






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,
)


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = 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'),
    ]
)

lowerCAmelCase_           = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def      snake_case		(							UpperCAmelCase	:       str   ):
				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												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 _, 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.
				A	 =      importlib.import_module('transformers'   )
				if hasattr(UpperCAmelCase,						UpperCAmelCase   ):
								return getattr(UpperCAmelCase,						UpperCAmelCase   )

				return None







def      snake_case		(							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	:       List[str],						):
				A	 =      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      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       List[Any]		)    ->   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(_SCREAMING_SNAKE_CASE		)
		def       A(  cls		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      kwargs.pop('config'						,_SCREAMING_SNAKE_CASE		)
						A	 =      kwargs.pop('trust_remote_code'						,_SCREAMING_SNAKE_CASE		)
						A	 =      True

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

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

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

						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       A(  _SCREAMING_SNAKE_CASE		:       Dict						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Any:


						'''simple docstring'''
						FEATURE_EXTRACTOR_MAPPING.register(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)













def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      ''
				for i in table:
								res += inp[i - 1]
				return res







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				return data[1:] + data[0]







def      snake_case		(							UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Dict   ):
				A	 =      ''
				for i in range(len(UpperCAmelCase   )   ):
								if a[i] == b[i]:
												res += "0"
								else:
												res += "1"
				return res







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Optional[Any]   ):
				A	 =      int('0b' + data[0] + data[-1],						2   )
				A	 =      int('0b' + data[1:3],						2   )
				return bin(s[row][col]   )[2:]







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Any,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[int]   ):
				A	 =      message[:4]
				A	 =      message[4:]
				A	 =      apply_table(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      xor(UpperCAmelCase,						UpperCAmelCase   )
				A	 =      apply_sbox(UpperCAmelCase,						temp[:4]   )  # noqa: E741
				A	 =      apply_sbox(UpperCAmelCase,						temp[4:]   )
				A	 =      '0' * (2 - len(UpperCAmelCase   )) + l  # noqa: E741
				A	 =      '0' * (2 - len(UpperCAmelCase   )) + r
				A	 =      apply_table(l + r,						UpperCAmelCase   )
				A	 =      xor(UpperCAmelCase,						UpperCAmelCase   )
				return temp + right


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter 10 bit key: ')
					lowerCAmelCase_           = input('Enter 8 bit message: ')

					lowerCAmelCase_           = [6, 3, 7, 4, 8, 5, 10, 9]
					lowerCAmelCase_           = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
					lowerCAmelCase_           = [2, 4, 3, 1]
					lowerCAmelCase_           = [2, 6, 3, 1, 4, 8, 5, 7]
					lowerCAmelCase_           = [4, 1, 3, 5, 7, 2, 8, 6]
					lowerCAmelCase_           = [4, 1, 2, 3, 2, 3, 4, 1]
					lowerCAmelCase_           = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
					lowerCAmelCase_           = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

					# key generation
					lowerCAmelCase_           = apply_table(key, paa_table)
					lowerCAmelCase_           = temp[:5]
					lowerCAmelCase_           = temp[5:]
					lowerCAmelCase_           = left_shift(left)
					lowerCAmelCase_           = left_shift(right)
					lowerCAmelCase_           = apply_table(left + right, pa_table)
					lowerCAmelCase_           = left_shift(left)
					lowerCAmelCase_           = left_shift(right)
					lowerCAmelCase_           = left_shift(left)
					lowerCAmelCase_           = left_shift(right)
					lowerCAmelCase_           = apply_table(left + right, pa_table)

					# encryption
					lowerCAmelCase_           = apply_table(message, IP)
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = temp[4:] + temp[:4]
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = apply_table(temp, IP_inv)
					print('Cipher text is:', CT)

					# decryption
					lowerCAmelCase_           = apply_table(CT, IP)
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = temp[4:] + temp[:4]
					lowerCAmelCase_           = function(expansion, sa, sa, keya, temp)
					lowerCAmelCase_           = apply_table(temp, IP_inv)
					print('Plain text after decypting is:', PT)





