




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

UpperCAmelCase_    :					Union[str, Any]					      =						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_    :					str					      =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)







def      _UpperCamelCase	(_lowerCamelCase							:       str		)->				List[str]:




     '''simple docstring'''





     for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
          if class_name in processors:
               __snake_case         =					model_type_to_module_name(_lowerCamelCase		)

               __snake_case         =					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.
     __snake_case         =					importlib.import_module('''transformers'''		)
     if hasattr(_lowerCamelCase				,		_lowerCamelCase		):
          return getattr(_lowerCamelCase				,		_lowerCamelCase		)

     return None







class       lowerCAmelCase				:


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



            '''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(SCREAMING_SNAKE_CASE_					)
       def  lowerCAmelCase (				cls      ,					__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					kwargs.pop('''config'''      ,					SCREAMING_SNAKE_CASE_					)
            __snake_case         =					kwargs.pop('''trust_remote_code'''      ,					SCREAMING_SNAKE_CASE_					)
            __snake_case         =					True

            __snake_case         =					None
            __snake_case         =					None

            # First, let's see if we have a preprocessor config.
            # Filter the kwargs for `get_file_from_repo`.
            __snake_case         =					{
                key: kwargs[key] for key in inspect.signature(SCREAMING_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         =					get_file_from_repo(
                SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_					)
            if preprocessor_config_file is not None:
                 __snake_case   ,      __snake_case         =					ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_					)
                 __snake_case         =					config_dict.get('''processor_class'''      ,					SCREAMING_SNAKE_CASE_					)
                 if "AutoProcessor" in config_dict.get('''auto_map'''      ,					{}					):
                      __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:
                 __snake_case   ,      __snake_case         =					FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_					)
                 __snake_case         =					config_dict.get('''processor_class'''      ,					SCREAMING_SNAKE_CASE_					)
                 if "AutoProcessor" in config_dict.get('''auto_map'''      ,					{}					):
                      __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
                 __snake_case         =					get_file_from_repo(
                     SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_					)
                 if tokenizer_config_file is not None:
                      with open(SCREAMING_SNAKE_CASE_      ,					encoding='''utf-8'''					) as reader:
                           __snake_case         =					json.load(SCREAMING_SNAKE_CASE_					)

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

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

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

            if processor_class is not None:
                 __snake_case         =					processor_class_from_name(SCREAMING_SNAKE_CASE_					)

            __snake_case         =					processor_auto_map is not None
            __snake_case         =					processor_class is not None or type(SCREAMING_SNAKE_CASE_					) in PROCESSOR_MAPPING
            __snake_case         =					resolve_trust_remote_code(
                SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_					)

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

                 try:
                      return AutoFeatureExtractor.from_pretrained(
                          SCREAMING_SNAKE_CASE_      ,					trust_remote_code=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_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 (				__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					int:



            '''simple docstring'''




            PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_					)












'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






