




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE      :				Dict				      ={
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				int				      =["""CamembertTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				List[str]				      =["""CamembertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				int				      =[
          """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """CamembertForCausalLM""",
          """CamembertForMaskedLM""",
          """CamembertForMultipleChoice""",
          """CamembertForQuestionAnswering""",
          """CamembertForSequenceClassification""",
          """CamembertForTokenClassification""",
          """CamembertModel""",
          """CamembertPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Tuple				      =[
          """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFCamembertForCausalLM""",
          """TFCamembertForMaskedLM""",
          """TFCamembertForMultipleChoice""",
          """TFCamembertForQuestionAnswering""",
          """TFCamembertForSequenceClassification""",
          """TFCamembertForTokenClassification""",
          """TFCamembertModel""",
          """TFCamembertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_camembert import CamembertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_camembert_fast import CamembertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_camembert import (
                CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                CamembertForCausalLM,
                CamembertForMaskedLM,
                CamembertForMultipleChoice,
                CamembertForQuestionAnswering,
                CamembertForSequenceClassification,
                CamembertForTokenClassification,
                CamembertModel,
                CamembertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_camembert import (
                TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFCamembertForCausalLM,
                TFCamembertForMaskedLM,
                TFCamembertForMultipleChoice,
                TFCamembertForQuestionAnswering,
                TFCamembertForSequenceClassification,
                TFCamembertForTokenClassification,
                TFCamembertModel,
                TFCamembertPreTrainedModel,
            )

else:
      import sys

      __SCREAMING_SNAKE_CASE      :				Tuple				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





def      lowerCAmelCase_     (					lowerCamelCase:    Optional[int]  )   ->     Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			[7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowerCamelCase       ,			len(lowerCamelCase  )  ):
                  lowerCamelCase__ 					=			collection[i]
                  lowerCamelCase__ 					=			i
                  while j >= gap and collection[j - gap] > insert_value:
                        lowerCamelCase__ 					=			collection[j - gap]
                        j -= gap
                  if j != i:
                        lowerCamelCase__ 					=			insert_value
      return collection


if __name__ == "__main__":
      from doctest import testmod

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