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class TFLogitsProcessorList(list):
"""
This class can be used to create a list of [`TFLogitsProcessor`] to subsequently process a `scores` input tensor.
This class inherits from list and adds a specific *__call__* method to apply each [`TFLogitsProcessor`] to the
inputs.
"""
@add_start_docstrin... | class_definition | 2,976 | 4,096 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,700 |
class TFTemperatureLogitsWarper(TFLogitsWarper):
r"""
[`TFLogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
if ... | class_definition | 4,099 | 4,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,701 |
class TFTopKLogitsWarper(TFLogitsWarper):
r"""
[`TFLogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *optional*,... | class_definition | 4,785 | 6,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,702 |
class TFTopPLogitsWarper(TFLogitsWarper):
"""
[`TFLogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to <= prob_cut_off.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
h... | class_definition | 6,116 | 8,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,703 |
class TFMinLengthLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`int`):
... | class_definition | 8,985 | 10,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,704 |
class TFRepetitionPenaltyLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] enforcing an exponential penalty on repeated sequences.
Args:
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/... | class_definition | 10,419 | 12,631 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,705 |
class TFNoBadWordsLogitsProcessor(TFLogitsProcessor):
"""
[`TFLogitsProcessor`] that enforces that specified sequences will never be sampled.
Args:
bad_words_ids (`List[List[int]]`):
List of list of token ids that are not allowed to be generated. In order to get the tokens of the words
... | class_definition | 12,634 | 18,398 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,706 |
class TFNoRepeatNGramLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] that enforces no repetition of n-grams. See
[Fairseq](https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345).
Args:
ngram_size (`int`):
A... | class_definition | 18,401 | 21,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,707 |
class TFForcedBOSTokenLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] that enforces the specified token as the first generated token.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
"""
def __init__(self, bos_token_id: int):... | class_definition | 21,286 | 22,543 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,708 |
class TFForcedEOSTokenLogitsProcessor(TFLogitsProcessor):
r"""
[`TFLogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`int`):
... | class_definition | 22,546 | 24,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,709 |
class TFSuppressTokensAtBeginLogitsProcessor(TFLogitsProcessor):
r"""
[`TFSuppressTokensAtBeginLogitsProcessor`] suppresses a list of tokens as soon as the `generate` function starts
generating using `begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` at not
sampl... | class_definition | 24,025 | 25,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,710 |
class TFSuppressTokensLogitsProcessor(TFLogitsProcessor):
r"""This processor can be used to suppress a list of tokens. The processor will set their log probs to `-inf` so that they
are not sampled."""
def __init__(self, suppress_tokens):
self.suppress_tokens = list(suppress_tokens)
def __call_... | class_definition | 25,360 | 26,363 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,711 |
class TFForceTokensLogitsProcessor(TFLogitsProcessor):
r"""This processor takes a list of pairs of integers which indicates a mapping from generation indices to token
indices that will be forced before sampling. The processor will set their log probs to `0` and all other tokens to
`-inf` so that they are sa... | class_definition | 26,366 | 28,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_logits_process.py | null | 10,712 |
class StoppingCriteria(ABC):
"""Abstract base class for all stopping criteria that can be applied during generation.
If your stopping criteria depends on the `scores` input, make sure you pass `return_dict_in_generate=True,
output_scores=True` to `generate`.
"""
@add_start_docstrings(STOPPING_CRIT... | class_definition | 1,751 | 2,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,713 |
class MaxLengthCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generated number of tokens exceeds `max_length`. Keep
in mind for decoder-only type of transformers, this will include the initial prompted tokens.
Args:
max_length (`int`):
Th... | class_definition | 2,283 | 3,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,714 |
class MaxTimeCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the full generation exceeds some amount of time. By default, the
time will start being counted when you initialize this function. You can override this by passing an
`initial_time`.
Args:
max_tim... | class_definition | 3,852 | 4,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,715 |
class StopStringCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever specific string sequences are generated. It preprocesses
the strings together with the tokenizer vocab to find positions where tokens can validly complete the stop strings.
Generation is stopped as soon a... | class_definition | 4,948 | 25,429 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,716 |
class EosTokenCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever the "end-of-sequence" token is generated.
By default, it uses the `model.generation_config.eos_token_id`.
Args:
eos_token_id (`Union[int, List[int], torch.Tensor]`):
The id(s) of the *en... | class_definition | 25,432 | 26,435 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,717 |
class ConfidenceCriteria(StoppingCriteria):
"""
This class can be used to stop generation whenever assistant model's confidence in its prediction for the current token is lower than the threshold
`model.generation_config.assistant_confidence_threshold` even if the number of speculative tokens (defined b... | class_definition | 26,438 | 27,339 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,718 |
class StoppingCriteriaList(list):
@add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
is_done = torch.full((input_ids.shape[0],), False, device=input_ids.device, dtype=torch.bool)
for ... | class_definition | 27,342 | 28,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/stopping_criteria.py | null | 10,719 |
class CandidateGenerator:
"""Abstract base class for all candidate generators that can be applied during assisted generation."""
def get_candidates(self, input_ids: torch.LongTensor) -> Tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""
Fetches the candidates to be tried for the current ... | class_definition | 1,176 | 3,271 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/candidate_generator.py | null | 10,720 |
class AssistedCandidateGenerator(CandidateGenerator):
"""
`CandidateGenerator` class to be used for assisted generation and speculative decoding. This class generates
candidates through the use of a smaller model. Read the following blog post for more information:
https://huggingface.co/blog/assisted-ge... | class_definition | 3,274 | 17,291 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/candidate_generator.py | null | 10,721 |
class AssistedCandidateGeneratorDifferentTokenizers(AssistedCandidateGenerator):
"""
`CandidateGenerator` class to be used for Universal Assisted Generation (UAD): assisted generation with different tokenizers
for the assistant and main models. This class generates candidates through the use of a smaller
... | class_definition | 17,294 | 31,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/candidate_generator.py | null | 10,722 |
class PromptLookupCandidateGenerator(CandidateGenerator):
"""
`CandidateGenerator` class to be used for prompt lookup generation. This class generates candidates by looking up
likely continuations in the provided prompt (input_ids) itself.
Read the following blog post for more information: https://githu... | class_definition | 31,172 | 36,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/candidate_generator.py | null | 10,723 |
class EarlyExitCandidateGenerator(AssistedCandidateGenerator):
"""
`CandidateGenerator` class to be used for assisted generation and speculative decoding. This class generates
candidates through the use of **the model itself**, exiting early. Can only be used with models that support early
exit, e.g., `... | class_definition | 36,600 | 39,611 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/candidate_generator.py | null | 10,724 |
class WatermarkDetectorOutput:
"""
Outputs of a watermark detector.
Args:
num_tokens_scored (np.array of shape (batch_size)):
Array containing the number of tokens scored for each element in the batch.
num_green_tokens (np.array of shape (batch_size)):
Array containi... | class_definition | 1,216 | 2,800 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,725 |
class WatermarkDetector:
r"""
Detector for detection of watermark generated text. The detector needs to be given the exact same settings that were
given during text generation to replicate the watermark greenlist generation and so detect the watermark. This includes
the correct device that was used duri... | class_definition | 2,803 | 11,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,726 |
class BayesianDetectorConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BayesianDetectorModel`]. It is used to
instantiate a Bayesian Detector model according to the specified arguments.
Configuration objects inherit from [`PretrainedConfig`] and can be use... | class_definition | 11,087 | 12,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,727 |
class BayesianWatermarkDetectorModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if the text is watermarked.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
posterior_probabilities ... | class_definition | 12,288 | 12,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,728 |
class BayesianDetectorWatermarkedLikelihood(nn.Module):
"""Watermarked likelihood model for binary-valued g-values.
This takes in g-values and returns p(g_values|watermarked).
"""
def __init__(self, watermarking_depth: int):
"""Initializes the model parameters."""
super().__init__()
... | class_definition | 12,816 | 15,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,729 |
class BayesianDetectorModel(PreTrainedModel):
r"""
Bayesian classifier for watermark detection.
This detector uses Bayes' rule to compute a watermarking score, which is the sigmoid of the log of ratio of the
posterior probabilities P(watermarked|g_values) and P(unwatermarked|g_values). Please see the s... | class_definition | 15,683 | 21,423 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,730 |
class SynthIDTextWatermarkDetector:
r"""
SynthID text watermark detector class.
This class has to be initialized with the trained bayesian detector module check script
in examples/synthid_text/detector_training.py for example in training/saving/loading this
detector module. The folder also showcase... | class_definition | 21,426 | 24,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/watermarking.py | null | 10,731 |
class BeamScorer(ABC):
"""
Abstract base class for all beam scorers that are used for [`~PreTrainedModel.beam_search`] and
[`~PreTrainedModel.beam_sample`].
"""
@abstractmethod
@add_start_docstrings(PROCESS_INPUTS_DOCSTRING)
def process(
self,
input_ids: torch.LongTensor,
... | class_definition | 4,463 | 5,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py | null | 10,732 |
class BeamSearchScorer(BeamScorer):
r"""
[`BeamScorer`] implementing standard beam search decoding.
Adapted in part from [Facebook's XLM beam search
code](https://github.com/facebookresearch/XLM/blob/9e6f6814d17be4fe5b15f2e6c43eb2b2d76daeb4/src/model/transformer.py#L529).
Reference for the diverse... | class_definition | 5,412 | 20,004 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py | null | 10,733 |
class ConstrainedBeamSearchScorer(BeamScorer):
r"""
[`BeamScorer`] implementing constrained beam search decoding.
Args:
batch_size (`int`):
Batch Size of `input_ids` for which standard beam search decoding is run in parallel.
num_beams (`int`):
Number of beams for b... | class_definition | 20,007 | 45,576 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py | null | 10,734 |
class BeamHypotheses:
def __init__(self, num_beams: int, length_penalty: float, early_stopping: bool, max_length: Optional[int] = None):
"""
Initialize n-best list of hypotheses.
"""
self.length_penalty = length_penalty
self.early_stopping = early_stopping
self.max_le... | class_definition | 45,579 | 49,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_search.py | null | 10,735 |
class BaseStreamer:
"""
Base class from which `.generate()` streamers should inherit.
"""
def put(self, value):
"""Function that is called by `.generate()` to push new tokens"""
raise NotImplementedError()
def end(self):
"""Function that is called by `.generate()` to signal... | class_definition | 790 | 1,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py | null | 10,736 |
class TextStreamer(BaseStreamer):
"""
Simple text streamer that prints the token(s) to stdout as soon as entire words are formed.
<Tip warning={true}>
The API for the streamer classes is still under development and may change in the future.
</Tip>
Parameters:
tokenizer (`AutoTokenize... | class_definition | 1,174 | 6,310 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py | null | 10,737 |
class TextIteratorStreamer(TextStreamer):
"""
Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is
useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive
Gradio demo).
<Tip warni... | class_definition | 6,313 | 9,263 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py | null | 10,738 |
class AsyncTextIteratorStreamer(TextStreamer):
"""
Streamer that stores print-ready text in a queue, to be used by a downstream application as an async iterator.
This is useful for applications that benefit from acessing the generated text asynchronously (e.g. in an
interactive Gradio demo).
<Tip w... | class_definition | 9,266 | 13,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/streamers.py | null | 10,739 |
class GenerateDecoderOnlyOutput(ModelOutput):
"""
Outputs of decoder-only generation models, when using non-beam methods.
Args:
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal to `ma... | class_definition | 3,694 | 6,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py | null | 10,740 |
class GenerateEncoderDecoderOutput(ModelOutput):
"""
Outputs of encoder-decoder generation models, when using non-beam methods.
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_len... | class_definition | 6,289 | 10,073 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py | null | 10,741 |
class GenerateBeamDecoderOnlyOutput(ModelOutput):
"""
Outputs of decoder-only generation models, when using beam methods.
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) i... | class_definition | 10,087 | 13,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py | null | 10,742 |
class GenerateBeamEncoderDecoderOutput(ModelOutput):
"""
Outputs of encoder-decoder generation models, when using beam methods.
Args:
sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_len... | class_definition | 13,411 | 17,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py | null | 10,743 |
class GenerationMixin:
"""
A class containing all functions for auto-regressive text generation, to be used as a mixin in [`PreTrainedModel`].
The class exposes [`~generation.GenerationMixin.generate`], which can be used for:
- *greedy decoding* if `num_beams=1` and `do_sample=False`
- *con... | class_definition | 19,398 | 237,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/utils.py | null | 10,744 |
class TFGreedySearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using greedy search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is either equal ... | class_definition | 1,894 | 3,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,745 |
class TFGreedySearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (r... | class_definition | 3,692 | 6,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,746 |
class TFSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using sampling.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is eit... | class_definition | 6,950 | 8,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,747 |
class TFSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respectively... | class_definition | 8,821 | 12,180 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,748 |
class TFBeamSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam search.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) ... | class_definition | 12,194 | 14,779 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,749 |
class TFBeamSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
attributes (respe... | class_definition | 14,793 | 18,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,750 |
class TFBeamSampleDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using beam sample.
Args:
sequences (`tf.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
The generated sequences. The second dimension (sequence_length) ... | class_definition | 18,896 | 21,461 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,751 |
class TFBeamSampleEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states attributes (res... | class_definition | 21,475 | 25,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,752 |
class TFContrastiveSearchDecoderOnlyOutput(ModelOutput):
"""
Base class for outputs of decoder-only generation models using contrastive search.
Args:
sequences (`tf.Tensor` of shape `(batch_size, sequence_length)`):
The generated sequences. The second dimension (sequence_length) is eith... | class_definition | 25,494 | 27,287 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,753 |
class TFContrastiveSearchEncoderDecoderOutput(ModelOutput):
"""
Base class for outputs of encoder-decoder generation models using contrastive search. Hidden states and attention
weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
encoder_hidden_states att... | class_definition | 27,301 | 30,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,754 |
class TFGenerationMixin:
"""
A class containing all of the functions supporting generation, to be used as a mixin in [`TFPreTrainedModel`].
The class exposes [`~generation.TFGenerationMixin.generate`], which can be used for:
- *greedy decoding* by calling [`~generation.TFGenerationMixin.greedy_sear... | class_definition | 31,170 | 173,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/tf_utils.py | null | 10,755 |
class FlaxLogitsProcessor:
"""Abstract base class for all logit processors that can be applied during generation."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray) -> jnp.ndarray:
"""Flax method for processing logits."""
... | class_definition | 1,743 | 2,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,756 |
class FlaxLogitsWarper:
"""Abstract base class for all logit warpers that can be applied during generation with multinomial sampling."""
@add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray) -> jnp.ndarray:
"""Flax method for warpin... | class_definition | 2,210 | 2,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,757 |
class FlaxLogitsProcessorList(list):
"""
This class can be used to create a list of [`FlaxLogitsProcessor`] or [`FlaxLogitsWarper`] to subsequently process
a `scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
[`FlaxLogitsProcessor`] or [`FlaxLogitsWa... | class_definition | 2,694 | 3,871 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,758 |
class FlaxTemperatureLogitsWarper(FlaxLogitsWarper):
r"""
[`FlaxLogitsWarper`] for temperature (exponential scaling output probability distribution).
Args:
temperature (`float`):
The value used to module the logits distribution.
"""
def __init__(self, temperature: float):
... | class_definition | 3,874 | 4,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,759 |
class FlaxTopPLogitsWarper(FlaxLogitsWarper):
"""
[`FlaxLogitsWarper`] that performs top-p, i.e. restricting to top tokens summing to prob_cut_off <= prob_cut_off.
Args:
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_... | class_definition | 4,572 | 6,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,760 |
class FlaxTopKLogitsWarper(FlaxLogitsWarper):
r"""
[`FlaxLogitsWarper`] that performs top-k, i.e. restricting to the k highest probability elements.
Args:
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
filter_value (`float`, *opti... | class_definition | 6,595 | 8,240 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,761 |
class FlaxForcedBOSTokenLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces the specified token as the first generated token.
Args:
bos_token_id (`int`):
The id of the token to force as the first generated token.
"""
def __init__(self, bos_token_id:... | class_definition | 8,243 | 8,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,762 |
class FlaxForcedEOSTokenLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces the specified token as the last generated token when `max_length` is reached.
Args:
max_length (`int`):
The maximum length of the sequence to be generated.
eos_token_id (`int... | class_definition | 8,941 | 9,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,763 |
class FlaxMinLengthLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] enforcing a min-length by setting EOS probability to 0.
Args:
min_length (`int`):
The minimum length below which the score of `eos_token_id` is set to `-float("Inf")`.
eos_token_id (`int`):
... | class_definition | 9,859 | 11,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,764 |
class FlaxSuppressTokensAtBeginLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] supressing a list of tokens as soon as the `generate` function starts generating using
`begin_index` tokens. This should ensure that the tokens defined by `begin_suppress_tokens` are not sampled at the
begi... | class_definition | 11,077 | 12,023 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,765 |
class FlaxSuppressTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] suppressing a list of tokens at each decoding step. The processor will set their log probs
to be `-inf` so they are not sampled.
Args:
suppress_tokens (`list`):
Tokens to not sample.
"""
... | class_definition | 12,026 | 12,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,766 |
class FlaxForceTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that takes a list of pairs of integers which indicates a mapping from generation indices to
token indices that will be forced before sampling. The processor will set their log probs to 0 and all other tokens
to `-inf... | class_definition | 12,643 | 14,979 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,767 |
class FlaxWhisperTimeStampLogitsProcessor(FlaxLogitsProcessor):
r"""
Whisper specific Processor. This processor can be used to force a list of tokens. The processor will set their log
probs to `inf` so that they are sampled at their corresponding index.
Args:
generate_config (`GenerateConfig`):... | class_definition | 14,982 | 19,232 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,768 |
class FlaxNoRepeatNGramLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that enforces no repetition of n-grams. See
[Fairseq](https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345).
Args:
ngram_size (`int`):
... | class_definition | 19,235 | 23,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/flax_logits_process.py | null | 10,769 |
class Constraint(ABC):
r"""Abstract base class for all constraints that can be applied during generation.
It must define how the constraint can be satisfied.
All classes that inherit Constraint must follow the requirement that
```py
completed = False
while not completed:
_, completed =... | class_definition | 72 | 4,675 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py | null | 10,770 |
class PhrasalConstraint(Constraint):
r"""
[`Constraint`] enforcing that an ordered sequence of tokens is included in the output.
Args:
token_ids (`List[int]`):
The id of the token that must be generated by the output.
"""
def __init__(self, token_ids: List[int]):
super(... | class_definition | 4,678 | 7,136 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py | null | 10,771 |
class DisjunctiveTrie:
def __init__(self, nested_token_ids: List[List[int]], no_subsets=True):
r"""
A helper class that builds a trie with the words represented in `nested_token_ids`.
"""
self.max_height = max([len(one) for one in nested_token_ids])
root = {}
for tok... | class_definition | 7,139 | 8,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py | null | 10,772 |
class DisjunctiveConstraint(Constraint):
r"""
A special [`Constraint`] that is fulfilled by fulfilling just one of several constraints.
Args:
nested_token_ids (`List[List[int]]`):
A list of words, where each word is a list of ids. This constraint is fulfilled by generating just one from... | class_definition | 8,948 | 12,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py | null | 10,773 |
class ConstraintListState:
r"""
A class for beam scorers to track its progress through a list of constraints.
Args:
constraints (`List[Constraint]`):
A list of [`Constraint`] objects that must be fulfilled by the beam scorer.
"""
def __init__(self, constraints: List[Constraint]... | class_definition | 12,006 | 19,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/generation/beam_constraints.py | null | 10,774 |
class SageMakerTrainer(Trainer):
def __init__(self, args=None, **kwargs):
warnings.warn(
"`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
"instead.",
FutureWarning,
)
super().__init__(args=args, **kwargs) | class_definition | 723 | 1,043 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/sagemaker/trainer_sm.py | null | 10,775 |
class SageMakerTrainingArguments(TrainingArguments):
mp_parameters: str = field(
default="",
metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"},
)
def __post_init__(self):
super().__post_init__()
warnings.warn(
... | class_definition | 2,085 | 5,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/sagemaker/training_args_sm.py | null | 10,776 |
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