






from typing import TYPE_CHECKING

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


snake_case__   :						Optional[Any]													=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Any													=   [
     """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RobertaPreLayerNormForCausalLM""",
     """RobertaPreLayerNormForMaskedLM""",
     """RobertaPreLayerNormForMultipleChoice""",
     """RobertaPreLayerNormForQuestionAnswering""",
     """RobertaPreLayerNormForSequenceClassification""",
     """RobertaPreLayerNormForTokenClassification""",
     """RobertaPreLayerNormModel""",
     """RobertaPreLayerNormPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Union[str, Any]													=   [
     """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRobertaPreLayerNormForCausalLM""",
     """TFRobertaPreLayerNormForMaskedLM""",
     """TFRobertaPreLayerNormForMultipleChoice""",
     """TFRobertaPreLayerNormForQuestionAnswering""",
     """TFRobertaPreLayerNormForSequenceClassification""",
     """TFRobertaPreLayerNormForTokenClassification""",
     """TFRobertaPreLayerNormMainLayer""",
     """TFRobertaPreLayerNormModel""",
     """TFRobertaPreLayerNormPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Optional[Any]													=   [
     """FlaxRobertaPreLayerNormForCausalLM""",
     """FlaxRobertaPreLayerNormForMaskedLM""",
     """FlaxRobertaPreLayerNormForMultipleChoice""",
     """FlaxRobertaPreLayerNormForQuestionAnswering""",
     """FlaxRobertaPreLayerNormForSequenceClassification""",
     """FlaxRobertaPreLayerNormForTokenClassification""",
     """FlaxRobertaPreLayerNormModel""",
     """FlaxRobertaPreLayerNormPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_roberta_prelayernorm import (
     ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
     RobertaPreLayerNormConfig,
     RobertaPreLayerNormOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaPreLayerNormForCausalLM,
      RobertaPreLayerNormForMaskedLM,
      RobertaPreLayerNormForMultipleChoice,
      RobertaPreLayerNormForQuestionAnswering,
      RobertaPreLayerNormForSequenceClassification,
      RobertaPreLayerNormForTokenClassification,
      RobertaPreLayerNormModel,
      RobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta_prelayernorm import (
      TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaPreLayerNormForCausalLM,
      TFRobertaPreLayerNormForMaskedLM,
      TFRobertaPreLayerNormForMultipleChoice,
      TFRobertaPreLayerNormForQuestionAnswering,
      TFRobertaPreLayerNormForSequenceClassification,
      TFRobertaPreLayerNormForTokenClassification,
      TFRobertaPreLayerNormMainLayer,
      TFRobertaPreLayerNormModel,
      TFRobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
      FlaxRobertaPreLayerNormPreTrainedModel,
  )

else:
 import sys

 snake_case__   :						Union[str, Any]													=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













import requests






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  {'Content-Type': 'application/json'}
   UpperCamelCase_						=  requests.post(__lowercase     ,   json={'text': message_body}     ,   headers=__lowercase)
   if response.status_code != 200:
      UpperCamelCase_						=  (
          'Request to slack returned an error '
          f"""{response.status_code}, the response is:\n{response.text}"""
      )
      raise ValueError(__lowercase)


if __name__ == "__main__":
 # Set the slack url to the one provided by Slack when you create the webhook at
 # https://my.slack.com/services/new/incoming-webhook/
 send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")





