
'''simple docstring'''

from typing import TYPE_CHECKING

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


__snake_case:  str								=			{
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Optional[Any]								=			["Speech2TextTokenizer"]

try:
    if not is_speech_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Union[str, Any]								=			["Speech2TextFeatureExtractor"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Union[str, Any]								=			[
        "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFSpeech2TextForConditionalGeneration",
        "TFSpeech2TextModel",
        "TFSpeech2TextPreTrainedModel",
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  str								=			[
        "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Speech2TextForConditionalGeneration",
        "Speech2TextModel",
        "Speech2TextPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
    from .processing_speech_to_text import SpeechaTextProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speech_to_text import SpeechaTextTokenizer

    try:
        if not is_speech_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_speech_to_text import (
            TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFSpeechaTextForConditionalGeneration,
            TFSpeechaTextModel,
            TFSpeechaTextPreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_to_text import (
            SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechaTextForConditionalGeneration,
            SpeechaTextModel,
            SpeechaTextPreTrainedModel,
        )

else:
    import sys

    __snake_case:  Dict								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




'''simple docstring'''

from math import factorial

__snake_case:  List[Any]								=			{str(d): factorial(d) for d in range(10)}






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  return sum(DIGIT_FACTORIAL[d] for d in str(A_   )   )






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							7 * factorial(9   ) + 1
  return sum(i for i in range(3     ,		A_   ) if sum_of_digit_factorial(A_   ) == i   )


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


