
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


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	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"""),
    ]
)

lowercase_											=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def     a__			(    snake_case    ):





  """simple docstring"""
  for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
    if class_name in processors:
      __SCREAMING_SNAKE_CASE     :					List[str]			=						model_type_to_module_name(snake_case    )

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

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

  return None





class   __UpperCamelCase				:






     """simple docstring"""







     def __init__(							self    :    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    :    str      ,		_A    :    Union[str, Any]      ,		**_A    :    Optional[int]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						kwargs.pop('''config'''      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''trust_remote_code'''      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						True

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Dict			=						None

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

           __SCREAMING_SNAKE_CASE     :					List[str]			=						config_dict.get('''processor_class'''      ,		_A				)
           if "AutoProcessor" in config_dict.get('''auto_map'''      ,		{}				):
             __SCREAMING_SNAKE_CASE     :					Dict			=						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     :					List[Any]			=						AutoConfig.from_pretrained(
               _A      ,		trust_remote_code=_A      ,		**_A				)

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

       if processor_class is not None:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						processor_class_from_name(_A				)

       __SCREAMING_SNAKE_CASE     :					str			=						processor_auto_map is not None
       __SCREAMING_SNAKE_CASE     :					Tuple			=						processor_class is not None or type(_A				) in PROCESSOR_MAPPING
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						resolve_trust_remote_code(
           _A      ,		_A      ,		_A      ,		_A				)

       if has_remote_code and trust_remote_code:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						get_class_from_dynamic_module(
             _A      ,		_A      ,		**_A				)
         __SCREAMING_SNAKE_CASE     :					int			=						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__    (							_A    :    List[str]      ,		_A    :    Tuple				):







       """simple docstring"""




       PROCESSOR_MAPPING.register(_A      ,		_A				)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""LEDTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
