






'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase__						:str          =			{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:Optional[int]          =			[
       """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:
   UpperCAmelCase__						:Optional[int]          =			[
       """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:
   UpperCAmelCase__						:str          =			[
       """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

   UpperCAmelCase__						:List[Any]          =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCAmelCase_  )
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     str   		=			field(default='automatic-speech-recognition'    ,   metadata={'include_in_asdict_even_if_is_default': True}  )
       snake_case__    :     ClassVar[Features]   		=			Features({'audio': Audio()}  )
       snake_case__    :     ClassVar[Features]   		=			Features({'transcription': Value('string'  )}  )
       snake_case__    :     str   		=			"audio"
       snake_case__    :     str   		=			"transcription"




       def 					a_					(					self      : Any	,    A__      : Any					):


            """simple docstring"""







            if self.audio_column not in features:
                 raise ValueError(f"Column {self.audio_column} is not present in features."					)
            if not isinstance(features[self.audio_column]	,    A__					):
                 raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
            __lowerCamelCase						:       Dict				   =				copy.deepcopy(self					)
            __lowerCamelCase						:       List[Any]				   =				self.input_schema.copy()
            __lowerCamelCase						:       Optional[int]				   =				features[self.audio_column]
            __lowerCamelCase						:       Any				   =				input_schema
            return task_template




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            return {self.audio_column: "audio", self.transcription_column: "transcription"}



