




'''simple docstring'''





from typing import TYPE_CHECKING

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


a		:  int   			=			{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  Union[str, Any]   			=			[
      '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GPTJForCausalLM''',
      '''GPTJForQuestionAnswering''',
      '''GPTJForSequenceClassification''',
      '''GPTJModel''',
      '''GPTJPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  Union[str, Any]   			=			[
      '''TFGPTJForCausalLM''',
      '''TFGPTJForQuestionAnswering''',
      '''TFGPTJForSequenceClassification''',
      '''TFGPTJModel''',
      '''TFGPTJPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''FlaxGPTJForCausalLM''',
      '''FlaxGPTJModel''',
      '''FlaxGPTJPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptj import (
        GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTJForCausalLM,
        GPTJForQuestionAnswering,
        GPTJForSequenceClassification,
        GPTJModel,
        GPTJPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gptj import (
        TFGPTJForCausalLM,
        TFGPTJForQuestionAnswering,
        TFGPTJForSequenceClassification,
        TFGPTJModel,
        TFGPTJPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
  import sys

  a		:  Any   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








'''simple docstring'''





from math import exp





def UpperCAmelCase						(					UpperCAmelCase_	):
    return 8 * x - 2 * exp(-x	)





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_	):
    _a							:       Optional[int]       					=			lower_bound
    _a							:       str       					=			upper_bound
    for _ in range(0      ,  UpperCAmelCase_	):
        _a					,   _a							:       List[str]       					=			xa, xa - (f(UpperCAmelCase_	) * (xa - xa)) / (f(UpperCAmelCase_	) - f(UpperCAmelCase_	))
    return xa


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


