






from packaging import version

from .. import __version__
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,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


a		:       List[str]			     = "pytorch_model.bin"
a		:       List[str]			     = "pytorch_model.bin.index.json"
a		:       int			     = "adapter_config.json"
a		:       List[str]			     = "adapter_model.bin"
a		:       List[str]			     = "adapter_model.safetensors"
a		:       List[str]			     = "tf_model.h5"
a		:       Any			     = "tf_model.h5.index.json"
a		:       int			     = "model.ckpt"
a		:       Dict			     = "flax_model.msgpack"
a		:       Any			     = "flax_model.msgpack.index.json"
a		:       Optional[Any]			     = "model.safetensors"
a		:       List[str]			     = "model.safetensors.index.json"
a		:       List[Any]			     = "config.json"
a		:       List[str]			     = "preprocessor_config.json"
a		:       List[str]			     = FEATURE_EXTRACTOR_NAME
a		:       Union[str, Any]			     = "generation_config.json"
a		:       str			     = "modelcard.json"

a		:       Tuple			     = "▁"
a		:       Tuple			     = SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

a		:       int			     = [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
a		:       Union[str, Any]			     = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
a		:       Tuple			     = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[Any]		):







  if version.parse(snake_case_		) < version.parse(snake_case_		):
    if "dev" in min_version:
      __UpperCAmelCase							:	Union[str, Any]           =  (
          '''This example requires a source install from HuggingFace Transformers (see '''
          '''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
      )
    else:
      __UpperCAmelCase							:	int           =  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."""		)









"""simple docstring"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




