AutoAndroidController/py/venv/Lib/site-packages/transformers/models/parakeet/processing_parakeet.py

# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. 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.
from typing import Optional, Union

from ...audio_utils import AudioInput, make_list_of_audio
from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from ...tokenization_utils_base import PreTokenizedInput, TextInput
from ...utils import logging


logger = logging.get_logger(__name__)


class ParakeetProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "audio_kwargs": {
            "sampling_rate": 16000,
            "padding": "longest",
        },
        "text_kwargs": {
            "padding": True,
            "padding_side": "right",
            "add_special_tokens": False,
        },
        "common_kwargs": {"return_tensors": "pt"},
    }


class ParakeetProcessor(ProcessorMixin):
    attributes = ["feature_extractor", "tokenizer"]
    feature_extractor_class = "ParakeetFeatureExtractor"
    tokenizer_class = "ParakeetTokenizerFast"

    def __call__(
        self,
        audio: AudioInput,
        text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput], None] = None,
        sampling_rate: Optional[int] = None,
        **kwargs: Unpack[ParakeetProcessorKwargs],
    ):
        audio = make_list_of_audio(audio)

        output_kwargs = self._merge_kwargs(
            ParakeetProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        if sampling_rate is None:
            logger.warning_once(
                f"You've provided audio without specifying the sampling rate. It will be assumed to be {output_kwargs['audio_kwargs']['sampling_rate']}, which can result in silent errors."
            )
        elif sampling_rate != output_kwargs["audio_kwargs"]["sampling_rate"]:
            raise ValueError(
                f"The sampling rate of the audio ({sampling_rate}) does not match the sampling rate of the processor ({output_kwargs['audio_kwargs']['sampling_rate']}). Please provide resampled the audio to the expected sampling rate."
            )

        if audio is not None:
            inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
        if text is not None:
            encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])

        if text is None:
            return inputs
        else:
            inputs["labels"] = encodings["input_ids"]
            return inputs

    @property
    def model_input_names(self):
        feature_extractor_input_names = self.feature_extractor.model_input_names
        return feature_extractor_input_names + ["labels"]


__all__ = ["ParakeetProcessor"]