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Utilities for Generation
-----------------------------------------------------------------------------------------------------------------------

This page lists all the utility functions used by :meth:`~transformers.PreTrainedModel.generate`,
:meth:`~transformers.PreTrainedModel.greedy_search`, :meth:`~transformers.PreTrainedModel.sample`,
:meth:`~transformers.PreTrainedModel.beam_search`, :meth:`~transformers.PreTrainedModel.beam_sample`, and
:meth:`~transformers.PreTrainedModel.group_beam_search`.

Most of those are only useful if you are studying the code of the generate methods in the library.

Generate Outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The output of :meth:`~transformers.PreTrainedModel.generate` is an instance of a subclass of
:class:`~transformers.file_utils.ModelOutput`. This output is a data structure containing all the information returned
by :meth:`~transformers.PreTrainedModel.generate`, but that can also be used as tuple or dictionary.

Here's an example:

.. code-block::

    from transformers import GPT2Tokenizer, GPT2LMHeadModel

    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    model = GPT2LMHeadModel.from_pretrained('gpt2')

    inputs = tokenizer("Hello, my dog is cute and ", return_tensors="pt")
    generation_output = model.generate(**inputs, return_dict_in_generate=True, output_scores=True)

The ``generation_output`` object is a :class:`~transformers.generation_utils.GreedySearchDecoderOnlyOutput`, as we can
see in the documentation of that class below, it means it has the following attributes:

- ``sequences``: the generated sequences of tokens
- ``scores`` (optional): the prediction scores of the language modelling head, for each generation step
- ``hidden_states`` (optional): the hidden states of the model, for each generation step
- ``attentions`` (optional): the attention weights of the model, for each generation step

Here we have the ``scores`` since we passed along ``output_scores=True``, but we don't have ``hidden_states`` and
``attentions`` because we didn't pass ``output_hidden_states=True`` or ``output_attentions=True``.

You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
will get ``None``. Here for instance ``generation_output.scores`` are all the generated prediction scores of the
language modeling head, and ``generation_output.attentions`` is ``None``.

When using our ``generation_output`` object as a tuple, it only keeps the attributes that don't have ``None`` values.
Here, for instance, it has two elements, ``loss`` then ``logits``, so

.. code-block::

    generation_output[:2]

will return the tuple ``(generation_output.sequences, generation_output.scores)`` for instance.

When using our ``generation_output`` object as a dictionary, it only keeps the attributes that don't have ``None``
values. Here, for instance, it has two keys that are ``sequences`` and ``scores``.

We document here all output types.


GreedySearchOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. autoclass:: transformers.generation_utils.GreedySearchDecoderOnlyOutput
    :members:

.. autoclass:: transformers.generation_utils.GreedySearchEncoderDecoderOutput
    :members:


SampleOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. autoclass:: transformers.generation_utils.SampleDecoderOnlyOutput
    :members:

.. autoclass:: transformers.generation_utils.SampleEncoderDecoderOutput
    :members:


BeamSearchOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. autoclass:: transformers.generation_utils.BeamSearchDecoderOnlyOutput
    :members:

.. autoclass:: transformers.generation_utils.BeamSearchEncoderDecoderOutput
    :members:


BeamSampleOutput
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. autoclass:: transformers.generation_utils.BeamSampleDecoderOnlyOutput
    :members:

.. autoclass:: transformers.generation_utils.BeamSampleEncoderDecoderOutput
    :members:


LogitsProcessor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

A :class:`~transformers.LogitsProcessor` can be used to modify the prediction scores of a language model head for
generation.

.. autoclass:: transformers.LogitsProcessor
    :members: __call__

.. autoclass:: transformers.LogitsProcessorList
    :members: __call__

.. autoclass:: transformers.LogitsWarper
    :members: __call__

.. autoclass:: transformers.MinLengthLogitsProcessor
    :members: __call__

.. autoclass:: transformers.TemperatureLogitsWarper
    :members: __call__

.. autoclass:: transformers.RepetitionPenaltyLogitsProcessor
    :members: __call__

.. autoclass:: transformers.TopPLogitsWarper
    :members: __call__

.. autoclass:: transformers.TopKLogitsWarper
    :members: __call__

.. autoclass:: transformers.NoRepeatNGramLogitsProcessor
    :members: __call__

.. autoclass:: transformers.NoBadWordsLogitsProcessor
    :members: __call__

.. autoclass:: transformers.PrefixConstrainedLogitsProcessor
    :members: __call__

.. autoclass:: transformers.HammingDiversityLogitsProcessor
    :members: __call__

BeamSearch
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.BeamScorer
    :members: process, finalize

.. autoclass:: transformers.BeamSearchScorer
    :members: process, finalize

Utilities
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autofunction:: transformers.top_k_top_p_filtering

.. autofunction:: transformers.tf_top_k_top_p_filtering
