image-match/python/py/Lib/site-packages/transformers/pipelines/zero_shot_classification.py

import inspect

import numpy as np

from ..tokenization_python import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import ArgumentHandler, ChunkPipeline, build_pipeline_init_args


logger = logging.get_logger(__name__)


class ZeroShotClassificationArgumentHandler(ArgumentHandler):
    """
    Handles arguments for zero-shot for text classification by turning each possible label into an NLI
    premise/hypothesis pair.
    """

    def _parse_labels(self, labels):
        if isinstance(labels, str):
            labels = [label.strip() for label in labels.split(",") if label.strip()]
        return labels

    def __call__(self, sequences, labels, hypothesis_template):
        if len(labels) == 0 or len(sequences) == 0:
            raise ValueError("You must include at least one label and at least one sequence.")
        if hypothesis_template.format(labels[0]) == hypothesis_template:
            raise ValueError(
                f'The provided hypothesis_template "{hypothesis_template}" was not able to be formatted with the target labels. '
                "Make sure the passed template includes formatting syntax such as {} where the label should go."
            )

        if isinstance(sequences, str):
            sequences = [sequences]

        sequence_pairs = []
        for sequence in sequences:
            sequence_pairs.extend([[sequence, hypothesis_template.format(label)] for label in labels])

        return sequence_pairs, sequences


@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True))
class ZeroShotClassificationPipeline(ChunkPipeline):
    """
    NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` trained on NLI (natural
    language inference) tasks. Equivalent of `text-classification` pipelines, but these models don't require a
    hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is
    **much** more flexible.

    Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis
    pair and passed to the pretrained model. Then, the logit for *entailment* is taken as the logit for the candidate
    label being valid. Any NLI model can be used, but the id of the *entailment* label must be included in the model
    config's :attr:*~transformers.PreTrainedConfig.label2id*.

    Example:

    ```python
    >>> from transformers import pipeline

    >>> oracle = pipeline(model="facebook/bart-large-mnli")
    >>> oracle(
    ...     "I have a problem with my iphone that needs to be resolved asap!!",
    ...     candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
    ... )
    {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]}

    >>> oracle(
    ...     "I have a problem with my iphone that needs to be resolved asap!!",
    ...     candidate_labels=["english", "german"],
    ... )
    {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['english', 'german'], 'scores': [0.814, 0.186]}
    ```

    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)

    This NLI pipeline can currently be loaded from [`pipeline`] using the following task identifier:
    `"zero-shot-classification"`.

    The models that this pipeline can use are models that have been fine-tuned on an NLI task. See the up-to-date list
    of available models on [huggingface.co/models](https://huggingface.co/models?search=nli).
    """

    _load_processor = False
    _load_image_processor = False
    _load_feature_extractor = False
    _load_tokenizer = True

    def __init__(self, args_parser=ZeroShotClassificationArgumentHandler(), **kwargs):
        self._args_parser = args_parser
        super().__init__(**kwargs)
        if self.entailment_id == -1:
            logger.warning(
                "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
                "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."
            )

    @property
    def entailment_id(self):
        for label, ind in self.model.config.label2id.items():
            if label.lower().startswith("entail"):
                return ind
        return -1

    def _parse_and_tokenize(
        self, sequence_pairs, padding=True, add_special_tokens=True, truncation=TruncationStrategy.ONLY_FIRST, **kwargs
    ):
        """
        Parse arguments and tokenize only_first so that hypothesis (label) is not truncated
        """
        return_tensors = "pt"
        if self.tokenizer.pad_token is None:
            # Override for tokenizers not supporting padding
            logger.error(
                "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
                " `pad_token=eos_token`"
            )
            self.tokenizer.pad_token = self.tokenizer.eos_token
        try:
            inputs = self.tokenizer(
                sequence_pairs,
                add_special_tokens=add_special_tokens,
                return_tensors=return_tensors,
                padding=padding,
                truncation=truncation,
            )
        except Exception as e:
            if "too short" in str(e):
                # tokenizers might yell that we want to truncate
                # to a value that is not even reached by the input.
                # In that case we don't want to truncate.
                # It seems there's not a really better way to catch that
                # exception.

                inputs = self.tokenizer(
                    sequence_pairs,
                    add_special_tokens=add_special_tokens,
                    return_tensors=return_tensors,
                    padding=padding,
                    truncation=TruncationStrategy.DO_NOT_TRUNCATE,
                )
            else:
                raise e

        return inputs

    def _sanitize_parameters(self, **kwargs):
        preprocess_params = {}
        if "candidate_labels" in kwargs:
            preprocess_params["candidate_labels"] = self._args_parser._parse_labels(kwargs["candidate_labels"])
        if "hypothesis_template" in kwargs:
            preprocess_params["hypothesis_template"] = kwargs["hypothesis_template"]

        postprocess_params = {}
        if "multi_label" in kwargs:
            postprocess_params["multi_label"] = kwargs["multi_label"]
        return preprocess_params, {}, postprocess_params

    def __call__(
        self,
        sequences: str | list[str],
        *args,
        **kwargs,
    ):
        """
        Classify the sequence(s) given as inputs. See the [`ZeroShotClassificationPipeline`] documentation for more
        information.

        Args:
            sequences (`str` or `list[str]`):
                The sequence(s) to classify, will be truncated if the model input is too large.
            candidate_labels (`str` or `list[str]`):
                The set of possible class labels to classify each sequence into. Can be a single label, a string of
                comma-separated labels, or a list of labels.
            hypothesis_template (`str`, *optional*, defaults to `"This example is {}."`):
                The template used to turn each label into an NLI-style hypothesis. This template must include a {} or
                similar syntax for the candidate label to be inserted into the template. For example, the default
                template is `"This example is {}."` With the candidate label `"sports"`, this would be fed into the
                model like `"<cls> sequence to classify <sep> This example is sports . <sep>"`. The default template
                works well in many cases, but it may be worthwhile to experiment with different templates depending on
                the task setting.
            multi_label (`bool`, *optional*, defaults to `False`):
                Whether or not multiple candidate labels can be true. If `False`, the scores are normalized such that
                the sum of the label likelihoods for each sequence is 1. If `True`, the labels are considered
                independent and probabilities are normalized for each candidate by doing a softmax of the entailment
                score vs. the contradiction score.

        Return:
            A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:

            - **sequence** (`str`) -- The sequence for which this is the output.
            - **labels** (`list[str]`) -- The labels sorted by order of likelihood.
            - **scores** (`list[float]`) -- The probabilities for each of the labels.
        """
        if len(args) == 0:
            pass
        elif len(args) == 1 and "candidate_labels" not in kwargs:
            kwargs["candidate_labels"] = args[0]
        else:
            raise ValueError(f"Unable to understand extra arguments {args}")

        return super().__call__(sequences, **kwargs)

    def preprocess(self, inputs, candidate_labels=None, hypothesis_template="This example is {}."):
        sequence_pairs, sequences = self._args_parser(inputs, candidate_labels, hypothesis_template)

        for i, (candidate_label, sequence_pair) in enumerate(zip(candidate_labels, sequence_pairs)):
            model_input = self._parse_and_tokenize([sequence_pair])

            yield {
                "candidate_label": candidate_label,
                "sequence": sequences[0],
                "is_last": i == len(candidate_labels) - 1,
                **model_input,
            }

    def _forward(self, inputs):
        candidate_label = inputs["candidate_label"]
        sequence = inputs["sequence"]
        model_inputs = {k: inputs[k] for k in self.tokenizer.model_input_names}
        # `XXXForSequenceClassification` models should not use `use_cache=True` even if it's supported
        model_forward = self.model.forward
        if "use_cache" in inspect.signature(model_forward).parameters:
            model_inputs["use_cache"] = False
        outputs = self.model(**model_inputs)

        model_outputs = {
            "candidate_label": candidate_label,
            "sequence": sequence,
            "is_last": inputs["is_last"],
            **outputs,
        }
        return model_outputs

    def postprocess(self, model_outputs, multi_label=False):
        candidate_labels = [outputs["candidate_label"] for outputs in model_outputs]
        sequences = [outputs["sequence"] for outputs in model_outputs]
        logits = np.concatenate([output["logits"].float().numpy() for output in model_outputs])
        N = logits.shape[0]
        n = len(candidate_labels)
        num_sequences = N // n
        reshaped_outputs = logits.reshape((num_sequences, n, -1))

        if multi_label or len(candidate_labels) == 1:
            # softmax over the entailment vs. contradiction dim for each label independently
            entailment_id = self.entailment_id
            contradiction_id = -1 if entailment_id == 0 else 0
            entail_contr_logits = reshaped_outputs[..., [contradiction_id, entailment_id]]
            scores = np.exp(entail_contr_logits) / np.exp(entail_contr_logits).sum(-1, keepdims=True)
            scores = scores[..., 1]
        else:
            # softmax the "entailment" logits over all candidate labels
            entail_logits = reshaped_outputs[..., self.entailment_id]
            scores = np.exp(entail_logits) / np.exp(entail_logits).sum(-1, keepdims=True)

        top_inds = list(reversed(scores[0].argsort()))
        return {
            "sequence": sequences[0],
            "labels": [candidate_labels[i] for i in top_inds],
            "scores": scores[0, top_inds].tolist(),
        }