


"""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


lowerCamelCase_     :     str    		=						logging.get_logger(__name__)

lowerCamelCase_     :     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"""),
    ]
)

lowerCamelCase_     :     Tuple    		=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)
def _A		(		lowercase	):







    """simple docstring"""
    for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
        if class_name in processors:
            a					=model_type_to_module_name(_UpperCAmelCase	)

            a					=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.
    a					=importlib.import_module('''transformers'''	)
    if hasattr(_UpperCAmelCase ,					_UpperCAmelCase	):
        return getattr(_UpperCAmelCase ,					_UpperCAmelCase	)

    return None






class    __A       :






 """simple docstring"""




 def __init__(	self		)      ->  List[str]:
     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    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					**__A		)      ->  Tuple:
     a					=kwargs.pop('''config'''				,					SCREAMING_SNAKE_CASE_		)
     a					=kwargs.pop('''trust_remote_code'''				,					SCREAMING_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(SCREAMING_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(
         SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
     if preprocessor_config_file is not None:
         a	,   a					=ImageProcessingMixin.get_image_processor_dict(SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
         a					=config_dict.get('''processor_class'''				,					SCREAMING_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(SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
         a					=config_dict.get('''processor_class'''				,					SCREAMING_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(
             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:
                 a					=json.load(SCREAMING_SNAKE_CASE_		)

             a					=config_dict.get('''processor_class'''				,					SCREAMING_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(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_		):
             a					=AutoConfig.from_pretrained(
                 SCREAMING_SNAKE_CASE_				,					trust_remote_code=SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)

         # And check if the config contains the processor class.
         a					=getattr(SCREAMING_SNAKE_CASE_				,					'''processor_class'''				,					SCREAMING_SNAKE_CASE_		)
         if hasattr(SCREAMING_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(SCREAMING_SNAKE_CASE_		)

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

     if has_remote_code and trust_remote_code:
         a					=get_class_from_dynamic_module(
             SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					**SCREAMING_SNAKE_CASE_		)
         a					=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    SCREAMING_SNAKE_CASE							(	__A				,					__A		)      ->  Any:
     PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_		)



"""simple docstring"""







from math import gcd
def _A		(		lowercase = 1_20_00	):







    """simple docstring"""
    a					=0
    for d in range(max_d + 1	):
        for n in range(d // 3 + 1 ,					(d + 1) // 2	):
            if gcd(lowercase ,					lowercase	) == 1:
                fractions_number += 1
    return fractions_number


if __name__ == "__main__":
       print(F'{solution() = }')