"""simple docstring"""




from typing import TYPE_CHECKING

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


UpperCAmelCase       :    List[str]									=			{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase       :    Tuple									=			[
					    "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "HubertForCTC",
					    "HubertForSequenceClassification",
					    "HubertModel",
					    "HubertPreTrainedModel",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase       :    List[str]									=			[
					    "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFHubertForCTC",
					    "TFHubertModel",
					    "TFHubertPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase       :    str									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""





def     _A    (    UpperCamelCase_	:			int,     UpperCamelCase_	:			int = 1000)      ->		str:
   '''simple docstring'''


   if (not isinstance(UpperCamelCase_,     UpperCamelCase_)) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer")
   elif (not isinstance(UpperCamelCase_,     UpperCamelCase_)) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer")

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   __lowercase								=		(
       4 * _subsum(UpperCamelCase_,     1,     UpperCamelCase_)
       - 2 * _subsum(UpperCamelCase_,     4,     UpperCamelCase_)
       - _subsum(UpperCamelCase_,     5,     UpperCamelCase_)
       - _subsum(UpperCamelCase_,     6,     UpperCamelCase_)
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16))[2:]




def     _A    (    UpperCamelCase_	:			int,     UpperCamelCase_	:			int,     UpperCamelCase_	:			int)      ->		float:
   '''simple docstring'''


   __lowercase								=		0.0
   for sum_index in range(digit_pos_to_extract + precision):
      __lowercase								=		8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         __lowercase								=		pow(
             16,     digit_pos_to_extract - 1 - sum_index,     UpperCamelCase_)
      else:
         __lowercase								=		pow(16,     digit_pos_to_extract - 1 - sum_index)
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
   import doctest

   doctest.testmod()




