



'''simple docstring'''

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,
)


_A				:    List[Any]     						=logging.get_logger(__name__)

_A				:    int     						=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'''),
    ]
)

_A				:    List[Any]     						=_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 						__UpperCamelCase				(							_lowercase					) ->			Optional[Any]:
	for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
		if class_name in extractors:
			_lowercase					:       int       =		model_type_to_module_name(_lowercase					)

			_lowercase					:       List[Any]       =		importlib.import_module(f'''.{module_name}''',				'transformers.models'					)
			try:
				return getattr(_lowercase,				_lowercase					)
			except AttributeError:
				continue

	for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
		if getattr(_lowercase,				'__name__',				_lowercase					) == 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.
	_lowercase					:       str       =		importlib.import_module('transformers'					)
	if hasattr(_lowercase,				_lowercase					):
		return getattr(_lowercase,				_lowercase					)

	return None







def 						__UpperCamelCase				(							_lowercase,				_lowercase = None,				_lowercase = False,				_lowercase = False,				_lowercase = None,				_lowercase = None,				_lowercase = None,				_lowercase = False,				**_lowercase,				) ->			str:
	_lowercase					:       List[Any]       =		get_file_from_repo(
	    _lowercase,				_lowercase,				cache_dir=_lowercase,				force_download=_lowercase,				resume_download=_lowercase,				proxies=_lowercase,				use_auth_token=_lowercase,				revision=_lowercase,				local_files_only=_lowercase,				)
	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(_lowercase,				encoding='utf-8'					) as reader:
		return json.load(_lowercase					)




class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	Dict					)  ->       List[Any]:



						'''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				:	Optional[Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  **UpperCamelCase_				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		kwargs.pop('config'     ,  UpperCamelCase_					)
						_lowercase					:       Optional[int]       =		kwargs.pop('trust_remote_code'     ,  UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		True

						_lowercase					:       Any       =		FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_     ,  **UpperCamelCase_					)
						_lowercase					:       List[str]       =		config_dict.get('feature_extractor_type'     ,  UpperCamelCase_					)
						_lowercase					:       Dict       =		None
						if "AutoFeatureExtractor" in config_dict.get('auto_map'     ,  {}					):
							_lowercase					:       Tuple       =		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_					):
								_lowercase					:       Any       =		AutoConfig.from_pretrained(UpperCamelCase_     ,  **UpperCamelCase_					)
							# It could be in `config.feature_extractor_type``
							_lowercase					:       Union[str, Any]       =		getattr(UpperCamelCase_     ,  'feature_extractor_type'     ,  UpperCamelCase_					)
							if hasattr(UpperCamelCase_     ,  'auto_map'					) and "AutoFeatureExtractor" in config.auto_map:
								_lowercase					:       Optional[Any]       =		config.auto_map['AutoFeatureExtractor']

						if feature_extractor_class is not None:
							_lowercase					:       Any       =		feature_extractor_class_from_name(UpperCamelCase_					)

						_lowercase					:       str       =		feature_extractor_auto_map is not None
						_lowercase					:       str       =		feature_extractor_class is not None or type(UpperCamelCase_					) in FEATURE_EXTRACTOR_MAPPING
						_lowercase					:       List[Any]       =		resolve_trust_remote_code(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)

						if has_remote_code and trust_remote_code:
							_lowercase					:       str       =		get_class_from_dynamic_module(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  **UpperCamelCase_					)
							_lowercase					:       int       =		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:
							_lowercase					:       List[str]       =		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_				:	Optional[Any]     ,  UpperCamelCase_				:	Tuple					)  ->       Dict:



						'''simple docstring'''





						FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase_     ,  UpperCamelCase_					)





'''simple docstring'''

import unittest

from knapsack import greedy_knapsack as kp



class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		[10, 20, 30, 40, 50, 60]
						_lowercase					:       Tuple       =		[2, 4, 6, 8, 10, 12]
						_lowercase					:       Optional[Any]       =		100
						self.assertEqual(kp.calc_profit(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)     ,  210					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'max_weight must greater than zero.'					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'Weight can not be negative.'					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'Profit can not be negative.'					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[str]:



						'''simple docstring'''





						self.assertRaisesRegex(UpperCamelCase_     ,  'max_weight must greater than zero.'					)




					def 						__UpperCAmelCase     (  self				:	int					)  ->       List[Any]:



						'''simple docstring'''





						self.assertRaisesRegex(
						    UpperCamelCase_     ,  'The length of profit and weight must be same.'					)


if __name__ == "__main__":
		unittest.main()
