






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


__UpperCAmelCase      =				logging.get_logger(__name__)

__UpperCAmelCase      =				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"),
    ]
)

__UpperCAmelCase      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   SCREAMING_SNAKE_CASE				    =		model_type_to_module_name(lowercase_			)

   SCREAMING_SNAKE_CASE				    =		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.
 SCREAMING_SNAKE_CASE				    =		importlib.import_module('transformers'			)
 if hasattr(lowercase_	,  lowercase_			):
  return getattr(lowercase_	,  lowercase_			)

 return None
class 	a_:



     """simple docstring"""



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

      """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(_A)
     def     __UpperCamelCase						(		cls					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[int]  ,  **lowerCAmelCase__					:	int)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('config'  ,  _A)
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('trust_remote_code'  ,  _A)
      SCREAMING_SNAKE_CASE				    =		True

      SCREAMING_SNAKE_CASE				    =		None
      SCREAMING_SNAKE_CASE				    =		None

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

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

        SCREAMING_SNAKE_CASE				    =		config_dict.get('processor_class'  ,  _A)
        if "AutoProcessor" in config_dict.get('auto_map'  ,  {}):
         SCREAMING_SNAKE_CASE				    =		config_dict['auto_map']['AutoProcessor']

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

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

      if processor_class is not None:
       SCREAMING_SNAKE_CASE				    =		processor_class_from_name(_A)

      SCREAMING_SNAKE_CASE				    =		processor_auto_map is not None
      SCREAMING_SNAKE_CASE				    =		processor_class is not None or type(_A) in PROCESSOR_MAPPING
      SCREAMING_SNAKE_CASE				    =		resolve_trust_remote_code(
          _A  ,  _A  ,  _A  ,  _A)

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

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

       try:
        return AutoFeatureExtractor.from_pretrained(
            _A  ,  trust_remote_code=_A  ,  **_A)
       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     __UpperCamelCase						(		lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	int)							->	List[str]:

      """simple docstring"""
      PROCESSOR_MAPPING.register(_A  ,  _A)






'''simple docstring'''





class 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]						,					_A	:Tuple      )					->      Tuple:







       '''simple docstring'''



       __A													=      data
       __A													=      None
       __A													=      None






 def __str__(      self	:List[str]      )					->      Optional[int]:







       '''simple docstring'''



       return F'{self.data}'


class 			UpperCamelCase__	:






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







       '''simple docstring'''



       __A													=      None
       __A													=      None






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







       '''simple docstring'''



       __A													=      self.head
       while node:
             yield node.data
             __A													=      node.next






 def __str__(      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



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






 def __len__(      self	:int      )					->      List[str]:







       '''simple docstring'''



       return sum(1 for _ in self      )






 def 						lowercase_    (      self	:List[str]						,					_A	:Any      )					->      str:







       '''simple docstring'''



       self.insert_at_nth(0						,					_A      )






 def 						lowercase_    (      self	:Dict						,					_A	:Optional[Any]      )					->      Tuple:







       '''simple docstring'''



       self.insert_at_nth(len(self      )						,					_A      )






 def 						lowercase_    (      self	:Optional[int]						,					_A	:int						,					_A	:Optional[int]      )					->      int:







       '''simple docstring'''



       __A													=      len(self      )

       if not 0 <= index <= length:
             raise IndexError('list index out of range'      )
       __A													=      Node(_A      )
       if self.head is None:
             __A													=      __A													=      new_node
       elif index == 0:
             __A													=      new_node
             __A													=      self.head
             __A													=      new_node
       elif index == length:
             __A													=      new_node
             __A													=      self.tail
             __A													=      new_node
       else:
             __A													=      self.head
             for _ in range(0						,					_A      ):
                   __A													=      temp.next
             __A													=      new_node
             __A													=      temp.previous
             __A													=      temp
             __A													=      new_node






 def 						lowercase_    (      self	:Optional[int]      )					->      List[str]:







       '''simple docstring'''



       return self.delete_at_nth(0      )






 def 						lowercase_    (      self	:Any      )					->      Tuple:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Dict						,					_A	:int      )					->      List[str]:







       '''simple docstring'''



       __A													=      len(self      )

       if not 0 <= index <= length - 1:
             raise IndexError('list index out of range'      )
       __A													=      self.head  # default first node
       if length == 1:
             __A													=      __A													=      None
       elif index == 0:
             __A													=      self.head.next
             __A													=      None
       elif index == length - 1:
             __A													=      self.tail
             __A													=      self.tail.previous
             __A													=      None
       else:
             __A													=      self.head
             for _ in range(0						,					_A      ):
                   __A													=      temp.next
             __A													=      temp
             __A													=      temp.previous
             __A													=      temp.next
       return delete_node.data






 def 						lowercase_    (      self	:List[str]						,					_A	:Dict      )					->      str:







       '''simple docstring'''



       __A													=      self.head

       while current.data != data:  # Find the position to delete
             if current.next:
                   __A													=      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
             __A													=      current.next  # 1 --> 3
             __A													=      current.previous  # 1 <--> 3
       return data






 def 						lowercase_    (      self	:List[str]      )					->      Tuple:







       '''simple docstring'''



       return len(self      ) == 0





def 							snake_case				(		)->      None:







      """simple docstring"""
      __A													=      DoublyLinkedList()
      assert linked_list.is_empty() is True
      assert str(UpperCAmelCase						) == ""

      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(1_0						):
            assert len(UpperCAmelCase						) == i
            linked_list.insert_at_nth(UpperCAmelCase   ,	i + 1						)
      assert str(UpperCAmelCase						) == "->".join(str(UpperCAmelCase						) for i in range(1   ,	1_1						)						)

      linked_list.insert_at_head(0						)
      linked_list.insert_at_tail(1_1						)
      assert str(UpperCAmelCase						) == "->".join(str(UpperCAmelCase						) for i in range(0   ,	1_2						)						)

      assert linked_list.delete_head() == 0
      assert linked_list.delete_at_nth(9						) == 1_0
      assert linked_list.delete_tail() == 1_1
      assert len(UpperCAmelCase						) == 9
      assert str(UpperCAmelCase						) == "->".join(str(UpperCAmelCase						) for i in range(1   ,	1_0						)						)


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

 testmod()






