






'''simple docstring'''





import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


__A				:				Any					=		logging.get_logger(__name__)

__A				:				Optional[int]					=		OrderedDict(
    [
        ("""align""", """AlignProcessor"""),
        ("""altclip""", """AltCLIPProcessor"""),
        ("""bark""", """BarkProcessor"""),
        ("""blip""", """BlipProcessor"""),
        ("""blip-2""", """Blip2Processor"""),
        ("""bridgetower""", """BridgeTowerProcessor"""),
        ("""chinese_clip""", """ChineseCLIPProcessor"""),
        ("""clap""", """ClapProcessor"""),
        ("""clip""", """CLIPProcessor"""),
        ("""clipseg""", """CLIPSegProcessor"""),
        ("""flava""", """FlavaProcessor"""),
        ("""git""", """GitProcessor"""),
        ("""groupvit""", """CLIPProcessor"""),
        ("""hubert""", """Wav2Vec2Processor"""),
        ("""instructblip""", """InstructBlipProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2Processor"""),
        ("""layoutlmv3""", """LayoutLMv3Processor"""),
        ("""markuplm""", """MarkupLMProcessor"""),
        ("""mctct""", """MCTCTProcessor"""),
        ("""mgp-str""", """MgpstrProcessor"""),
        ("""oneformer""", """OneFormerProcessor"""),
        ("""owlvit""", """OwlViTProcessor"""),
        ("""pix2struct""", """Pix2StructProcessor"""),
        ("""sam""", """SamProcessor"""),
        ("""sew""", """Wav2Vec2Processor"""),
        ("""sew-d""", """Wav2Vec2Processor"""),
        ("""speech_to_text""", """Speech2TextProcessor"""),
        ("""speech_to_text_2""", """Speech2Text2Processor"""),
        ("""speecht5""", """SpeechT5Processor"""),
        ("""trocr""", """TrOCRProcessor"""),
        ("""tvlt""", """TvltProcessor"""),
        ("""unispeech""", """Wav2Vec2Processor"""),
        ("""unispeech-sat""", """Wav2Vec2Processor"""),
        ("""vilt""", """ViltProcessor"""),
        ("""vision-text-dual-encoder""", """VisionTextDualEncoderProcessor"""),
        ("""wav2vec2""", """Wav2Vec2Processor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2Processor"""),
        ("""wavlm""", """Wav2Vec2Processor"""),
        ("""whisper""", """WhisperProcessor"""),
        ("""xclip""", """XCLIPProcessor"""),
    ]
)

__A				:				Optional[Any]					=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)






def  lowerCamelCase_  (     lowercase__):
 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   lowerCamelCase__	=  model_type_to_module_name(lowercase__)

   lowerCamelCase__	=  importlib.import_module(F'''.{module_name}'''					,  "transformers.models")
   try:
    return getattr(lowercase__					,  lowercase__)
   except AttributeError:
    continue

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(lowercase__					,  "__name__"					,  lowercase__) == class_name:
   return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
 lowerCamelCase__	=  importlib.import_module("transformers")
 if hasattr(lowercase__					,  lowercase__):
  return getattr(lowercase__					,  lowercase__)

 return None
class 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Optional[int]  )				->				List[Any]:

     '''simple docstring'''




     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(__lowerCamelCase  )
    def  a__					(       cls  :  Optional[int]					,  __lowerCamelCase  :  Dict					,  **__lowerCamelCase  :  List[Any]  )				->				int:

     '''simple docstring'''




     lowerCamelCase__	=  kwargs.pop("config"					,  __lowerCamelCase  )
     lowerCamelCase__	=  kwargs.pop("trust_remote_code"					,  __lowerCamelCase  )
     lowerCamelCase__	=  True

     lowerCamelCase__	=  None
     lowerCamelCase__	=  None

     # First, let's see if we have a preprocessor config.
     # Filter the kwargs for `get_file_from_repo`.
     lowerCamelCase__	=  {
         key: kwargs[key] for key in inspect.signature(__lowerCamelCase  ).parameters.keys() if key in kwargs
     }
     # Let's start by checking whether the processor class is saved in an image processor
     lowerCamelCase__	=  get_file_from_repo(
         __lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase  )
     if preprocessor_config_file is not None:
      lowerCamelCase__	,						lowerCamelCase__	=  ImageProcessingMixin.get_image_processor_dict(__lowerCamelCase					,  **__lowerCamelCase  )
      lowerCamelCase__	=  config_dict.get("processor_class"					,  __lowerCamelCase  )
      if "AutoProcessor" in config_dict.get("auto_map"					,  {}  ):
       lowerCamelCase__	=  config_dict["auto_map"]["AutoProcessor"]

        # If not found, let's check whether the processor class is saved in a feature extractor config
     if preprocessor_config_file is not None and processor_class is None:
      lowerCamelCase__	,						lowerCamelCase__	=  FeatureExtractionMixin.get_feature_extractor_dict(__lowerCamelCase					,  **__lowerCamelCase  )
      lowerCamelCase__	=  config_dict.get("processor_class"					,  __lowerCamelCase  )
      if "AutoProcessor" in config_dict.get("auto_map"					,  {}  ):
       lowerCamelCase__	=  config_dict["auto_map"]["AutoProcessor"]

     if processor_class is None:
      # Next, let's check whether the processor class is saved in a tokenizer
      lowerCamelCase__	=  get_file_from_repo(
          __lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase  )
      if tokenizer_config_file is not None:
       with open(__lowerCamelCase					,  encoding="utf-8"  ) as reader:
        lowerCamelCase__	=  json.load(__lowerCamelCase  )

       lowerCamelCase__	=  config_dict.get("processor_class"					,  __lowerCamelCase  )
       if "AutoProcessor" in config_dict.get("auto_map"					,  {}  ):
        lowerCamelCase__	=  config_dict["auto_map"]["AutoProcessor"]

     if processor_class is None:
      # Otherwise, load config, if it can be loaded.
      if not isinstance(__lowerCamelCase					,  __lowerCamelCase  ):
       lowerCamelCase__	=  AutoConfig.from_pretrained(
           __lowerCamelCase					,  trust_remote_code=__lowerCamelCase					,  **__lowerCamelCase  )

      # And check if the config contains the processor class.
      lowerCamelCase__	=  getattr(__lowerCamelCase					,  "processor_class"					,  __lowerCamelCase  )
      if hasattr(__lowerCamelCase					,  "auto_map"  ) and "AutoProcessor" in config.auto_map:
       lowerCamelCase__	=  config.auto_map["AutoProcessor"]

     if processor_class is not None:
      lowerCamelCase__	=  processor_class_from_name(__lowerCamelCase  )

     lowerCamelCase__	=  processor_auto_map is not None
     lowerCamelCase__	=  processor_class is not None or type(__lowerCamelCase  ) in PROCESSOR_MAPPING
     lowerCamelCase__	=  resolve_trust_remote_code(
         __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  )

     if has_remote_code and trust_remote_code:
      lowerCamelCase__	=  get_class_from_dynamic_module(
          __lowerCamelCase					,  __lowerCamelCase					,  **__lowerCamelCase  )
      lowerCamelCase__	=  kwargs.pop("code_revision"					,  __lowerCamelCase  )
      if os.path.isdir(__lowerCamelCase  ):
       processor_class.register_for_auto_class()
      return processor_class.from_pretrained(
          __lowerCamelCase					,  trust_remote_code=__lowerCamelCase					,  **__lowerCamelCase  )
     elif processor_class is not None:
      return processor_class.from_pretrained(
          __lowerCamelCase					,  trust_remote_code=__lowerCamelCase					,  **__lowerCamelCase  )
     # Last try: we use the PROCESSOR_MAPPING.
     elif type(__lowerCamelCase  ) in PROCESSOR_MAPPING:
      return PROCESSOR_MAPPING[type(__lowerCamelCase  )].from_pretrained(__lowerCamelCase					,  **__lowerCamelCase  )

     # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
     # tokenizer.
     try:
      return AutoTokenizer.from_pretrained(
          __lowerCamelCase					,  trust_remote_code=__lowerCamelCase					,  **__lowerCamelCase  )
     except Exception:
      try:
       return AutoImageProcessor.from_pretrained(
           __lowerCamelCase					,  trust_remote_code=__lowerCamelCase					,  **__lowerCamelCase  )
      except Exception:
       pass

      try:
       return AutoFeatureExtractor.from_pretrained(
           __lowerCamelCase					,  trust_remote_code=__lowerCamelCase					,  **__lowerCamelCase  )
      except Exception:
       pass

     raise ValueError(
         f'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
         "tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains"
         "the files of at least one of those processing classes."  )




    @staticmethod
    def  a__					(       __lowerCamelCase  :  Any					,  __lowerCamelCase  :  str  )				->				List[Any]:

     '''simple docstring'''




     PROCESSOR_MAPPING.register(__lowerCamelCase					,  __lowerCamelCase  )









'''simple docstring'''





from __future__ import annotations

from queue import Queue
class 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     lowerCamelCase__	=  {}

    def  a__					(       self  :  Optional[int]  )				->				None:

     '''simple docstring'''




     for i in self.vertices:
      print(__lowerCamelCase					,  " : "					,  " -> ".join([str(__lowerCamelCase  ) for j in self.vertices[i]]  )  )

    def  a__					(       self  :  Optional[Any]					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  int  )				->				None:

     '''simple docstring'''




     if from_vertex in self.vertices:
      self.vertices[from_vertex].append(__lowerCamelCase  )
     else:
      lowerCamelCase__	=  [to_vertex]




    def  a__					(       self  :  Optional[int]					,  __lowerCamelCase  :  int  )				->				set[int]:

     '''simple docstring'''




     lowerCamelCase__	=  set()

     # create a first in first out queue to store all the vertices for BFS
     lowerCamelCase__	=  Queue()

     # mark the source node as visited and enqueue it
     visited.add(__lowerCamelCase  )
     queue.put(__lowerCamelCase  )

     while not queue.empty():
      lowerCamelCase__	=  queue.get()

      # loop through all adjacent vertex and enqueue it if not yet visited
      for adjacent_vertex in self.vertices[vertex]:
       if adjacent_vertex not in visited:
        queue.put(__lowerCamelCase  )
        visited.add(__lowerCamelCase  )
     return visited


if __name__ == "__main__":
 from doctest import testmod

 testmod(verbose=True)

 __A				:				Optional[Any]					=		Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 # 0  :  1 -> 2
 # 1  :  2
 # 2  :  0 -> 3
 # 3  :  3

 assert sorted(g.bfs(2)) == [0, 1, 2, 3]

