






"""simple docstring"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		Any		,				_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		List[Any] )      ->				None:
  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    lowerCamelCase_         =						array[indexa], array[indexa]







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any] )      ->				None:
  if length > 1:
    lowerCamelCase_         =						int(length / 2 )
    for i in range(__UpperCAmelCase		,				low + middle ):
      comp_and_swap(__UpperCAmelCase		,				__UpperCAmelCase		,				i + middle		,				__UpperCAmelCase )
    bitonic_merge(__UpperCAmelCase		,				__UpperCAmelCase		,				__UpperCAmelCase		,				__UpperCAmelCase )
    bitonic_merge(__UpperCAmelCase		,				low + middle		,				__UpperCAmelCase		,				__UpperCAmelCase )







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		int )      ->				None:
  if length > 1:
    lowerCamelCase_         =						int(length / 2 )
    bitonic_sort(__UpperCAmelCase		,				__UpperCAmelCase		,				__UpperCAmelCase		,				1 )
    bitonic_sort(__UpperCAmelCase		,				low + middle		,				__UpperCAmelCase		,				0 )
    bitonic_merge(__UpperCAmelCase		,				__UpperCAmelCase		,				__UpperCAmelCase		,				__UpperCAmelCase )


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				str					    =						input('''Enter numbers separated by a comma:\n''').strip()
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						[int(item.strip()) for item in user_input.split(''',''')]

  bitonic_sort(unsorted, 0, len(unsorted), 1)
  print('''\nSorted array in ascending order is: ''', end='''''')
  print(*unsorted, sep=''', ''')

  bitonic_merge(unsorted, 0, len(unsorted), 0)
  print('''Sorted array in descending order is: ''', end='''''')
  print(*unsorted, sep=''', ''')





"""simple docstring"""






from typing import TYPE_CHECKING

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


__A			   = {
    "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:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "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

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