



from typing import TYPE_CHECKING

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


__lowerCamelCase         =   {
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =   [
       '''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:
   __lowerCamelCase         =   [
       '''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

   __lowerCamelCase         =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		len(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		[[0] * n for i in range(__snake_case    )]
      for i in range(__snake_case    ):
            UpperCAmelCase_  :	Optional[Any]				      =		y_points[i]

      for i in range(2					,  __snake_case    ):
            for j in range(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	int				      =		(
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
