'''simple docstring'''




from string import ascii_uppercase

_a   :	List[str] 							=   {str(ord(c) - 55): c for c in ascii_uppercase}

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       str:





       """simple docstring"""


       if isinstance(lowerCamelCase__							,    lowerCamelCase__						):
              raise TypeError("int() can't convert non-string with explicit base"						)
       if num < 0:
              raise ValueError("parameter must be positive int"						)
       if isinstance(lowerCamelCase__							,    lowerCamelCase__						):
              raise TypeError("'str' object cannot be interpreted as an integer"						)
       if isinstance(lowerCamelCase__							,    lowerCamelCase__						):
              raise TypeError("'float' object cannot be interpreted as an integer"						)
       if base in (0, 1):
              raise ValueError("base must be >= 2"						)
       if base > 36:
              raise ValueError("base must be <= 36"						)
       __UpperCAmelCase  :				Union[str, Any]       				=		""
       __UpperCAmelCase  :				List[str]       				=		0
       __UpperCAmelCase  :				Any       				=		0
       while div != 1:
              __UpperCAmelCase  :				Union[str, Any]       				=		divmod(lowerCamelCase__							,    lowerCamelCase__						)
              if base >= 11 and 9 < mod < 36:
                     __UpperCAmelCase  :				Optional[Any]       				=		ALPHABET_VALUES[str(lowerCamelCase__						)]
              else:
                     __UpperCAmelCase  :				Union[str, Any]       				=		str(lowerCamelCase__						)
              new_value += actual_value
              __UpperCAmelCase  :				Union[str, Any]       				=		num // base
              __UpperCAmelCase  :				Union[str, Any]       				=		div
              if div == 0:
                     return str(new_value[::-1]						)
              elif div == 1:
                     new_value += str(lowerCamelCase__						)
                     return str(new_value[::-1]						)

       return new_value[::-1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   for base in range(2, 37):
      for num in range(1000):
         assert int(decimal_to_any(num, base), base) == num, (
             num,
             base,
             decimal_to_any(num, base),
             int(decimal_to_any(num, base), base),
         )







'''simple docstring'''




from typing import TYPE_CHECKING

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


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

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






