class_docstr: Handles the `task name -> model class` and `architecture -> configuration` mappings.
docstr: Infers the library from the model repo.
  
  Args:
      model_name_or_path (`str`):
          The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, an
          instance of a model, or a model class.
      subfolder (`str`, defaults to `""`):
          In case the model files are located inside a subfolder of the model directory / repo on the Hugging
          Face Hub, you can specify the subfolder name here.
      revision (`Optional[str]`, *optional*, defaults to `None`):
          Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id.
      cache_dir (`Optional[str]`, *optional*):
          Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used.
      library_name (`Optional[str]`, *optional*):
           The library name of the model.
  Returns:
      `str`: The library name automatically detected from the model repo.
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code: def infer_library_from_model(
      cls,
      model_name_or_path: Union[str, Path],
      subfolder: str = "",
      revision: Optional[str] = None,
      cache_dir: str = huggingface_hub.constants.HUGGINGFACE_HUB_CACHE,
      library_name: str = None,
  ):
      """
      Infers the library from the model repo.
  
      Args:
          model_name_or_path (`str`):
              The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, an
              instance of a model, or a model class.
          subfolder (`str`, defaults to `""`):
              In case the model files are located inside a subfolder of the model directory / repo on the Hugging
              Face Hub, you can specify the subfolder name here.
          revision (`Optional[str]`, *optional*, defaults to `None`):
              Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id.
          cache_dir (`Optional[str]`, *optional*):
              Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used.
          library_name (`Optional[str]`, *optional*):
               The library name of the model.
      Returns:
          `str`: The library name automatically detected from the model repo.
      """
      if library_name is not None:
          return library_name
  
      full_model_path = Path(model_name_or_path) / subfolder
  
      if not full_model_path.is_dir():
          model_info = huggingface_hub.model_info(
              model_name_or_path, revision=revision
          )
          library_name = getattr(model_info, "library_name", None)
  
          # sentence-transformers package name is sentence_transformers
          if library_name is not None:
              library_name = library_name.replace("-", "_")
  
      if library_name is None:
          all_files, _ = TasksManager.get_model_files(
              model_name_or_path, subfolder, cache_dir
          )
  
          if "model_index.json" in all_files:
              library_name = "diffusers"
          elif CONFIG_NAME in all_files:
              model_config = PretrainedConfig.from_pretrained(
                  model_name_or_path,
                  subfolder=subfolder,
                  revision=revision,
              )
  
              if hasattr(model_config, "pretrained_cfg") or hasattr(
                  model_config, "architecture"
              ):
                  library_name = "timm"
              elif hasattr(model_config, "_diffusers_version"):
                  library_name = "diffusers"
              elif any(
                  file_path.startswith("sentence_")
                  for file_path in all_files
              ):
                  library_name = "sentence_transformers"
              else:
                  library_name = "transformers"
  
      if library_name is None:
          raise ValueError(
              "The library name could not be automatically inferred. If using the command-line, please provide the argument --library {transformers,diffusers,timm,sentence_transformers}. Example: `--library diffusers`."
          )
  
      return library_name
  
imports: import importlib
  import inspect
  import itertools
  import json
  import os
  from functools import partial
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
  import huggingface_hub
  from packaging import version
  from requests.exceptions import ConnectionError
  from transformers import AutoConfig, PretrainedConfig, is_tf_available, is_torch_available
  from transformers.utils import SAFE_WEIGHTS_NAME, TF2_WEIGHTS_NAME, WEIGHTS_NAME, logging
  from ..utils import CONFIG_NAME
  from ..utils.import_utils import is_onnx_available
label_desc: Use image node_id 13 for calling the TasksManager obj's underlying member method code with example usage: obj.infer_library_from_model(cls, model_name_or_path, subfolder, revision, cache_dir, library_name) and returns: library_name, library_name