






from typing import TYPE_CHECKING

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


__snake_case							 =  {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							 =  ["""OpenAIGPTTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							 =  [
     """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """OpenAIGPTDoubleHeadsModel""",
     """OpenAIGPTForSequenceClassification""",
     """OpenAIGPTLMHeadModel""",
     """OpenAIGPTModel""",
     """OpenAIGPTPreTrainedModel""",
     """load_tf_weights_in_openai_gpt""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case							 =  [
     """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFOpenAIGPTDoubleHeadsModel""",
     """TFOpenAIGPTForSequenceClassification""",
     """TFOpenAIGPTLMHeadModel""",
     """TFOpenAIGPTMainLayer""",
     """TFOpenAIGPTModel""",
     """TFOpenAIGPTPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
 from .tokenization_openai import OpenAIGPTTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_openai_fast import OpenAIGPTTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_openai import (
      OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OpenAIGPTDoubleHeadsModel,
      OpenAIGPTForSequenceClassification,
      OpenAIGPTLMHeadModel,
      OpenAIGPTModel,
      OpenAIGPTPreTrainedModel,
      load_tf_weights_in_openai_gpt,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTMainLayer,
      TFOpenAIGPTModel,
      TFOpenAIGPTPreTrainedModel,
  )

else:
 import sys

 __snake_case							 =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    UpperCamelCase	:Tuple     							=       float()
    try:
        UpperCamelCase	:Union[str, Any]     							=       mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __snake_case							 =  1_2.5
 __snake_case							 =  10
 __snake_case							 =  newtons_second_law_of_motion(mass, acceleration)
 print("""The force is """, force, """N""")





