





from typing import TYPE_CHECKING

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


_A :   int  			=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A :   str  			=  ['LlamaTokenizer']

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A :   Optional[Any]  			=  [
							    '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

							_A :   Optional[int]  			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""

   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise TypeError('''only integers accepted as input'''					)
   else:
      lowerCamelCase__       :						Any        =							str(abs(UpperCAmelCase					)					)
      lowerCamelCase__       :						Union[str, Any]        =							[list(UpperCAmelCase					) for char in range(len(UpperCAmelCase					)					)]
      for index in range(len(UpperCAmelCase					)					):
         num_transpositions[index].pop(UpperCAmelCase					)
      return max(
          int(''''''.join(list(UpperCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
       __import__('doctest').testmod()



