

"""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						=     logging.get_logger(__name__)

_A						=     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						=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def 				a__	(	lowerCAmelCase						)  ->   List[str]:
      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  UpperCAmelCase__ :       str						=						model_type_to_module_name(lowerCAmelCase						)

                  UpperCAmelCase__ :       int						=						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.
      UpperCAmelCase__ :       str						=						importlib.import_module("""transformers"""						)
      if hasattr(lowerCAmelCase ,  lowerCAmelCase						):
            return getattr(lowerCAmelCase ,  lowerCAmelCase						)

      return None





class 			lowerCamelCase  :


  '''simple docstring'''







  def __init__(self  ):


        """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       ,					_lowerCamelCase       ,					**_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       int						=						kwargs.pop("""config"""       ,					_lowerCamelCase  )
        UpperCAmelCase__ :       Tuple						=						kwargs.pop("""trust_remote_code"""       ,					_lowerCamelCase  )
        UpperCAmelCase__ :       List[Any]						=						True

        UpperCAmelCase__ :       int						=						None
        UpperCAmelCase__ :       str						=						None

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

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

                    UpperCAmelCase__ :       Optional[Any]						=						config_dict.get("""processor_class"""       ,					_lowerCamelCase  )
                    if "AutoProcessor" in config_dict.get("""auto_map"""       ,					{}  ):
                          UpperCAmelCase__ :       Optional[Any]						=						config_dict["""auto_map"""]["""AutoProcessor"""]

        if processor_class is None:
              # Otherwise, load config, if it can be loaded.
              if not isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                    UpperCAmelCase__ :       Union[str, Any]						=						AutoConfig.from_pretrained(
                        _lowerCamelCase       ,					trust_remote_code=_lowerCamelCase       ,					**_lowerCamelCase  )

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

        if processor_class is not None:
              UpperCAmelCase__ :       Optional[Any]						=						processor_class_from_name(_lowerCamelCase  )

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

        if has_remote_code and trust_remote_code:
              UpperCAmelCase__ :       Optional[int]						=						get_class_from_dynamic_module(
                  _lowerCamelCase       ,					_lowerCamelCase       ,					**_lowerCamelCase  )
              UpperCAmelCase__ :       str						=						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       ,					_lowerCamelCase  ):


        """simple docstring"""

        PROCESSOR_MAPPING.register(_lowerCamelCase       ,					_lowerCamelCase  )




"""simple docstring"""
from typing import TYPE_CHECKING

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


_A						=     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""GPT2TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPT2DoubleHeadsModel""",
        """GPT2ForQuestionAnswering""",
        """GPT2ForSequenceClassification""",
        """GPT2ForTokenClassification""",
        """GPT2LMHeadModel""",
        """GPT2Model""",
        """GPT2PreTrainedModel""",
        """load_tf_weights_in_gpt2""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFGPT2DoubleHeadsModel""",
        """TFGPT2ForSequenceClassification""",
        """TFGPT2LMHeadModel""",
        """TFGPT2MainLayer""",
        """TFGPT2Model""",
        """TFGPT2PreTrainedModel""",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""TFGPT2Tokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

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

