



'''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			:   Union[str, Any]			=      logging.get_logger(__name__)

A			:   str			=      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			:   int			=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def 		_a   (  lowerCamelCase_							):
   for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
      if class_name in processors:
         snake_case   :							int						  =model_type_to_module_name(lowerCamelCase_							)

         snake_case   :							List[str]						  =importlib.import_module(F'''.{module_name}'''					,						'''transformers.models'''							)
         try:
            return getattr(lowerCamelCase_					,						lowerCamelCase_							)
         except AttributeError:
            continue

   for processor in PROCESSOR_MAPPING._extra_content.values():
      if getattr(lowerCamelCase_					,						'''__name__'''					,						lowerCamelCase_							) == 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.
   snake_case   :							Optional[int]						  =importlib.import_module('''transformers'''							)
   if hasattr(lowerCamelCase_					,						lowerCamelCase_							):
      return getattr(lowerCamelCase_					,						lowerCamelCase_							)

   return None





class 							lowerCAmelCase_		:





    def __init__(    self    :    Optional[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(_snake_case     )
    def 					__snake_case       (    cls    :    Union[str, Any],    _snake_case    :    List[str],    **_snake_case    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =kwargs.pop('''config''',    _snake_case     )
       snake_case   :							Optional[Any]						  =kwargs.pop('''trust_remote_code''',    _snake_case     )
       snake_case   :							List[str]						  =True

       snake_case   :							Any						  =None
       snake_case   :							Tuple						  =None

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

       if processor_class is None:
          # Next, let's check whether the processor class is saved in a tokenizer
          snake_case   :							Dict						  =get_file_from_repo(
              _snake_case,    _snake_case,    **_snake_case     )
          if tokenizer_config_file is not None:
             with open(_snake_case,    encoding='''utf-8'''     ) as reader:
                snake_case   :							Optional[Any]						  =json.load(_snake_case     )

             snake_case   :							int						  =config_dict.get('''processor_class''',    _snake_case     )
             if "AutoProcessor" in config_dict.get('''auto_map''',    {}     ):
                snake_case   :							List[str]						  =config_dict['''auto_map''']['''AutoProcessor''']

       if processor_class is None:
          # Otherwise, load config, if it can be loaded.
          if not isinstance(_snake_case,    _snake_case     ):
             snake_case   :							List[str]						  =AutoConfig.from_pretrained(
                 _snake_case,    trust_remote_code=_snake_case,    **_snake_case     )

          # And check if the config contains the processor class.
          snake_case   :							List[str]						  =getattr(_snake_case,    '''processor_class''',    _snake_case     )
          if hasattr(_snake_case,    '''auto_map'''     ) and "AutoProcessor" in config.auto_map:
             snake_case   :							str						  =config.auto_map['''AutoProcessor''']

       if processor_class is not None:
          snake_case   :							List[Any]						  =processor_class_from_name(_snake_case     )

       snake_case   :							int						  =processor_auto_map is not None
       snake_case   :							List[Any]						  =processor_class is not None or type(_snake_case     ) in PROCESSOR_MAPPING
       snake_case   :							int						  =resolve_trust_remote_code(
           _snake_case,    _snake_case,    _snake_case,    _snake_case     )

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

       # 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(
              _snake_case,    trust_remote_code=_snake_case,    **_snake_case     )
       except Exception:
          try:
             return AutoImageProcessor.from_pretrained(
                 _snake_case,    trust_remote_code=_snake_case,    **_snake_case     )
          except Exception:
             pass

          try:
             return AutoFeatureExtractor.from_pretrained(
                 _snake_case,    trust_remote_code=_snake_case,    **_snake_case     )
          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 					__snake_case       (    _snake_case    :    Dict,    _snake_case    :    Dict     ):



       '''simple docstring'''







       PROCESSOR_MAPPING.register(_snake_case,    _snake_case     )










'''simple docstring'''





class 							lowerCAmelCase_		:





    def __init__(    self    :    List[Any],    _snake_case    :    Any     ):



       '''simple docstring'''







       snake_case   :							Tuple						  =data
       snake_case   :							int						  =None
       snake_case   :							List[Any]						  =None






    def __str__(    self    :    List[str]     ):



       '''simple docstring'''







       return f'''{self.data}'''





class 							lowerCAmelCase_		:





    def __init__(    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =None
       snake_case   :							int						  =None





    def __iter__(    self    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							str						  =self.head
       while node:
          yield node.data
          snake_case   :							Union[str, Any]						  =node.next





    def __str__(    self    :    List[str]     ):



       '''simple docstring'''







       return "->".join([str(_snake_case     ) for item in self]     )





    def __len__(    self    :    Dict     ):



       '''simple docstring'''







       return sum(1 for _ in self     )





    def 					__snake_case       (    self    :    List[Any],    _snake_case    :    str     ):



       '''simple docstring'''







       self.insert_at_nth(0,    _snake_case     )





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    Dict     ):



       '''simple docstring'''







       self.insert_at_nth(len(self     ),    _snake_case     )





    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    int,    _snake_case    :    Dict     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =len(self     )

       if not 0 <= index <= length:
          raise IndexError('''list index out of range'''     )
       snake_case   :							int						  =Node(_snake_case     )
       if self.head is None:
          snake_case   :							Optional[Any]						  =new_node
       elif index == 0:
          snake_case   :							int						  =new_node
          snake_case   :							Union[str, Any]						  =self.head
          snake_case   :							Optional[int]						  =new_node
       elif index == length:
          snake_case   :							Dict						  =new_node
          snake_case   :							int						  =self.tail
          snake_case   :							Union[str, Any]						  =new_node
       else:
          snake_case   :							Optional[int]						  =self.head
          for _ in range(0,    _snake_case     ):
             snake_case   :							Union[str, Any]						  =temp.next
          snake_case   :							int						  =new_node
          snake_case   :							Union[str, Any]						  =temp.previous
          snake_case   :							List[Any]						  =temp
          snake_case   :							int						  =new_node





    def 					__snake_case       (    self    :    str     ):



       '''simple docstring'''







       return self.delete_at_nth(0     )





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       return self.delete_at_nth(len(self     ) - 1     )





    def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    int     ):



       '''simple docstring'''







       snake_case   :							Dict						  =len(self     )

       if not 0 <= index <= length - 1:
          raise IndexError('''list index out of range'''     )
       snake_case   :							List[str]						  =self.head  # default first node
       if length == 1:
          snake_case   :							Union[str, Any]						  =None
       elif index == 0:
          snake_case   :							Tuple						  =self.head.next
          snake_case   :							Optional[int]						  =None
       elif index == length - 1:
          snake_case   :							Union[str, Any]						  =self.tail
          snake_case   :							List[str]						  =self.tail.previous
          snake_case   :							int						  =None
       else:
          snake_case   :							int						  =self.head
          for _ in range(0,    _snake_case     ):
             snake_case   :							Any						  =temp.next
          snake_case   :							List[Any]						  =temp
          snake_case   :							Optional[Any]						  =temp.previous
          snake_case   :							int						  =temp.next
       return delete_node.data





    def 					__snake_case       (    self    :    List[Any],    _snake_case    :    Tuple     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =self.head

       while current.data != data:  # Find the position to delete
          if current.next:
             snake_case   :							Any						  =current.next
          else:  # We have reached the end an no value matches
             raise ValueError('''No data matching given value'''     )

       if current == self.head:
          self.delete_head()

       elif current == self.tail:
          self.delete_tail()

       else:  # Before: 1 <--> 2(current) <--> 3
          snake_case   :							Optional[int]						  =current.next  # 1 --> 3
          snake_case   :							List[Any]						  =current.previous  # 1 <--> 3
       return data






    def 					__snake_case       (    self    :    Any     ):



       '''simple docstring'''







       return len(self     ) == 0




def 		_a   (  ):
   snake_case   :							Optional[Any]						  =DoublyLinkedList()
   assert linked_list.is_empty() is True
   assert str(lowerCamelCase_							) == ""

   try:
      linked_list.delete_head()
      raise AssertionError  # This should not happen.
   except IndexError:
      assert True  # This should happen.

   try:
      linked_list.delete_tail()
      raise AssertionError  # This should not happen.
   except IndexError:
      assert True  # This should happen.

   for i in range(10							):
      assert len(lowerCamelCase_							) == i
      linked_list.insert_at_nth(lowerCamelCase_					,						i + 1							)
   assert str(lowerCamelCase_							) == "->".join(str(lowerCamelCase_							) for i in range(1					,						11							)							)

   linked_list.insert_at_head(0							)
   linked_list.insert_at_tail(11							)
   assert str(lowerCamelCase_							) == "->".join(str(lowerCamelCase_							) for i in range(0					,						12							)							)

   assert linked_list.delete_head() == 0
   assert linked_list.delete_at_nth(9							) == 10
   assert linked_list.delete_tail() == 11
   assert len(lowerCamelCase_							) == 9
   assert str(lowerCamelCase_							) == "->".join(str(lowerCamelCase_							) for i in range(1					,						10							)							)


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

      testmod()





