def __lowerCAmelCase			(							UpperCamelCase__    )     ->   list:


    if any(not isinstance(_lowerCamelCase    ,  _lowerCamelCase    ) or x < 0 for x in sequence    ):
        raise TypeError('''Sequence must be list of non-negative integers'''    )
    for _ in range(len(_lowerCamelCase    )    ):
        for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase    ,  sequence[1:]    )    ):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
  assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]

'''simple docstring'''




from typing import TYPE_CHECKING

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


__UpperCAmelCase												={
    "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:
  __UpperCAmelCase												=["WhisperTokenizerFast"]

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

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

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase												=[
      "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

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