





'''simple docstring'''


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE									=				{
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				["WhisperTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WhisperForConditionalGeneration",
           "WhisperModel",
           "WhisperPreTrainedModel",
           "WhisperForAudioClassification",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWhisperForConditionalGeneration",
           "TFWhisperModel",
           "TFWhisperPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "FlaxWhisperForConditionalGeneration",
           "FlaxWhisperModel",
           "FlaxWhisperPreTrainedModel",
           "FlaxWhisperForAudioClassification",
       ]


if TYPE_CHECKING:
       from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
       from .feature_extraction_whisper import WhisperFeatureExtractor
       from .processing_whisper import WhisperProcessor
       from .tokenization_whisper import WhisperTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_whisper_fast import WhisperTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_whisper import (
                  WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WhisperForAudioClassification,
                  WhisperForConditionalGeneration,
                  WhisperModel,
                  WhisperPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_whisper import (
                  TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWhisperForConditionalGeneration,
                  TFWhisperModel,
                  TFWhisperPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_whisper import (
                  FlaxWhisperForAudioClassification,
                  FlaxWhisperForConditionalGeneration,
                  FlaxWhisperModel,
                  FlaxWhisperPreTrainedModel,
              )

else:
       import sys

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









'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       list     ):



    '''simple docstring'''





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


if __name__ == "__main__":
       import doctest

       doctest.testmod()


