

"""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      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				List[str]
	_A       :				Optional[str]    =				None
	# Automatically constructed
	_A       :				ClassVar[str]    =				"dict"
	_A       :				ClassVar[Any]    =				None
	_A       :				str    =				field(default="""Translation"""       ,       init=_SCREAMING_SNAKE_CASE       ,       repr=_SCREAMING_SNAKE_CASE						)





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





	def    lowerCamelCase(self							):
		from .features import Value

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






@dataclass
class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				Optional[List]    =				None
	_A       :				Optional[int]    =				None
	_A       :				Optional[str]    =				None
	# Automatically constructed
	_A       :				ClassVar[str]    =				"dict"
	_A       :				ClassVar[Any]    =				None
	_A       :				str    =				field(default="""TranslationVariableLanguages"""       ,       init=_SCREAMING_SNAKE_CASE       ,       repr=_SCREAMING_SNAKE_CASE						)





	def    lowerCamelCase(self							):
		A_		:						str	    =   sorted(set(self.languages							)							) if self.languages else None
		A_		:						List[Any]	    =   len(self.languages							) if self.languages else None





	def __call__(self							):
		return pa.struct({"""language""": pa.list_(pa.string()							), """translation""": pa.list_(pa.string()							)}							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   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.
		A_		:						Optional[int]	    =   []
		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.
		A_		:						List[Any]	    =   zip(*sorted(lowerCAmelCase_							)							)

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





	def    lowerCamelCase(self							):
		from .features import Sequence, Value

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









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase         =							[
							    "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

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

