from typing import TYPE_CHECKING

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


__lowerCamelCase      :			List[Any]							       =		{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			int							       =		['''XLMRobertaTokenizer''']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			str							       =		['''XLMRobertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''XLMRobertaForCausalLM''',
       '''XLMRobertaForMaskedLM''',
       '''XLMRobertaForMultipleChoice''',
       '''XLMRobertaForQuestionAnswering''',
       '''XLMRobertaForSequenceClassification''',
       '''XLMRobertaForTokenClassification''',
       '''XLMRobertaModel''',
       '''XLMRobertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFXLMRobertaForCausalLM''',
       '''TFXLMRobertaForMaskedLM''',
       '''TFXLMRobertaForMultipleChoice''',
       '''TFXLMRobertaForQuestionAnswering''',
       '''TFXLMRobertaForSequenceClassification''',
       '''TFXLMRobertaForTokenClassification''',
       '''TFXLMRobertaModel''',
       '''TFXLMRobertaPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			str							       =		[
       '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxXLMRobertaForMaskedLM''',
       '''FlaxXLMRobertaForCausalLM''',
       '''FlaxXLMRobertaForMultipleChoice''',
       '''FlaxXLMRobertaForQuestionAnswering''',
       '''FlaxXLMRobertaForSequenceClassification''',
       '''FlaxXLMRobertaForTokenClassification''',
       '''FlaxXLMRobertaModel''',
       '''FlaxXLMRobertaPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_xlm_roberta import (
       XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
       XLMRobertaConfig,
       XLMRobertaOnnxConfig,
   )

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_roberta import XLMRobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMRobertaForCausalLM,
          XLMRobertaForMaskedLM,
          XLMRobertaForMultipleChoice,
          XLMRobertaForQuestionAnswering,
          XLMRobertaForSequenceClassification,
          XLMRobertaForTokenClassification,
          XLMRobertaModel,
          XLMRobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm_roberta import (
          TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMRobertaForCausalLM,
          TFXLMRobertaForMaskedLM,
          TFXLMRobertaForMultipleChoice,
          TFXLMRobertaForQuestionAnswering,
          TFXLMRobertaForSequenceClassification,
          TFXLMRobertaForTokenClassification,
          TFXLMRobertaModel,
          TFXLMRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xlm_roberta import (
          FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxXLMRobertaForCausalLM,
          FlaxXLMRobertaForMaskedLM,
          FlaxXLMRobertaForMultipleChoice,
          FlaxXLMRobertaForQuestionAnswering,
          FlaxXLMRobertaForSequenceClassification,
          FlaxXLMRobertaForTokenClassification,
          FlaxXLMRobertaModel,
          FlaxXLMRobertaPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			Any							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      if collection[k] < collection[least]:
        SCREAMING_SNAKE_CASE__      							=				k
    if least != i:
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				(collection[i], collection[least])
  return collection


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