text: # coding=utf-8
  # Copyright 2021 The HuggingFace Inc. team.
  #
  # 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.
  """ AutoProcessor class."""
  import importlib
  from collections import OrderedDict
  
  # Build the list of all feature extractors
  from ...configuration_utils import PretrainedConfig
  from ...feature_extraction_utils import FeatureExtractionMixin
  from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_list_of_files
  from .auto_factory import _LazyAutoMapping
  from .configuration_auto import (
      CONFIG_MAPPING_NAMES,
      AutoConfig,
      config_class_to_model_type,
      model_type_to_module_name,
      replace_list_option_in_docstrings,
  )
  
  
  PROCESSOR_MAPPING_NAMES = OrderedDict(
      [
          ("clip", "CLIPProcessor"),
          ("layoutlmv2", "LayoutLMv2Processor"),
          ("layoutxlm", "LayoutXLMProcessor"),
          ("speech_to_text", "Speech2TextProcessor"),
          ("speech_to_text_2", "Speech2Text2Processor"),
          ("trocr", "TrOCRProcessor"),
          ("wav2vec2", "Wav2Vec2Processor"),
          ("wav2vec2_with_lm", "Wav2Vec2ProcessorWithLM"),
          ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
      ]
  )
  
  PROCESSOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
  
  
  def processor_class_from_name(class_name: str):
      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
              module_name = model_type_to_module_name(module_name)
  
              module = importlib.import_module(f".{module_name}", "transformers.models")
              return getattr(module, class_name)
              break
  
      return None
  
  
  class AutoProcessor:
      r"""
      This is a generic processor class that will be instantiated as one of the processor classes of the library when
      created with the [`AutoProcessor.from_pretrained`] class method.
  
      This class cannot be instantiated directly using `__init__()` (throws an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoProcessor is designed to be instantiated "
              "using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method."
          )
  
      @classmethod
      @replace_list_option_in_docstrings(PROCESSOR_MAPPING_NAMES)
      def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
          r"""
          Instantiate one of the processor classes of the library from a pretrained model vocabulary.
  
          The processor class to instantiate is selected based on the `model_type` property of the config object (either
          passed as an argument or loaded from `pretrained_model_name_or_path` if possible):
  
          List options
  
          Params:
              pretrained_model_name_or_path (`str` or `os.PathLike`):
                  This can be either:
  
                  - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on
                    huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
                    namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
                  - a path to a *directory* containing a processor files saved using the `save_pretrained()` method,
                    e.g., `./my_model_directory/`.
              cache_dir (`str` or `os.PathLike`, *optional*):
                  Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
                  standard cache should not be used.
              force_download (`bool`, *optional*, defaults to `False`):
                  Whether or not to force to (re-)download the feature extractor files and override the cached versions
                  if they exist.
              resume_download (`bool`, *optional*, defaults to `False`):
                  Whether or not to delete incompletely received file. Attempts to resume the download if such a file
                  exists.
              proxies (`Dict[str, str]`, *optional*):
                  A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                  'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
              use_auth_token (`str` or *bool*, *optional*):
                  The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                  when running `transformers-cli login` (stored in `~/.huggingface`).
              revision (`str`, *optional*, defaults to `"main"`):
                  The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                  git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                  identifier allowed by git.
              return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                  If `False`, then this function returns just the final feature extractor object. If `True`, then this
                  functions returns a `Tuple(feature_extractor, unused_kwargs)` where *unused_kwargs* is a dictionary
                  consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
                  `kwargs` which has not been used to update `feature_extractor` and is otherwise ignored.
              kwargs (`Dict[str, Any]`, *optional*):
                  The values in kwargs of any keys which are feature extractor attributes will be used to override the
                  loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
                  controlled by the `return_unused_kwargs` keyword parameter.
  
          <Tip>
  
          Passing `use_auth_token=True` is required when you want to use a private model.
  
          </Tip>
  
          Examples:
  
          ```python
          >>> from transformers import AutoProcessor
  
          >>> # Download processor from huggingface.co and cache.
          >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
  
          >>> # If processor files are in a directory (e.g. processor was saved using *save_pretrained('./test/saved_model/')*)
          >>> processor = AutoProcessor.from_pretrained("./test/saved_model/")
          ```"""
          config = kwargs.pop("config", None)
          kwargs["_from_auto"] = True
  
          # First, let's see if we have a preprocessor config.
          # get_list_of_files only takes three of the kwargs we have, so we filter them.
          get_list_of_files_kwargs = {
              key: kwargs[key] for key in ["revision", "use_auth_token", "local_files_only"] if key in kwargs
          }
          model_files = get_list_of_files(pretrained_model_name_or_path, **get_list_of_files_kwargs)
          # strip to file name
          model_files = [f.split("/")[-1] for f in model_files]
  
          if FEATURE_EXTRACTOR_NAME in model_files:
              config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
              if "processor_class" in config_dict:
                  processor_class = processor_class_from_name(config_dict["processor_class"])
                  return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
  
          # Otherwise, load config, if it can be loaded.
          if not isinstance(config, PretrainedConfig):
              config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
  
          model_type = config_class_to_model_type(type(config).__name__)
  
          if getattr(config, "processor_class", None) is not None:
              processor_class = config.processor_class
              return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
  
          model_type = config_class_to_model_type(type(config).__name__)
          if model_type is not None:
              return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
  
          raise ValueError(
              f"Unrecognized processor in {pretrained_model_name_or_path}. Should have a `processor_type` key in "
              f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
              f"{', '.join(c for c in PROCESSOR_MAPPING_NAMES.keys())}"
          )
  