





from typing import TYPE_CHECKING

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


_lowerCAmelCase							:      Tuple						=	{'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:      Union[str, Any]						=	['''T5Tokenizer''']

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:      Tuple						=	['''T5TokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:      Any						=	[
     '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''T5EncoderModel''',
     '''T5ForConditionalGeneration''',
     '''T5Model''',
     '''T5PreTrainedModel''',
     '''load_tf_weights_in_t5''',
     '''T5ForQuestionAnswering''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:      str						=	[
     '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFT5EncoderModel''',
     '''TFT5ForConditionalGeneration''',
     '''TFT5Model''',
     '''TFT5PreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:      int						=	[
     '''FlaxT5EncoderModel''',
     '''FlaxT5ForConditionalGeneration''',
     '''FlaxT5Model''',
     '''FlaxT5PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

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






def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int = 1000	) ->						str:
   if (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"	)
   elif (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) 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
   A_					:				List[str]          =			(
       4 * _subsum(_lowerCAmelCase				,				1				,				_lowerCAmelCase	)
       - 2 * _subsum(_lowerCAmelCase				,				4				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				5				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				6				,				_lowerCAmelCase	)
   )

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

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int	) ->						float:
   A_					:				int          =			0.0
   for sum_index in range(digit_pos_to_extract + precision	):
      A_					:				Optional[int]          =			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
         A_					:				Optional[Any]          =			pow(
             16				,				digit_pos_to_extract - 1 - sum_index				,				_lowerCAmelCase	)
      else:
         A_					:				Tuple          =			pow(16				,				digit_pos_to_extract - 1 - sum_index	)
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
 import doctest

 doctest.testmod()
