_token_id` has to be defined"
	sent_max_len = min(sent_lengths.max().item() + 1, max_length)
	decoded = input_ids.new(output_batch_size, sent_max_len).fill_(pad_token_id)

	# fill with hypothesis and eos_token_id if necessary
	for i, hypo in enumerate(best):
	decoded[i, : sent_lengths[i]] = hypo
	if sent_lengths[i] < max_length:
	decoded[i, sent_lengths[i]] = eos_token_id
	else:
	# none of the hypotheses have an eos_token
	assert (len(hypo) == max_length for hypo in best)
	decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)

	return decoded

	@staticmethod
	def _reorder_cache(past: Tuple, beam_idx: Tensor) -> Tuple[Tensor]:
	return tuple(layer_past.index_select(1, beam_idx) for layer_past in past)


	def calc_banned_ngram_tokens(prev_input_ids: Tensor, num_hypos: int, no_repeat_ngram_size: int, cur_len: int) -> None:
	"""Copied from fairseq for no_repeat_ngram in beam_search"""
	if cur_len + 1 < no_repeat_ngram_size:
	# return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
	return [[] for _ in range(num_hypos)]
	generated_ngrams = [{} for _ in range(num_hypos)]
	for idx in range(num_hypos):
	gen_tokens = prev_input_ids[idx].tolist()
	generated_ngram = generated_ngrams[idx]
	for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
	prev_ngram_tuple = tuple(ngram[:-1])
	generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]

	def _get_generated_ngrams(hypo_idx):
	# Before decoding the next token, prevent decoding of ngrams that have already appeared
	start_idx = cur_len + 1 - no_repeat_ngram_size
	ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())
	return generated_ngrams[hypo_idx].get(ngram_idx, [])

	banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
	return banned_tokens


	def calc_banned_bad_words_ids(prev_input_ids: Iterable[int], bad_words_ids: Iterable[int]) -> Iterable[int]:
	banned_tokens = []

	def _tokens_match(prev_tokens, tokens):
	if len(tokens) == 0:
	# if bad word tokens is just one token always ban it
	return True
	if len(tokens) > len(prev_input_ids):
	# if bad word tokens are longer then prev input_ids they can't be equal
	return False

	if prev_tokens[-len(tokens) :] == tokens:
	# if tokens match
	return True
	else:
	return False

	for prev_input_ids_slice in prev_input_ids:
	banned_tokens_slice = []

	for banned_token_seq in bad_words_ids:
	assert len(banned_token_seq) > 0, "Banned words token sequences {} cannot have an empty list".format(
	bad_words_ids
	)

	if _tokens_match(prev_input_ids_slice.tolist(), banned_token_seq[:-1]) is False:
	# if tokens do not match continue
	continue

	banned_tokens_slice.append(banned_token_seq[-1])

	banned_tokens.append(banned_tokens_slice)

	return banned_tokens


	def top_k_top_p_filtering(
	logits: Tensor,
	top_k: int = 0,
	top_p: float = 1.0,
	filter_value: float = -float("Inf"),
	min_tokens_to_keep: int = 1,
	) -> Tensor:
	""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
	Args:
	logits: logits distribution shape (batch size, vocabulary size)
	if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
	if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
	Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
	Make sure we keep at least min_tokens_to_keep per batch example in the output
	From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
	"""
	if top_k > 0:
	top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1)) # Safety check
	# Remove all tokens with a probability less than the last token of the top-k
	indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
	logits[indices_to_remove] = filter_value

	if top_p < 1.0:
	sorted_logits, sorted_indices = torch.sort(logits, descending=True)
	cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

	# Remove tokens with cumulative probability above the threshold (token with 0 are kept)
	sorted_indices_to_remove = cumulative_probs > top_p
	if min_tokens_to_keep > 1:
	# Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
	sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
	# Shift the indices to the right to keep also the first token above the threshold
	sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
	sorted_indices_to_remove[..., 0] = 0

	# scatter sorted tensors to original indexing
	indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
	logits[indices_to_remove] = filter_value
	return logits


	class BeamHypotheses(object):
	def __init__(self, num_beams, max_length, length_penalty, early_stopping):
	"""
	Initialize n-best list of hypotheses.
	"""
	self.max_length = max_length - 1 # ignoring bos_token
	self.length_penalty = length_penalty
	self.early_stopping = early_stopping
	self.num_beams = num_beams
	self.beams = []
	self.worst_score = 1e9

	def __len__(self):
	"""
	Number of hypotheses in the list.
	"""
	return len(self.beams)

	def add(self, hyp, sum_logprobs):
	"""
	Add a new hypothesis to the list.
	"""
	score = sum_logprobs / len(hyp) ** self.length_penalty
	if len(self) < self.num_beams or score > self.worst_score:
	self.beams.append((score, hyp))
	if len(self) > self.num_beams:
	sorted_scores = sorted([(s, idx) for idx, (s, _) in enumerate(self.beams)])
	del self.beams[sorted_scores[0][1]]
	self.worst_score = sorted_scores[1][0]
	else:
	self.worst_score = min(score, self.worst_score)

	def is_done(self, best_sum_logprobs, cur_len):
	"""
	If there are enough hypotheses and that none of the hypotheses being generated
	can become better than the worst one in the heap, then we are done with this sentence.
	"""

	if len(self) < self.num_beams:
	return False
	elif self.early_stopping:
	return True
	else:
	cur_score = best_sum_logprobs / cur_len ** self.length_penalty
	ret = self.worst_score >= cur_score
	return ret