



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_snake_case	=						{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case	=						["SpeechT5Tokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case	=						[
						    "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SpeechT5ForSpeechToText",
						    "SpeechT5ForSpeechToSpeech",
						    "SpeechT5ForTextToSpeech",
						    "SpeechT5Model",
						    "SpeechT5PreTrainedModel",
						    "SpeechT5HifiGan",
						]

if TYPE_CHECKING:
						from .configuration_speechta import (
						    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
						    SpeechTaConfig,
						    SpeechTaHifiGanConfig,
						)
						from .feature_extraction_speechta import SpeechTaFeatureExtractor
						from .processing_speechta import SpeechTaProcessor

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_speechta import SpeechTaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speechta import (
												    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechTaForSpeechToSpeech,
												    SpeechTaForSpeechToText,
												    SpeechTaForTextToSpeech,
												    SpeechTaHifiGan,
												    SpeechTaModel,
												    SpeechTaPreTrainedModel,
												)

else:
						import sys

						_snake_case	=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





from __future__ import annotations






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if not nums:
														raise ValueError("List is empty"						)
							return sum(_lowerCamelCase						) / len(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
