

"""simple docstring"""


def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     list:

		if len(__SCREAMING_SNAKE_CASE			) < 2:
				return collection

		def circle_sort_util(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			) -> bool:
				__lowerCAmelCase:	List[Any]   				=      False

				if low == high:
						return swapped

				__lowerCAmelCase:	int   				=      low
				__lowerCAmelCase:	List[str]   				=      high

				while left < right:
						if collection[left] > collection[right]:
								__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      (
								    collection[right],
								    collection[left],
								)
								__lowerCAmelCase:	Any   				=      True

						left += 1
						right -= 1

				if left == right and collection[left] > collection[right + 1]:
						__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      (
						    collection[right + 1],
						    collection[left],
						)

						__lowerCAmelCase:	Union[str, Any]   				=      True

				__lowerCAmelCase:	Optional[int]   				=      low + int((high - low) / 2			)
				__lowerCAmelCase:	str   				=      circle_sort_util(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	List[Any]   				=      circle_sort_util(__SCREAMING_SNAKE_CASE ,			mid + 1 ,			__SCREAMING_SNAKE_CASE			)

				return swapped or left_swap or right_swap

		__lowerCAmelCase:	Optional[int]   				=      True

		while is_not_sorted is True:
				__lowerCAmelCase:	Union[str, Any]   				=      circle_sort_util(__SCREAMING_SNAKE_CASE ,			0 ,			len(__SCREAMING_SNAKE_CASE			) - 1			)

		return collection


if __name__ == "__main__":
			__A						       =      input("Enter numbers separated by a comma:\n").strip()
			__A						       =      [int(item) for item in user_input.split(",")]
			print(circle_sort(unsorted))





"""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__)


