



"""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


_UpperCamelCase:					str     				=	logging.get_logger(__name__)

_UpperCamelCase:					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:					Any     				=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)


def 	lowercase__  (      _UpperCAmelCase					)  ->      Union[str, Any]:






      '''simple docstring'''


      for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
            if class_name in processors:
                  lowercase	:   Union[str, Any]											= model_type_to_module_name(_UpperCAmelCase					)

                  lowercase	:   Tuple											= importlib.import_module(f'''.{module_name}'''						,   'transformers.models'					)
                  try:
                        return getattr(_UpperCAmelCase						,   _UpperCAmelCase					)
                  except AttributeError:
                        continue

      for processor in PROCESSOR_MAPPING._extra_content.values():
            if getattr(_UpperCAmelCase						,   '__name__'						,   _UpperCAmelCase					) == 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.
      lowercase	:   Tuple											= importlib.import_module('transformers'					)
      if hasattr(_UpperCAmelCase						,   _UpperCAmelCase					):
            return getattr(_UpperCAmelCase						,   _UpperCAmelCase					)

      return None





class 	a__		:


   def __init__(				self   :				Union[str, Any]      )     ->							Tuple:
         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 							lowercase					(				cls   :				List[Any],  lowerCAmelCase   :				Any,  **lowerCAmelCase   :				int      )     ->							Any:
         lowercase	:   List[Any]											= kwargs.pop('config',  _lowerCAmelCase      )
         lowercase	:   Optional[Any]											= kwargs.pop('trust_remote_code',  _lowerCAmelCase      )
         lowercase	:   Any											= True

         lowercase	:   str											= None
         lowercase	:   List[str]											= None

         # First, let's see if we have a preprocessor config.
         # Filter the kwargs for `get_file_from_repo`.
         lowercase	:   Optional[Any]											= {
             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
         lowercase	:   Tuple											= get_file_from_repo(
             _lowerCAmelCase,  _lowerCAmelCase,  **_lowerCAmelCase      )
         if preprocessor_config_file is not None:
               lowercase						,     lowercase	:   Union[str, Any]											= ImageProcessingMixin.get_image_processor_dict(_lowerCAmelCase,  **_lowerCAmelCase      )
               lowercase	:   Optional[int]											= config_dict.get('processor_class',  _lowerCAmelCase      )
               if "AutoProcessor" in config_dict.get('auto_map',  {}      ):
                     lowercase	:   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:
               lowercase						,     lowercase	:   Dict											= FeatureExtractionMixin.get_feature_extractor_dict(_lowerCAmelCase,  **_lowerCAmelCase      )
               lowercase	:   Union[str, Any]											= config_dict.get('processor_class',  _lowerCAmelCase      )
               if "AutoProcessor" in config_dict.get('auto_map',  {}      ):
                     lowercase	:   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
               lowercase	:   int											= get_file_from_repo(
                   _lowerCAmelCase,  _lowerCAmelCase,  **_lowerCAmelCase      )
               if tokenizer_config_file is not None:
                     with open(_lowerCAmelCase,  encoding='utf-8'      ) as reader:
                           lowercase	:   Optional[Any]											= json.load(_lowerCAmelCase      )

                     lowercase	:   str											= config_dict.get('processor_class',  _lowerCAmelCase      )
                     if "AutoProcessor" in config_dict.get('auto_map',  {}      ):
                           lowercase	:   List[Any]											= config_dict['auto_map']['AutoProcessor']

         if processor_class is None:
               # Otherwise, load config, if it can be loaded.
               if not isinstance(_lowerCAmelCase,  _lowerCAmelCase      ):
                     lowercase	:   List[Any]											= AutoConfig.from_pretrained(
                         _lowerCAmelCase,  trust_remote_code=_lowerCAmelCase,  **_lowerCAmelCase      )

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

         if processor_class is not None:
               lowercase	:   Dict											= processor_class_from_name(_lowerCAmelCase      )

         lowercase	:   Dict											= processor_auto_map is not None
         lowercase	:   Optional[int]											= processor_class is not None or type(_lowerCAmelCase      ) in PROCESSOR_MAPPING
         lowercase	:   int											= resolve_trust_remote_code(
             _lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase      )

         if has_remote_code and trust_remote_code:
               lowercase	:   List[Any]											= get_class_from_dynamic_module(
                   _lowerCAmelCase,  _lowerCAmelCase,  **_lowerCAmelCase      )
               lowercase	:   List[Any]											= 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 							lowercase					(				lowerCAmelCase   :				List[Any],  lowerCAmelCase   :				int      )     ->							int:
         PROCESSOR_MAPPING.register(_lowerCAmelCase,  _lowerCAmelCase      )







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

