





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_		:
					lowerCAmelCase_							=	42
					lowerCAmelCase_							=	None
					# Automatically constructed
					lowerCAmelCase_							=	"dict"
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	field(default='''Translation'''							,  init=_lowerCamelCase							,  repr=_lowerCamelCase					)




					def __call__(		self  )		->							Union[str, Any]:
										return pa.struct({lang: pa.string() for lang in sorted(self.languages  )}  )






					def 	lowerCAmelCase			(		self  )		->							Union["FeatureType", Dict[str, "FeatureType"]]:
										from .features import Value

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





@dataclass
class 			UpperCamelCase_		:
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	None
					# Automatically constructed
					lowerCAmelCase_							=	"dict"
					lowerCAmelCase_							=	None
					lowerCAmelCase_							=	field(default='''TranslationVariableLanguages'''							,  init=_lowerCamelCase							,  repr=_lowerCamelCase					)




					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				sorted(set(self.languages  )  ) if self.languages else None
										_snake_case									=				len(self.languages  ) if self.languages else None




					def __call__(		self  )		->							int:
										return pa.struct({'language': pa.list_(pa.string()  ), 'translation': pa.list_(pa.string()  )}  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Dict:
										_snake_case									=				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.
										_snake_case									=				[]
										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.
										_snake_case							,     _snake_case									=				zip(*sorted(lowerCAmelCase_  )  )

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






					def 	lowerCAmelCase			(		self  )		->							Union["FeatureType", Dict[str, "FeatureType"]]:
										from .features import Sequence, Value

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_      							=     {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase_      							=     [
					    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LukeForEntityClassification""",
					    """LukeForEntityPairClassification""",
					    """LukeForEntitySpanClassification""",
					    """LukeForMultipleChoice""",
					    """LukeForQuestionAnswering""",
					    """LukeForSequenceClassification""",
					    """LukeForTokenClassification""",
					    """LukeForMaskedLM""",
					    """LukeModel""",
					    """LukePreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
					from .tokenization_luke import LukeTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_luke import (
										    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LukeForEntityClassification,
										    LukeForEntityPairClassification,
										    LukeForEntitySpanClassification,
										    LukeForMaskedLM,
										    LukeForMultipleChoice,
										    LukeForQuestionAnswering,
										    LukeForSequenceClassification,
										    LukeForTokenClassification,
										    LukeModel,
										    LukePreTrainedModel,
										)

else:
					import sys

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



