


'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowercase:		int    						=  {
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		Any    						=  ["BartTokenizerFast"]

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

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[Any]    						=  [
           "TFBartForConditionalGeneration",
           "TFBartForSequenceClassification",
           "TFBartModel",
           "TFBartPretrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		Optional[int]    						=  [
           "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

       __lowercase:		Tuple    						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



