


def    lowercase_	(	_A    :					Dict		):

      """simple docstring"""




      if collection == []:
            return []

      # get some information about the collection
      lowerCamelCase__	:       int					=						len(_A		)
      lowerCamelCase__	:       Dict					=						max(_A		)
      lowerCamelCase__	:       str					=						min(_A		)

      # create the counting array
      lowerCamelCase__	:       Optional[int]					=						coll_max + 1 - coll_min
      lowerCamelCase__	:       Tuple					=						[0] * counting_arr_length

      # count how much a number appears in the collection
      for number in collection:
            counting_arr[number - coll_min] += 1

      # sum each position with it's predecessors. now, counting_arr[i] tells
      # us how many elements <= i has in the collection
      for i in range(1					,      _A		):
            lowerCamelCase__	:       Dict					=						counting_arr[i] + counting_arr[i - 1]

      # create the output collection
      lowerCamelCase__	:       Union[str, Any]					=						[0] * coll_len

      # place the elements in the output, respecting the original order (stable
      # sort) from end to begin, updating counting_arr
      for i in reversed(range(0					,      _A		)		):
            lowerCamelCase__	:       int					=						collection[i]
            counting_arr[collection[i] - coll_min] -= 1

      return ordered

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      return "".join([chr(_A		) for i in counting_sort([ord(_A		) for c in string]		)]		)


if __name__ == "__main__":
    # Test string sort
    assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

    A   :					Dict	=      input("Enter numbers separated by a comma:\n").strip()
    A   :					Any	=      [int(item) for item in user_input.split(",")]
    print(counting_sort(unsorted))






from typing import TYPE_CHECKING

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


A   :					Optional[int]	=      {
    "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   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "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   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


