


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase     :						str	=     {
    'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
    'feature_extraction_wav2vec2': ['Wav2Vec2FeatureExtractor'],
    'processing_wav2vec2': ['Wav2Vec2Processor'],
    'tokenization_wav2vec2': ['Wav2Vec2CTCTokenizer', 'Wav2Vec2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase     :						List[Any]	=     [
						    'WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Wav2Vec2ForAudioFrameClassification',
						    'Wav2Vec2ForCTC',
						    'Wav2Vec2ForMaskedLM',
						    'Wav2Vec2ForPreTraining',
						    'Wav2Vec2ForSequenceClassification',
						    'Wav2Vec2ForXVector',
						    'Wav2Vec2Model',
						    'Wav2Vec2PreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase     :						Any	=     [
						    'TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFWav2Vec2ForCTC',
						    'TFWav2Vec2Model',
						    'TFWav2Vec2PreTrainedModel',
						    'TFWav2Vec2ForSequenceClassification',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase     :						int	=     [
						    'FlaxWav2Vec2ForCTC',
						    'FlaxWav2Vec2ForPreTraining',
						    'FlaxWav2Vec2Model',
						    'FlaxWav2Vec2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
						from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
						from .processing_wavaveca import WavaVecaProcessor
						from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavaveca import (
												    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavaVecaForAudioFrameClassification,
												    WavaVecaForCTC,
												    WavaVecaForMaskedLM,
												    WavaVecaForPreTraining,
												    WavaVecaForSequenceClassification,
												    WavaVecaForXVector,
												    WavaVecaModel,
												    WavaVecaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFWavaVecaForCTC,
												    TFWavaVecaForSequenceClassification,
												    TFWavaVecaModel,
												    TFWavaVecaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    FlaxWavaVecaForCTC,
												    FlaxWavaVecaForPreTraining,
												    FlaxWavaVecaModel,
												    FlaxWavaVecaPreTrainedModel,
												)


else:
						import sys

						__lowercase     :						Optional[Any]	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''



import os






def   lowerCamelCase							():
					with open(os.path.dirname(_SCREAMING_SNAKE_CASE       ) + '/p022_names.txt'       ) as file:
										__a      :	List[Any]    =  str(file.readlines()[0]       )
										__a      :	str    =  names.replace('"'						,				''       ).split(','       )

					names.sort()

					__a      :	Union[str, Any]    =  0
					__a      :	Tuple    =  0

					for i, name in enumerate(_SCREAMING_SNAKE_CASE       ):
										for letter in name:
															name_score += ord(_SCREAMING_SNAKE_CASE       ) - 64

										total_score += (i + 1) * name_score
										__a      :	Any    =  0
					return total_score


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



