
"""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   :      Optional[int]       =				logging.get_logger(__name__)

_a   :      Tuple       =				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   :      Any       =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def 					a__ ( a :					str		):

  """simple docstring"""







  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      _snake_case  :    Dict            = model_type_to_module_name(a		)

      _snake_case  :    Optional[int]            = importlib.import_module(f'.{module_name}'							,  "transformers.models"		)
      try:
        return getattr(a							,  a		)
      except AttributeError:
        continue

  for processor in PROCESSOR_MAPPING._extra_content.values():
    if getattr(a							,  "__name__"							,  a		) == 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  :    int            = importlib.import_module("transformers"		)
  if hasattr(a							,  a		):
    return getattr(a							,  a		)

  return None






class   _UpperCAmelCase					:
    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(snake_case_  )
    def        lowerCamelCase__						(     cls    ,     snake_case_    ,     **snake_case_  ):
      _snake_case  :    str            = kwargs.pop("config"    ,     snake_case_  )
      _snake_case  :    Dict            = kwargs.pop("trust_remote_code"    ,     snake_case_  )
      _snake_case  :    Tuple            = True

      _snake_case  :    List[str]            = None
      _snake_case  :    Optional[Any]            = None

      # First, let's see if we have a preprocessor config.
      # Filter the kwargs for `get_file_from_repo`.
      _snake_case  :    Optional[int]            = {
          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  :    Dict            = get_file_from_repo(
          snake_case_    ,     snake_case_    ,     **snake_case_  )
      if preprocessor_config_file is not None:
        _snake_case      ,       _snake_case  :    List[str]            = ImageProcessingMixin.get_image_processor_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  :    Optional[int]            = 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  :    Any            = FeatureExtractionMixin.get_feature_extractor_dict(snake_case_    ,     **snake_case_  )
        _snake_case  :    List[Any]            = config_dict.get("processor_class"    ,     snake_case_  )
        if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
          _snake_case  :    int            = 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  :    Union[str, Any]            = 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  :    Tuple            = json.load(snake_case_  )

          _snake_case  :    List[Any]            = config_dict.get("processor_class"    ,     snake_case_  )
          if "AutoProcessor" in config_dict.get("auto_map"    ,     {}  ):
            _snake_case  :    Tuple            = 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  :    Union[str, Any]            = AutoConfig.from_pretrained(
              snake_case_    ,     trust_remote_code=snake_case_    ,     **snake_case_  )

        # And check if the config contains the processor class.
        _snake_case  :    List[Any]            = 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  :    Optional[int]            = processor_class_from_name(snake_case_  )

      _snake_case  :    Optional[int]            = processor_auto_map is not None
      _snake_case  :    Any            = processor_class is not None or type(snake_case_  ) in PROCESSOR_MAPPING
      _snake_case  :    Union[str, Any]            = resolve_trust_remote_code(
          snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  )

      if has_remote_code and trust_remote_code:
        _snake_case  :    Dict            = get_class_from_dynamic_module(
            snake_case_    ,     snake_case_    ,     **snake_case_  )
        _snake_case  :    Union[str, Any]            = 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        lowerCamelCase__						(     snake_case_    ,     snake_case_  ):
      PROCESSOR_MAPPING.register(snake_case_    ,     snake_case_  )





"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



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



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



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



														"""simple docstring"""



														return self.length



