




"""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


SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=			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"),
    ]
)

SCREAMING_SNAKE_CASE							=			_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						int:
 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   A__			=							model_type_to_module_name(lowercase_		)

   A__			=							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.
 A__			=							importlib.import_module("transformers"		)
 if hasattr(lowercase_     ,					lowercase_		):
  return getattr(lowercase_     ,					lowercase_		)

 return None






class       UpperCAmelCase_    :




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




     '''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 				__magic_name__				(       cls		:			Optional[int]      ,					snake_case_		:			Tuple      ,					**snake_case_		:			Optional[int] ) ->				Dict:




     '''simple docstring'''

     A__			=							kwargs.pop("config"      ,					snake_case_ )
     A__			=							kwargs.pop("trust_remote_code"      ,					snake_case_ )
     A__			=							True

     A__			=							None
     A__			=							None

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

     if processor_class is None:
      # Next, let's check whether the processor class is saved in a tokenizer
      A__			=							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:
        A__			=							json.load(snake_case_ )

       A__			=							config_dict.get("processor_class"      ,					snake_case_ )
       if "AutoProcessor" in config_dict.get("auto_map"      ,					{} ):
        A__			=							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_ ):
       A__			=							AutoConfig.from_pretrained(
           snake_case_      ,					trust_remote_code=snake_case_      ,					**snake_case_ )

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

     if processor_class is not None:
      A__			=							processor_class_from_name(snake_case_ )

     A__			=							processor_auto_map is not None
     A__			=							processor_class is not None or type(snake_case_ ) in PROCESSOR_MAPPING
     A__			=							resolve_trust_remote_code(
         snake_case_      ,					snake_case_      ,					snake_case_      ,					snake_case_ )

     if has_remote_code and trust_remote_code:
      A__			=							get_class_from_dynamic_module(
          snake_case_      ,					snake_case_      ,					**snake_case_ )
      A__			=							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 				__magic_name__				(       snake_case_		:			Tuple      ,					snake_case_		:			Union[str, Any] ) ->				str:




     '''simple docstring'''

     PROCESSOR_MAPPING.register(snake_case_      ,					snake_case_ )











"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							=			{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPRContextEncoderTokenizerFast",
          "DPRQuestionEncoderTokenizerFast",
          "DPRReaderTokenizerFast",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPRContextEncoder",
          "DPRPretrainedContextEncoder",
          "DPRPreTrainedModel",
          "DPRPretrainedQuestionEncoder",
          "DPRPretrainedReader",
          "DPRQuestionEncoder",
          "DPRReader",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDPRContextEncoder",
          "TFDPRPretrainedContextEncoder",
          "TFDPRPretrainedQuestionEncoder",
          "TFDPRPretrainedReader",
          "TFDPRQuestionEncoder",
          "TFDPRReader",
      ]


if TYPE_CHECKING:
      from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
      from .tokenization_dpr import (
          DPRContextEncoderTokenizer,
          DPRQuestionEncoderTokenizer,
          DPRReaderOutput,
          DPRReaderTokenizer,
      )

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_dpr_fast import (
                DPRContextEncoderTokenizerFast,
                DPRQuestionEncoderTokenizerFast,
                DPRReaderTokenizerFast,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpr import (
                DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPRContextEncoder,
                DPRPretrainedContextEncoder,
                DPRPreTrainedModel,
                DPRPretrainedQuestionEncoder,
                DPRPretrainedReader,
                DPRQuestionEncoder,
                DPRReader,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_dpr import (
                TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDPRContextEncoder,
                TFDPRPretrainedContextEncoder,
                TFDPRPretrainedQuestionEncoder,
                TFDPRPretrainedReader,
                TFDPRQuestionEncoder,
                TFDPRReader,
            )

else:
      import sys

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





