

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# 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 ...utils import CONFIG_NAME, 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,
)


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

UpperCAmelCase__     = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 							A			(  _UpperCAmelCase       :     str					)       ->				Tuple:







			'''simple docstring'''
			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									_UpperCAmelCase	 =     model_type_to_module_name(_UpperCAmelCase					)

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

			for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
						if getattr(_UpperCAmelCase							,     '__name__'							,     _UpperCAmelCase					) == class_name:
									return extractor

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

			return None





def 							A			(  _UpperCAmelCase       :     Union[str, os.PathLike]							,     _UpperCAmelCase       :     Optional[Union[str, os.PathLike]] = None							,     _UpperCAmelCase       :     bool = False							,     _UpperCAmelCase       :     bool = False							,     _UpperCAmelCase       :     Optional[Dict[str, str]] = None							,     _UpperCAmelCase       :     Optional[Union[bool, str]] = None							,     _UpperCAmelCase       :     Optional[str] = None							,     _UpperCAmelCase       :     bool = False							,     **_UpperCAmelCase       :     Tuple							,     )       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     get_file_from_repo(
			    _UpperCAmelCase							,     _UpperCAmelCase							,     cache_dir=_UpperCAmelCase							,     force_download=_UpperCAmelCase							,     resume_download=_UpperCAmelCase							,     proxies=_UpperCAmelCase							,     use_auth_token=_UpperCAmelCase							,     revision=_UpperCAmelCase							,     local_files_only=_UpperCAmelCase							,     )
			if resolved_config_file is None:
						logger.info(
						    'Could not locate the feature extractor configuration file, will try to use the model config instead.'					)
						return {}

			with open(_UpperCAmelCase							,     encoding='utf-8'					) as reader:
						return json.load(_UpperCAmelCase					)







class     __lowerCAmelCase					:



			def __init__(		self     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						raise EnvironmentError(
						    'AutoFeatureExtractor is designed to be instantiated '
						    'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.')



			@classmethod
			@replace_list_option_in_docstrings(A)
			def 					_lowerCamelCase (		cls     :						Tuple				,	A     :						Union[str, Any]				,	**A     :						Any)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     kwargs.pop('config'				,	A)
						_UpperCAmelCase	 =     kwargs.pop('trust_remote_code'				,	A)
						_UpperCAmelCase	 =     True

						_UpperCAmelCase ,  _UpperCAmelCase	 =     FeatureExtractionMixin.get_feature_extractor_dict(A				,	**A)
						_UpperCAmelCase	 =     config_dict.get('feature_extractor_type'				,	A)
						_UpperCAmelCase	 =     None
						if "AutoFeatureExtractor" in config_dict.get('auto_map'				,	{}):
									_UpperCAmelCase	 =     config_dict['auto_map']['AutoFeatureExtractor']

						# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
						if feature_extractor_class is None and feature_extractor_auto_map is None:
									if not isinstance(A				,	A):
												_UpperCAmelCase	 =     AutoConfig.from_pretrained(A				,	**A)
									# It could be in `config.feature_extractor_type``
									_UpperCAmelCase	 =     getattr(A				,	'feature_extractor_type'				,	A)
									if hasattr(A				,	'auto_map') and "AutoFeatureExtractor" in config.auto_map:
												_UpperCAmelCase	 =     config.auto_map['AutoFeatureExtractor']

						if feature_extractor_class is not None:
									_UpperCAmelCase	 =     feature_extractor_class_from_name(A)

						_UpperCAmelCase	 =     feature_extractor_auto_map is not None
						_UpperCAmelCase	 =     feature_extractor_class is not None or type(A) in FEATURE_EXTRACTOR_MAPPING
						_UpperCAmelCase	 =     resolve_trust_remote_code(
						    A				,	A				,	A				,	A)

						if has_remote_code and trust_remote_code:
									_UpperCAmelCase	 =     get_class_from_dynamic_module(
									    A				,	A				,	**A)
									_UpperCAmelCase	 =     kwargs.pop('code_revision'				,	A)
									if os.path.isdir(A):
												feature_extractor_class.register_for_auto_class()
									return feature_extractor_class.from_dict(A				,	**A)
						elif feature_extractor_class is not None:
									return feature_extractor_class.from_dict(A				,	**A)
						# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
						elif type(A) in FEATURE_EXTRACTOR_MAPPING:
									_UpperCAmelCase	 =     FEATURE_EXTRACTOR_MAPPING[type(A)]
									return feature_extractor_class.from_dict(A				,	**A)

						raise ValueError(
						    F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
						    F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
						    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}")



			@staticmethod
			def 					_lowerCamelCase (		A     :						int				,	A     :						Any)     ->					Union[str, Any]:



						"""simple docstring"""






						FEATURE_EXTRACTOR_MAPPING.register(A				,	A)





import os

# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__     = [int(0.5 * n * (n + 1)) for n in range(1, 101)]





def 							A			(  )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
			_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     'words.txt'					)

			_UpperCAmelCase	 =     ''
			with open(_UpperCAmelCase					) as f:
						_UpperCAmelCase	 =     f.readline()

			_UpperCAmelCase	 =     [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
			_UpperCAmelCase	 =     [
			    word
			    for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase					)


if __name__ == "__main__":
							print(solution())
