AutoAndroidController/py/venv/Lib/site-packages/transformers/utils/__init__.py

#!/usr/bin/env python

# Copyright 2021 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 functools import lru_cache

from huggingface_hub import get_full_repo_name  # for backward compatibility
from huggingface_hub.constants import HF_HUB_DISABLE_TELEMETRY as DISABLE_TELEMETRY  # for backward compatibility
from packaging import version

from .. import __version__
from .auto_docstring import (
    ClassAttrs,
    ClassDocstring,
    ImageProcessorArgs,
    ModelArgs,
    ModelOutputArgs,
    auto_class_docstring,
    auto_docstring,
    get_args_doc_from_source,
    parse_docstring,
    set_min_indent,
)
from .backbone_utils import BackboneConfigMixin, BackboneMixin
from .chat_template_utils import DocstringParsingException, TypeHintParsingException, get_json_schema
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    TransformersKwargs,
    can_return_loss,
    can_return_tuple,
    expand_dims,
    filter_out_non_signature_kwargs,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_timm_config_dict,
    is_timm_local_checkpoint,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    torch_float,
    torch_int,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CHAT_TEMPLATE_DIR,
    CHAT_TEMPLATE_FILE,
    CLOUDFRONT_DISTRIB_PREFIX,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    LEGACY_PROCESSOR_CHAT_TEMPLATE_FILE,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushInProgress,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    list_repo_templates,
    try_to_load_from_cache,
)
from .import_utils import (
    ACCELERATE_MIN_VERSION,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    GGUF_MIN_VERSION,
    TORCH_FX_REQUIRED_VERSION,
    TRITON_MIN_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    XLA_FSDPV2_MIN_VERSION,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    check_torch_load_is_safe,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_apollo_torch_available,
    is_aqlm_available,
    is_auto_awq_available,
    is_auto_gptq_available,
    is_auto_round_available,
    is_av_available,
    is_bitsandbytes_available,
    is_bitsandbytes_multi_backend_available,
    is_bs4_available,
    is_ccl_available,
    is_coloredlogs_available,
    is_compressed_tensors_available,
    is_cuda_platform,
    is_cv2_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectron2_available,
    is_eetq_available,
    is_essentia_available,
    is_faiss_available,
    is_fbgemm_gpu_available,
    is_flash_attn_2_available,
    is_flash_attn_3_available,
    is_flash_attn_greater_or_equal,
    is_flash_attn_greater_or_equal_2_10,
    is_flax_available,
    is_flute_available,
    is_fp_quant_available,
    is_fsdp_available,
    is_ftfy_available,
    is_g2p_en_available,
    is_galore_torch_available,
    is_gguf_available,
    is_gptqmodel_available,
    is_grokadamw_available,
    is_habana_gaudi1,
    is_hadamard_available,
    is_hqq_available,
    is_huggingface_hub_greater_or_equal,
    is_in_notebook,
    is_ipex_available,
    is_jinja_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_kernels_available,
    is_levenshtein_available,
    is_libcst_available,
    is_librosa_available,
    is_liger_kernel_available,
    is_lomo_available,
    is_matplotlib_available,
    is_mistral_common_available,
    is_mlx_available,
    is_natten_available,
    is_ninja_available,
    is_nltk_available,
    is_num2words_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_optimum_quanto_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_pretty_midi_available,
    is_protobuf_available,
    is_psutil_available,
    is_py3nvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_quanto_greater,
    is_quark_available,
    is_qutlass_available,
    is_rich_available,
    is_rjieba_available,
    is_rocm_platform,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_schedulefree_available,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_available,
    is_spacy_available,
    is_speech_available,
    is_spqr_available,
    is_sudachi_available,
    is_sudachi_projection_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tf2onnx_available,
    is_tf_available,
    is_tiktoken_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_accelerator_available,
    is_torch_available,
    is_torch_bf16_available,
    is_torch_bf16_available_on_device,
    is_torch_bf16_cpu_available,
    is_torch_bf16_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_deterministic,
    is_torch_flex_attn_available,
    is_torch_fp16_available_on_device,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_greater_or_equal,
    is_torch_hpu_available,
    is_torch_mlu_available,
    is_torch_mps_available,
    is_torch_musa_available,
    is_torch_neuroncore_available,
    is_torch_npu_available,
    is_torch_optimi_available,
    is_torch_sdpa_available,
    is_torch_tensorrt_fx_available,
    is_torch_tf32_available,
    is_torch_xla_available,
    is_torch_xpu_available,
    is_torchao_available,
    is_torchaudio_available,
    is_torchcodec_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchdynamo_compiling,
    is_torchdynamo_exporting,
    is_torchvision_available,
    is_torchvision_v2_available,
    is_training_run_on_sagemaker,
    is_triton_available,
    is_uroman_available,
    is_vision_available,
    is_vptq_available,
    is_xlstm_available,
    is_yt_dlp_available,
    requires_backends,
    torch_only_method,
)
from .peft_utils import (
    ADAPTER_CONFIG_NAME,
    ADAPTER_SAFE_WEIGHTS_NAME,
    ADAPTER_WEIGHTS_NAME,
    check_peft_version,
    find_adapter_config_file,
)


WEIGHTS_NAME = "pytorch_model.bin"
WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json"
TF2_WEIGHTS_NAME = "tf_model.h5"
TF2_WEIGHTS_INDEX_NAME = "tf_model.h5.index.json"
TF_WEIGHTS_NAME = "model.ckpt"
FLAX_WEIGHTS_NAME = "flax_model.msgpack"
FLAX_WEIGHTS_INDEX_NAME = "flax_model.msgpack.index.json"
SAFE_WEIGHTS_NAME = "model.safetensors"
SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json"
CONFIG_NAME = "config.json"
FEATURE_EXTRACTOR_NAME = "preprocessor_config.json"
IMAGE_PROCESSOR_NAME = "preprocessor_config.json"
VIDEO_PROCESSOR_NAME = "video_preprocessor_config.json"
AUDIO_TOKENIZER_NAME = "audio_tokenizer_config.json"
PROCESSOR_NAME = "processor_config.json"
GENERATION_CONFIG_NAME = "generation_config.json"
MODEL_CARD_NAME = "modelcard.json"


SENTENCEPIECE_UNDERLINE = "▁"
SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

MULTIPLE_CHOICE_DUMMY_INPUTS = [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def check_min_version(min_version):
    if version.parse(__version__) < version.parse(min_version):
        if "dev" in min_version:
            error_message = (
                "This example requires a source install from HuggingFace Transformers (see "
                "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
            )
        else:
            error_message = f"This example requires a minimum version of {min_version},"
        error_message += f" but the version found is {__version__}.\n"
        raise ImportError(
            error_message
            + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
            "versions of HuggingFace Transformers."
        )


@lru_cache
def get_available_devices() -> frozenset[str]:
    """
    Returns a frozenset of devices available for the current PyTorch installation.
    """
    devices = {"cpu"}  # `cpu` is always supported as a device in PyTorch

    if is_torch_cuda_available():
        devices.add("cuda")

    if is_torch_mps_available():
        devices.add("mps")

    if is_torch_xpu_available():
        devices.add("xpu")

    if is_torch_npu_available():
        devices.add("npu")

    if is_torch_hpu_available():
        devices.add("hpu")

    if is_torch_mlu_available():
        devices.add("mlu")

    if is_torch_musa_available():
        devices.add("musa")

    return frozenset(devices)