
"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCamelCase__        =		{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase__        =		["""LlamaTokenizer"""]

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase__        =		[
           """LlamaForCausalLM""",
           """LlamaModel""",
           """LlamaPreTrainedModel""",
           """LlamaForSequenceClassification""",
       ]


if TYPE_CHECKING:
       from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_llama import LlamaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_llama_fast import LlamaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
       import sys

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

"""simple docstring"""







def    __lowerCAmelCase    (_UpperCamelCase		):
	if len(_UpperCamelCase		) <= 1:
		return lst

	__lowerCAmelCase      :						str						      =				1

	while i < len(_UpperCamelCase		):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				__lowerCAmelCase      :						int						      =				1

	return lst


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