






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A        =			{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				pass

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A        =			[
				    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'RoCBertForCausalLM',
				    'RoCBertForMaskedLM',
				    'RoCBertForMultipleChoice',
				    'RoCBertForPreTraining',
				    'RoCBertForQuestionAnswering',
				    'RoCBertForSequenceClassification',
				    'RoCBertForTokenClassification',
				    'RoCBertLayer',
				    'RoCBertModel',
				    'RoCBertPreTrainedModel',
				    'load_tf_weights_in_roc_bert',
				]

if TYPE_CHECKING:
				from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
				from .tokenization_roc_bert import RoCBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								raise OptionalDependencyNotAvailable()

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roc_bert import (
								    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RoCBertForCausalLM,
								    RoCBertForMaskedLM,
								    RoCBertForMultipleChoice,
								    RoCBertForPreTraining,
								    RoCBertForQuestionAnswering,
								    RoCBertForSequenceClassification,
								    RoCBertForTokenClassification,
								    RoCBertLayer,
								    RoCBertModel,
								    RoCBertPreTrainedModel,
								    load_tf_weights_in_roc_bert,
								)


else:
				import sys

				A        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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 							__a							:





		'''simple docstring'''



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


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






		def   __snake_case			(						self       ):
						from .features import Value

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






@dataclass
class 							__a							:





		'''simple docstring'''



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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   sorted(set(self.languages       )       ) if self.languages else None
						SCREAMING_SNAKE_CASE_   :	Dict													=   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   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   set(self.languages       )
						if self.languages and set(UpperCamelCase__       ) - lang_set:
										raise ValueError(
										    F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(UpperCamelCase__       )}).'''       )

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

        # Ensure translations are in ascending order by language code.
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   zip(*sorted(UpperCamelCase__       )       )

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






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

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