


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




	def __call__(   self							):

						'''simple docstring'''





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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						from .features import Value

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





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  sorted(set(self.languages							)							) if self.languages else None
						__a			:       int					    =  len(self.languages							) if self.languages else None




	def __call__(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





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

						# 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(__a						,				__a							):
																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    ,				__a			:       str					    =  zip(*sorted(__a							)							)

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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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_tf_available, is_torch_available


__lowercase	:						str       =	{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						str       =	[
	    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecAudioForAudioFrameClassification',
	    'Data2VecAudioForCTC',
	    'Data2VecAudioForSequenceClassification',
	    'Data2VecAudioForXVector',
	    'Data2VecAudioModel',
	    'Data2VecAudioPreTrainedModel',
	]
	__lowercase	:						Tuple       =	[
	    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecTextForCausalLM',
	    'Data2VecTextForMaskedLM',
	    'Data2VecTextForMultipleChoice',
	    'Data2VecTextForQuestionAnswering',
	    'Data2VecTextForSequenceClassification',
	    'Data2VecTextForTokenClassification',
	    'Data2VecTextModel',
	    'Data2VecTextPreTrainedModel',
	]
	__lowercase	:						Dict       =	[
	    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Data2VecVisionForImageClassification',
	    'Data2VecVisionForMaskedImageModeling',
	    'Data2VecVisionForSemanticSegmentation',
	    'Data2VecVisionModel',
	    'Data2VecVisionPreTrainedModel',
	]

if is_tf_available():
	__lowercase	:						Optional[Any]       =	[
	    'TFData2VecVisionForImageClassification',
	    'TFData2VecVisionForSemanticSegmentation',
	    'TFData2VecVisionModel',
	    'TFData2VecVisionPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
	from .configuration_dataavec_text import (
	    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecTextConfig,
	    DataaVecTextOnnxConfig,
	)
	from .configuration_dataavec_vision import (
	    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DataaVecVisionConfig,
	    DataaVecVisionOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dataavec_audio import (
		    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecAudioForAudioFrameClassification,
		    DataaVecAudioForCTC,
		    DataaVecAudioForSequenceClassification,
		    DataaVecAudioForXVector,
		    DataaVecAudioModel,
		    DataaVecAudioPreTrainedModel,
		)
		from .modeling_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecTextForCausalLM,
		    DataaVecTextForMaskedLM,
		    DataaVecTextForMultipleChoice,
		    DataaVecTextForQuestionAnswering,
		    DataaVecTextForSequenceClassification,
		    DataaVecTextForTokenClassification,
		    DataaVecTextModel,
		    DataaVecTextPreTrainedModel,
		)
		from .modeling_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForMaskedImageModeling,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		    DataaVecVisionPreTrainedModel,
		)
	if is_tf_available():
		from .modeling_tf_dataavec_vision import (
		    TFDataaVecVisionForImageClassification,
		    TFDataaVecVisionForSemanticSegmentation,
		    TFDataaVecVisionModel,
		    TFDataaVecVisionPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						int       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



