


def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list							):
     if not isinstance(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							):
          raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
     if len(SCREAMING_SNAKE_CASE__							) == 0:
          raise ValueError('Input list must be a non empty list'							)
     if len(SCREAMING_SNAKE_CASE__							) == 1:
          return True
     try:
          __UpperCamelCase       				=series[1] / series[0]
          for index in range(len(SCREAMING_SNAKE_CASE__							) - 1							):
               if series[index + 1] / series[index] != common_ratio:
                    return False
     except ZeroDivisionError:
          return False
     return True

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list							):
     if not isinstance(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							):
          raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
     if len(SCREAMING_SNAKE_CASE__							) == 0:
          raise ValueError('Input list must be a non empty list'							)
     __UpperCamelCase       				=1
     for value in series:
          answer *= value
     return pow(SCREAMING_SNAKE_CASE__     ,	1 / len(SCREAMING_SNAKE_CASE__							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_A									=		{
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A									=		[
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flaubert import (
          FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertPreTrainedModel,
          FlaubertWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_flaubert import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertPreTrainedModel,
          TFFlaubertWithLMHeadModel,
      )

else:
   import sys

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





