






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,
)


__snake_case    :    Tuple         =     logging.get_logger(__name__)

__snake_case    :    Any         =     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"""),
    ]
)

__snake_case    :    Tuple         =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str		)  ->   Union[str, Any]:

						"""simple docstring"""



						for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
												if class_name in extractors:
																		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 _, 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.
						lowerCAmelCase__    						=						importlib.import_module('transformers'		)
						if hasattr(UpperCamelCase_    ,       UpperCamelCase_		):
												return getattr(UpperCamelCase_    ,       UpperCamelCase_		)

						return None







def 	_UpperCamelCase   (    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_       :					Optional[int]    ,       )  ->   int:

						"""simple docstring"""



						lowerCAmelCase__    						=						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     __SCREAMING_SNAKE_CASE					:






						def __init__(	self   ):






												"""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(_UpperCamelCase   )
						def       UpperCamelCase__      (	cls     ,						_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.pop('config'     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						kwargs.pop('trust_remote_code'     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						True

												lowerCAmelCase__						,       lowerCAmelCase__    						=						FeatureExtractionMixin.get_feature_extractor_dict(_UpperCamelCase     ,						**_UpperCamelCase   )
												lowerCAmelCase__    						=						config_dict.get('feature_extractor_type'     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						None
												if "AutoFeatureExtractor" in config_dict.get('auto_map'     ,						{}   ):
																		lowerCAmelCase__    						=						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(_UpperCamelCase     ,						_UpperCamelCase   ):
																								lowerCAmelCase__    						=						AutoConfig.from_pretrained(_UpperCamelCase     ,						**_UpperCamelCase   )
																		# It could be in `config.feature_extractor_type``
																		lowerCAmelCase__    						=						getattr(_UpperCamelCase     ,						'feature_extractor_type'     ,						_UpperCamelCase   )
																		if hasattr(_UpperCamelCase     ,						'auto_map'   ) and "AutoFeatureExtractor" in config.auto_map:
																								lowerCAmelCase__    						=						config.auto_map['AutoFeatureExtractor']

												if feature_extractor_class is not None:
																		lowerCAmelCase__    						=						feature_extractor_class_from_name(_UpperCamelCase   )

												lowerCAmelCase__    						=						feature_extractor_auto_map is not None
												lowerCAmelCase__    						=						feature_extractor_class is not None or type(_UpperCamelCase   ) in FEATURE_EXTRACTOR_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   ):
																								feature_extractor_class.register_for_auto_class()
																		return feature_extractor_class.from_dict(_UpperCamelCase     ,						**_UpperCamelCase   )
												elif feature_extractor_class is not None:
																		return feature_extractor_class.from_dict(_UpperCamelCase     ,						**_UpperCamelCase   )
												# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
												elif type(_UpperCamelCase   ) in FEATURE_EXTRACTOR_MAPPING:
																		lowerCAmelCase__    						=						FEATURE_EXTRACTOR_MAPPING[type(_UpperCamelCase   )]
																		return feature_extractor_class.from_dict(_UpperCamelCase     ,						**_UpperCamelCase   )

												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       UpperCamelCase__      (	_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												FEATURE_EXTRACTOR_MAPPING.register(_UpperCamelCase     ,						_UpperCamelCase   )













from __future__ import annotations

from functools import lru_cache
from math import ceil

__snake_case    :    Union[str, Any]         =     1_00

__snake_case    :    Tuple         =     set(range(3, NUM_PRIMES, 2))
primes.add(2)
__snake_case    :    int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))







@lru_cache(maxsize=100		)
def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   set[int]:

						"""simple docstring"""



						if number_to_partition < 0:
												return set()
						elif number_to_partition == 0:
												return {1}

						lowerCAmelCase__    						=						set()
						lowerCAmelCase__    						=						42
						lowerCAmelCase__    						=						42

						for prime in primes:
												if prime > number_to_partition:
																		continue
												for sub in partition(number_to_partition - prime		):
																		ret.add(sub * prime		)

						return ret







def 	_UpperCamelCase   (    UpperCamelCase_       :					int = 5000		)  ->   int | None:

						"""simple docstring"""



						for number_to_partition in range(1    ,       UpperCamelCase_		):
												if len(partition(UpperCamelCase_		)		) > number_unique_partitions:
																		return number_to_partition
						return None


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





