
"""simple docstring"""


from math import gcd



def     _lowerCamelCase(__UpperCamelCase = 12000       )				-> int:
	_lowerCAmelCase								=0
	for d in range(max_d + 1       ):
		for n in range(d // 3 + 1    , (d + 1) // 2       ):
			if gcd(__UpperCamelCase    , __UpperCamelCase       ) == 1:
				fractions_number += 1
	return fractions_number


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





"""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     _lowerCamelCase(__UpperCamelCase       )				-> Optional[int]:
	for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
		if class_name in processors:
			_lowerCAmelCase								=model_type_to_module_name(__UpperCamelCase       )

			_lowerCAmelCase								=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.
	_lowerCAmelCase								=importlib.import_module("""transformers"""       )
	if hasattr(__UpperCamelCase    , __UpperCamelCase       ):
		return getattr(__UpperCamelCase    , __UpperCamelCase       )

	return None







class 		lowerCamelCase__					:





			'''simple docstring'''



			def __init__(							self  ) ->     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(__UpperCAmelCase  )
			def 					_lowerCAmelCase		(							cls     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     Tuple:
				_lowerCAmelCase								=kwargs.pop("""config"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=kwargs.pop("""trust_remote_code"""     ,     __UpperCAmelCase  )
				_lowerCAmelCase								=True

				_lowerCAmelCase								=None
				_lowerCAmelCase								=None

				# First, let's see if we have a preprocessor config.
				# Filter the kwargs for `get_file_from_repo`.
				_lowerCAmelCase								={
				    key: kwargs[key] for key in inspect.signature(__UpperCAmelCase  ).parameters.keys() if key in kwargs
				}
				# Let's start by checking whether the processor class is saved in an image processor
				_lowerCAmelCase								=get_file_from_repo(
				    __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )
				if preprocessor_config_file is not None:
					_lowerCAmelCase						,							_lowerCAmelCase								=ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase     ,     **__UpperCAmelCase  )
					_lowerCAmelCase								=config_dict.get("""processor_class"""     ,     __UpperCAmelCase  )
					if "AutoProcessor" in config_dict.get("""auto_map"""     ,     {}  ):
						_lowerCAmelCase								=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:
					_lowerCAmelCase						,							_lowerCAmelCase								=FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase     ,     **__UpperCAmelCase  )
					_lowerCAmelCase								=config_dict.get("""processor_class"""     ,     __UpperCAmelCase  )
					if "AutoProcessor" in config_dict.get("""auto_map"""     ,     {}  ):
						_lowerCAmelCase								=config_dict["""auto_map"""]["""AutoProcessor"""]

				if processor_class is None:
					# Next, let's check whether the processor class is saved in a tokenizer
					_lowerCAmelCase								=get_file_from_repo(
					    __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )
					if tokenizer_config_file is not None:
						with open(__UpperCAmelCase     ,     encoding="""utf-8"""  ) as reader:
							_lowerCAmelCase								=json.load(__UpperCAmelCase  )

						_lowerCAmelCase								=config_dict.get("""processor_class"""     ,     __UpperCAmelCase  )
						if "AutoProcessor" in config_dict.get("""auto_map"""     ,     {}  ):
							_lowerCAmelCase								=config_dict["""auto_map"""]["""AutoProcessor"""]

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

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

				if processor_class is not None:
					_lowerCAmelCase								=processor_class_from_name(__UpperCAmelCase  )

				_lowerCAmelCase								=processor_auto_map is not None
				_lowerCAmelCase								=processor_class is not None or type(__UpperCAmelCase  ) in PROCESSOR_MAPPING
				_lowerCAmelCase								=resolve_trust_remote_code(
				    __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  )

				if has_remote_code and trust_remote_code:
					_lowerCAmelCase								=get_class_from_dynamic_module(
					    __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )
					_lowerCAmelCase								=kwargs.pop("""code_revision"""     ,     __UpperCAmelCase  )
					if os.path.isdir(__UpperCAmelCase  ):
						processor_class.register_for_auto_class()
					return processor_class.from_pretrained(
					    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
				elif processor_class is not None:
					return processor_class.from_pretrained(
					    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
				# Last try: we use the PROCESSOR_MAPPING.
				elif type(__UpperCAmelCase  ) in PROCESSOR_MAPPING:
					return PROCESSOR_MAPPING[type(__UpperCAmelCase  )].from_pretrained(__UpperCAmelCase     ,     **__UpperCAmelCase  )

				# 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(
					    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
				except Exception:
					try:
						return AutoImageProcessor.from_pretrained(
						    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
					except Exception:
						pass

					try:
						return AutoFeatureExtractor.from_pretrained(
						    __UpperCAmelCase     ,     trust_remote_code=__UpperCAmelCase     ,     **__UpperCAmelCase  )
					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		(							__UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Optional[int]:
				PROCESSOR_MAPPING.register(__UpperCAmelCase     ,     __UpperCAmelCase  )



