


'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase      :      Union[str, Any]            =					{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowercase      :      Dict            =					[
     """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:
 _lowercase      :      List[str]            =					[
     """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:
 _lowercase      :      Dict            =					[
     """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

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









'''simple docstring'''



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="Translation"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def __call__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)





				def 					a__(		self      :			int				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Value

										return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				Optional[List]  		=	None
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="TranslationVariableLanguages"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     sorted(set(self.languages				)				) if self.languages else None
										UpperCAmelCase					    =     len(self.languages				) if self.languages else None




				def __call__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     set(self.languages				)
										if self.languages and set(lowerCAmelCase				) - lang_set:
																raise ValueError(
																    F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase				)})."""				)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										UpperCAmelCase					    =     []
										for lang, text in translation_dict.items():
																if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						translation_tuples.append((lang, text)				)
																else:
																						translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
										UpperCAmelCase    ,    UpperCAmelCase					    =     zip(*sorted(lowerCAmelCase				)				)

										return {"language": languages, "translation": translations}





				def 					a__(		self      :			Any				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Sequence, Value

										return {
										    "language": Sequence(Value('''string'''				)				),
										    "translation": Sequence(Value('''string'''				)				),
										}





