


def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Any = 10_00     )->      str:




      '''simple docstring'''
      if (not isinstance(__snake_case	,			__snake_case     )) or (digit_position <= 0):
            raise ValueError('Digit position must be a positive integer'     )
      elif (not isinstance(__snake_case	,			__snake_case     )) 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
      __magic_name__ 		=			(
          4 * _subsum(__snake_case	,			1	,			__snake_case     )
          - 2 * _subsum(__snake_case	,			4	,			__snake_case     )
          - _subsum(__snake_case	,			5	,			__snake_case     )
          - _subsum(__snake_case	,			6	,			__snake_case     )
      )

      # return the first hex digit of the fractional part of the result
      return hex(int((sum_result % 1) * 16     )     )[2:]
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						List[Any]	,			__snake_case			:						List[Any]     )->      float:




      '''simple docstring'''
      __magic_name__ 		=			0.0
      for sum_index in range(digit_pos_to_extract + precision     ):
            __magic_name__ 		=			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
                  __magic_name__ 		=			pow(
                      16	,			digit_pos_to_extract - 1 - sum_index	,			__snake_case     )
            else:
                  __magic_name__ 		=			pow(16	,			digit_pos_to_extract - 1 - sum_index     )
            total += exponential_term / denominator
      return total


if __name__ == "__main__":
     import doctest

     doctest.testmod()





from typing import TYPE_CHECKING

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


UpperCamelCase :				str						    =       {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Optional[int]						    =       ['''BartTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Any						    =       [
					    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''BartForCausalLM''',
					    '''BartForConditionalGeneration''',
					    '''BartForQuestionAnswering''',
					    '''BartForSequenceClassification''',
					    '''BartModel''',
					    '''BartPretrainedModel''',
					    '''PretrainedBartModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Tuple						    =       [
					    '''TFBartForConditionalGeneration''',
					    '''TFBartForSequenceClassification''',
					    '''TFBartModel''',
					    '''TFBartPretrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				List[Any]						    =       [
					    '''FlaxBartDecoderPreTrainedModel''',
					    '''FlaxBartForCausalLM''',
					    '''FlaxBartForConditionalGeneration''',
					    '''FlaxBartForQuestionAnswering''',
					    '''FlaxBartForSequenceClassification''',
					    '''FlaxBartModel''',
					    '''FlaxBartPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
					from .tokenization_bart import BartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bart_fast import BartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bart import (
										    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BartForCausalLM,
										    BartForConditionalGeneration,
										    BartForQuestionAnswering,
										    BartForSequenceClassification,
										    BartModel,
										    BartPretrainedModel,
										    PretrainedBartModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bart import (
										    TFBartForConditionalGeneration,
										    TFBartForSequenceClassification,
										    TFBartModel,
										    TFBartPretrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bart import (
										    FlaxBartDecoderPreTrainedModel,
										    FlaxBartForCausalLM,
										    FlaxBartForConditionalGeneration,
										    FlaxBartForQuestionAnswering,
										    FlaxBartForSequenceClassification,
										    FlaxBartModel,
										    FlaxBartPreTrainedModel,
										)

else:
					import sys

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

