



"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE  :						List[Any]    							=      {
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __SCREAMING_SNAKE_CASE  :						Tuple    							=      [
       '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GPTNeoForCausalLM''',
       '''GPTNeoForQuestionAnswering''',
       '''GPTNeoForSequenceClassification''',
       '''GPTNeoForTokenClassification''',
       '''GPTNeoModel''',
       '''GPTNeoPreTrainedModel''',
       '''load_tf_weights_in_gpt_neo''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __SCREAMING_SNAKE_CASE  :						Any    							=      [
       '''FlaxGPTNeoForCausalLM''',
       '''FlaxGPTNeoModel''',
       '''FlaxGPTNeoPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_gpt_neo import (
          GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
          GPTNeoForCausalLM,
          GPTNeoForQuestionAnswering,
          GPTNeoForSequenceClassification,
          GPTNeoForTokenClassification,
          GPTNeoModel,
          GPTNeoPreTrainedModel,
          load_tf_weights_in_gpt_neo,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
   import sys

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





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return 1 / (1 + np.exp(-vector							))



def        lowerCAmelCase_(   lowercase_     :							np.array							)	->       np.array:
				return vector * sigmoid(1.7_0_2 * vector							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
